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In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. We show that, when a Linear Independence Constraint Qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality, meaning that the closed-loop cost evaluated over the entire task converges asymptotically to the optimal cost of the infinite-horizon control problem. Compared to previous works this sufficient LICQ condition can be easily checked, it holds for a larger class of systems and it can be used to adaptively select the prediction horizon of the controller, as demonstrated by a numerical example., Comment: technical note

This paper presents the model predictive control (MPC) design using the backward Euler method for an 11-level switched-battery boost multilevel inverter (SBBMLI). The SBBMLI was proposed as the cost-effective solution for the communication power line in a high-speed rail (HSR) system. Initially, a generalized SBBMLI problem formulation was performed, and an open-loop simulation based on voltage and current mode controls was conducted. The finite control set-model predictive control (FCS-MPC) ability to track the reference sinusoidal output with low total harmonic distortion (THD) was then assessed as a performance criterion. Furthermore, the performance was assessed based on no-load and load disturbances. Finally, the results proved the ability of the proposed FCSMPC to address non-linear dynamics, constraints, and its efficiency of implementation.

The Model Predict Control (MPC) based Intelligent Advance Metering (IAM) is a core maneuver of future smart grids (SG). SG is the advanced generation of electric power and utility system that improve operation technology (OT) and information technology (IT) to provide nonstop, self-recovery, self-configuration, low-cost, and security-based electricity to the consumer in real-time. Smart metering (SM) allows SG to connect the electric, gas, and oil utilities through sensors. Power plants, consumers, and utility companies will be received real-time wireless control IAM with fifth generation (5G) network technology. The aim of 5G network technology is to enable power grid digitalization (PGD) and facilitate the (IOT) Internet of Things for the future advance SG with benefits such as high-rate public safety, low latency, ultra-high speed, large number of connectivity, and reliability. In this paper, we analyze future predictions about energy needs by using MPC, fast self-recovery system, self-configuration, and upgradation, better performance of service provider, faster power connecting after an outage, control electric theft, minimize electric leakage, a large number of wireless connecting of IAM home-based, and real-time monitoring via human machine interface (HMI) and for customer end IAM operation over 5G networks to reduce billing price, reduce meter cost, lower outage cost, and as well as personalized control over electricity consumption and future challenge in this area.

Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.

In dieser Arbeit wird ein Automatisierungskonzept für den optimalen Betrieb von Fernwärmenetzen untersucht. Ziel ist sowohl der wirtschaftlich optimale Betrieb als auch die Einhaltung aller thermo-hydraulischen Restriktionen der im Rahmen der Energiewende komplexer werdenden Fernwärmeversorgung. Zunächst wird der theoretische Hintergrund von Fernwärmenetzen und deren Modellierung sowie Optimierung und Regelung dargelegt. Basierend darauf wird ein Automatisierungskonzept vorgeschlagen, welches den Betrieb bestehender Fernwärmenetze mit wirtschaftlicher Optimierung um eine Ebene mit Netzsimulation und Modellprädiktiver Regelung erweitert. Die Entwicklung einer geeigneten Netzsimulation ist Gegenstand der Untersuchungen. Physikalische Modelle von vier Fernwärmenetzen werden erstellt und durch Ansätze aus den Bereichen des maschinellen Lernens und der Systemidentifikation vereinfacht. Dies führt zu reduzierten Rechenzeiten und einer guten Simulationsgenauigkeit verglichen mit vollständigen physikalischen Modellierungen. Anschließend wird die Optimierungskomponente einer Modellprädiktiven Regelung mit den vereinfachten Modellen entwickelt. Leistungen und Vorlauftemperaturen aller Wärmeerzeugungsanlagen sollen möglichst nah am wirtschaftlichen Optimum eingestellt werden, während alle thermo-hydraulischen Restriktionen wie minimale oder maximale Drücke und Temperaturen in den Netzsimulationen eingehalten werden. Die Untersuchungen zeigen, dass einfache Netze in Sekunden und komplexere Netze in etwa einer Minute optimiert werden können. Dabei werden hauptsächlich stationäre Simulationen verwendet, welche dynamische Effekte im Netz nicht berücksichtigen. Die Anwendbarkeit der stationär optimierten Fahrweisen wird deshalb durch instationäre Simulationen überprüft. Ebenfalls wird grundlegend eine Optimierung unter Verwendung von instationären Simulationen getestet. Abschließend werden die Limitierungen des vorgestellten Konzepts sowie der erreichte Entwicklungsstand diskutiert.

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A novel neural network (NN)-based parallel model predictive control (PMPC) method is proposed to deal with the tracking problem of the quadrotor unmanned aerial vehicles (Q-UAVs) system in this article. It is well known that the dynamics of Q-UAVs are changeable while the system is operating in some specific environments. In this case, traditional NN-based MPC methods are not applicable because their model networks are pre-trained and kept constant throughout the process. To solve this problem, we propose the PMPC algorithm, which introduces parallel control structure and experience pool replay technology into the MPC method. In this algorithm, an NN-based artificial system runs in parallel with the UAV system to reconstruct its dynamics model. Furthermore, the experience replay technology is used to maintain the accuracy of the reconstructed model, so as to ensure the effectiveness of the model prediction algorithm. Furthermore, a convergence proof of the artificial system is also given in this paper. Finally, numerical results and analysis are given to demonstrate the effectiveness of the PMPC algorithm.

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

Due to the fact that classical computers cannot efficiently obtain random numbers, it is common practice to design cryptosystems in terms of real random numbers and then replace them with (cryptographically secure) pseudorandom ones for concrete implementations. However, as pointed out by [10], this technique may lead to compromise of security in secure multiparty computation (MPC) protocols. Although this work suggests using information-theoretically secure protocols and pseudorandom generators (PRGs) with high min-entropy to alleviate the problem, yet it is preferable to base the security on computational assumptions rather than the stronger information-theoretic ones. By observing that the contrived constructions in the aforementioned work use MPC protocols and PRGs that are closely related to each other, we notice that it may help to alleviate the problem by using protocols and PRGs that are ”unrelated” to each other. In this paper, we propose a notion called”computational irrelevancy” to formalise the term ”unrelated” and under this condition provide a security guarantee under computational assumptions.

Heating accounts for about a quarter of the building’s energy consumption. In order to increase the value of green buildings, it is necessary to do some research on reducing heating energy consumption. The use of household heat metering in buildings can reduce the energy required for heating. In view of the regulation and control problems in energy consumption reduction and thermal comfort improvement, an energy efficient thermal comfort model predictive control (EETCMPC) method was proposed in the household thermal metering building. Firstly, the dynamic temperature change model of the heating room was analyzed and modeled. Then, the structure of EETCMPC system for household thermal metering room was presented, and the thermal comfort index based on Predicted Mean Vote (PMV) was introduced into the cost function, and the EETCMPC was designed. Finally, the performance of EETCMPC was analyzed and verified by means of simulation with Proportion Integration Differentiation (PID) and temperature reference tracking MPC (TRTMPC). Energy consumption of EETCMPC was reduced by approximately 4.4% compared with TRTMPC when the thermal comfort demand was met. Therefore, this method is valid and reliable for energy efficient thermal comfort control for household heat metering room and is significant for engineering application.

In this paper, an adaptive intelligent controller is developed for the velocity-tracking problem of a nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and measurement noises. The whole control system is consisting of two subsystems, where each subsystem has its own control responsibility. In this way, first, a kinematic controller is implemented according to the kinematic model of the robot, and then a dynamic controller is designed based on the characteristic of the robot dynamics. Our focus is designing and developing an adaptive fractional-order fuzzy logic proportional–integral–derivative (FOFPID) controller for the trajectory-tracking task in a two-WMR. Unlike the prevalent works which only designed the scaling factors of FOFPID, a simultaneous optimization of fuzzy membership functions and controller coefficients are realized to improve the efficiency of the WMR dynamic controller. Accordingly, the controller parameters are optimally adjusted by employing a combination of the sin cos algorithm and harmony search, called SCA-HS. To validate the applicability of the suggested framework, experimental studies are also conducted on a real-time platform using a two-WMR prototype. The experimental results confirm the effectiveness of the proposed controller for the exact trajectory-tracking problem in the presence of disturbances and noises.

The subjects of this study were five psychological support experts with less than five years of clinical experience. After setting out frameworks of simulated individual psychological interview, the author provided collage image creation experience by therapist (the author or subject) and client (subject). In the next stage, the subjects were asked to complete “self-assessment checklist for collage therapy experience” and share the findings in the interview. The author qualitatively investigated the subjects' psychological processes and awareness-raising effect through the training experience. Consequently, the experience variously evoked their feelings and made them aware the position as a therapist. Implementing the self-assessment checklist also suggested that application of collage therapy to clients with severe psychopathology required special considerations and the subjects' intuition might rise to the surface of their consciousness.

The mitochondrial pyruvate carrier (Mpc) plays an indispensable role in the transport of pyruvates across the mitochondrial inner membrane. Despite the two distinct homologous proteins, Mpc1 and Mpc2, were identified in 2012, there are still controversies on the basic functional units and oligomeric state of Mpc complexes. In this study, yeast Mpc1 and Mpc2 proteins were expressed in a prokaryotic heterologous system. Both homo- and hetero-dimers were successfully reconstituted in mixed detergents. Interactions among Mpc monomers were recorded utilizing paramagnetic relaxation enhancement (PRE) nuclear magnetic resonance (NMR) methods. By single-channel patch-clamp assays, we discovered that both the Mpc1–Mpc2 hetero-dimer and Mpc1 homo-dimer are able to transport K+ ions. Furthermore, the Mpc1–Mpc2 hetero-dimer demonstrated the ability to transport pyruvates, at a rate significantly higher than that of the Mpc1 homo-dimer, indicating that it could be the basic functional unit of Mpc complexes. Our findings provide valuable insights for further structural determination and the study of the transport mechanism of Mpc complexes.

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these systems improve quality and productivity, but on the other hand, they require a huge amount of energy. Energy saving solutions have to be developed and applied to provide sustainable production. The purpose of this research is to develop the optimal control strategy for industrial robots in order to minimize energy consumption. Therefore, a case study was conducted for the development of two control strategies to be applied to the RV-2AJ Mitsubishi robot arm with 5 DOF, where the system is a nonlinear one. The first examined controller is the classical linear proportional integral derivative (PID) controller, while the second one is the linear model predictive control (MPC) controller. In our study, the performances of both the classical PID model and the linear MPC controller were compared. As a result, it was found that the MPC controller in the execution of the three defined reference trajectories [(1) curve motion, (2) N-shaped motion, and (3) circle motion] was always faster and required less energy consumption, whereas in terms of precision the PID succeeded in executing the trajectory more precisely than the MPC but with higher energy consumption. The main contribution of the research is that the performances of the two control strategies with regard to a complex dynamic system were compared in the case of the execution of three different trajectories. The evaluations show that the MPC controller is, on the one hand, more energy efficient; on the other hand, it provides a shorter cycle time compared to the PID controller.

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts.

Pseudomonas aeruginosa is the most common causative agent associated with microbial keratitis. During contact lens wear, pathogens may be introduced into the ocular environment, which might cause adverse events. Lehfilcon A is a recently developed contact lens with a water gradient surface composed of polymeric 2-methacryloyloxyethyl phosphorylcholine (MPC). MPC is re-ported to impart anti-biofouling properties onto modified substrates. Therefore, in this in vitro experimental study, we tested the capability of lehfilcon A to resist adhesion by P. aeruginosa. Quantitative bacterial adhesion assays using five strains of P. aeruginosa were conducted to compare the adherence properties of lehfilcon A to five currently marketed silicone hydrogel (SiHy) contact lenses (comfilcon A, fanfilcon A, senofilcon A, senofilcon C, and samfilcon A). Compared to lehfilcon A, we observed 26.7 ± 8.8 times (p = 0.0028) more P. aeruginosa binding to comfilcon A, 30.0 ± 10.8 times (p = 0.0038) more binding to fanfilcon A, 18.2 ± 6.2 times (p = 0.0034) more binding to senofilcon A, 13.6 ± 3.9 times (p = 0.0019) more binding to senofilcon C, and 29.5 ± 11.8 times (p = 0.0057) more binding to samfilcon A. These results demonstrate that, for various strains of P. aeruginosa, lehfilcon A reduces bacterial adhesion compared to other contact lens materials.

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid–liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified.

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

Autonomous vehicle's technology is expected to be disruptive for automotive industry in next years. This paper proposes a novel real-time trajectory planner based on a Nonlinear Model Predictive Control (NMPC) algorithm. A nonlinear single track vehicle model with Pacejka's lateral tyre formulas has been implemented. The numerical solution of the NMPC problem is obtained by means of the implementation of a novel genetic algorithm strategy. Numerical results are discussed through simulations that shown a reasonable behavior of the proposed strategy in presence of static or moving obstacles as well as in a wide rage of road friction conditions. Moreover a real-time implementation is made possible by the reported computational time analysis.

The Ros River is polluted with heavy metals, petroleumproducts and organic matter due to the activities of variousindustries, including agriculture (washing away pesticides and fertilizers), food, light and petrochemical industries, etc.Intake of drinking water in Bila Tserkva district is carried outfrom the river Ros, so the aim of the work was to systematizethe results of research over the past 10 years and identify thedynamics of changes in the ecological state of the river.Object of research: Ros River within Bila Tserkva district of Kyiv region.Subject of research: negative environmental factors,water quality indicators. The study of the state of the question was carried out by methods of analysis and synthesis.Water quality assessment was conducted in the Ros Riveron a monthly basis. Initial data (monthly results) are averaged by seasons: spring (March-May, 2010-2020), summer(June-August, 2010-2020), autumn (September-November,2010-2020), winter (December of the previous year - January, February of the current year, 2010/2011-2020/2021).Among the studied indicators there are ammonium salt, nitrates, nitrites, chemical oxygen demand (COD), biochemicaloxygen demand (BOD5), phosphates, total iron, manganese.Seasonal concentrations throughout the study period for ammonium salt ranged from 0.11 to 2.17 mg/dm3, nitrites – from0.01 to 0.82 mg/dm3, nitrates did not exceed 40.0 mg/dm3,COD– in the range from 15.9 to 61.1 mg/dm3, BOD5– in therange from 1.5 to 8.3 mg/dm3, phosphates – from 0.05 to 0.49mg/dm3. Excess concentration of total iron and manganesewere observed in 59% and 29% of cases, respectively. Thatis, there is a tendency to deterioration water quality by somecomponents; the main reason for this is anthropogenic activity, namely the inconsistency of water treatment equipmentof economic entities with modern environmental standardsand the presence of unidentified sources of discharges intothe Ros River basin. Key words: Rosriver, hydrochemical indicators, ecological condition, seasonal values, MPC.

U ovome radu obrađeno je na podacima bazirano modelsko prediktivno upravljanje (MPC) temeljeno na Hankel matricama. Rad obuhvaća strukturu osnovnog MPC-a te strukturu MPC-a baziranog na podacima za linearne i nelinearne sustave. Rad demonstrira primjenu takvog upravljanja kroz simulacije na linearnom i nelinearnom sustavu. Objašnjen je utjecaj MPC parametara na kvalitetu rada regulatora te dodatno demonstrirana primjena ove strukture regulatora na stvarnoj postavi istosmjernog motora. This thesis presents a study of Data-driven Model Predictive Control (MPC) based on Hankel matrices. The thesis covers the fundamental theory of MPC, followed by the explanation of data-driven MPC for linear and nonlinear systems. The study demonstrates the application of data-driven MPC through simulations on linear and nonlinear systems and its impact on the controller's performance based on varying MPC parameters. The effectiveness of the approach is further demonstrated through its implementation on a physical DC motor.

In this thesis, we develop machine learning systems that are able to leverage the knowledge of equations of motion (scene-specific or scene-agnostic) to perform object discovery, physical parameter estimation, position and velocity estimation, camera pose estimation, and learn structured latent spaces that satisfy physical dynamics rules. These systems are unsupervised, learning from unlabelled videos, and use as inductive biases the general equations of motion followed by objects of interest in the scene. This is an important task as in many complex real world environments ground-truth states are not available, although there is physical knowledge of the underlying system. Our goals with this approach, i.e. integration of physics knowledge with unsupervised learning models, are to improve vision-based prediction, enable new forms of control, increase data-efficiency and provide model interpretability, all of which are key areas of interest in machine learning. With the above goals in mind, we start by asking the following question: given a scene in which the objects’ motions are known up to some physical parameters (e.g. a ball bouncing off the floor with unknown restitution coefficient), how do we build a model that uses such knowledge to discover the objects in the scene and estimate these physical parameters? Our first model, PAIG (Physics-as-Inverse-Graphics), approaches this problem from a vision-as-inverse-graphics perspective, describing the visual scene as a composition of objects defined by their location and appearance, which are rendered onto the frame in a graphics manner. This is a known approach in the unsupervised learning literature, where the fundamental problem then becomes that of derendering, that is, inferring and discovering these locations and appearances for each object. In PAIG we introduce a key rendering component, the Coordinate-Consistent Decoder, which enables the integration of the known equations of motion with an inverse-graphics autoencoder architecture (trainable end-to-end), to perform simultaneous object discovery and physical parameter estimation. Although trained on simple simulated 2D scenes, we show that knowledge of the physical equations of motion of the objects in the scene can be used to greatly improve future prediction and provide physical scene interpretability. Our second model, V-SysId, tackles the limitations shown by the PAIG architecture, namely the training difficulty, the restriction to simulated 2D scenes, and the need for noiseless scenes without distractors. Here, we approach the problem from rst principles by asking the question: are neural networks a necessary component to solve this problem? Can we use simpler ideas from classical computer vision instead? With V- SysId, we approach the problem of object discovery and physical parameter estimation from a keypoint extraction, tracking and selection perspective, composed of 3 separate stages: proposal keypoint extraction and tracking, 3D equation tting and camera pose estimation from 2D trajectories, and entropy-based trajectory selection. Since all the stages use lightweight algorithms and optimisers, V-SysId is able to perform joint object discovery, physical parameter and camera pose estimation from even a single video, drastically improving data-efficiency. Additionally, due to the fact that it does not use a rendering/derendering approach, it can be used in real 3D scenes with many distractor objects. We show that this approach enables a number of interest applications, such as vision-based robot end-effector localisation and remote breath rate measurement. Finally, we move into the area of structured recurrent variational models from vision, where we are motivated by the following observation: in existing models, applying a force in the direction from a start point and an end point (in latent space), does not result in a movement from the start point towards the end point, even on the simplest unconstrained environments. This means that the latent space learned by these models does not follow Newton’s law, where the acceleration vector has the same direction as the force vector (in point-mass systems), and prevents the use of PID controllers, which are the simplest and most well understood type of controller. We solve this problem by building inductive biases from Newtonian physics into the latent variable model, which we call NewtonianVAE. Crucially, Newtonian correctness in the latent space brings about the ability to perform proportional (or PID) control, as opposed to the more computationally expensive model predictive control (MPC). PID controllers are ubiquitous in industrial applications, but had thus far lacked integration with unsupervised vision models. We show that the NewtonianVAE learns physically correct latent spaces in simulated 2D and 3D control systems, which can be used to perform goal-based discovery and control in imitation learning, and path following via Dynamic Motion Primitives.

This study aimed to examine specific niches and usage for the aztreonam/amoxicillin/clavulanate combination and to use population pharmacokinetic simulations of clinical dosing regimens to predict the impact of this combination on restricting mutant selection. The in vitro susceptibility of 19 New-Delhi metallo-β-lactamase (NDM)-producing clinical isolates to amoxicillin/clavulanate and aztreonam alone and in co-administration was determined based on the minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC). The fractions of a 24-h duration that the free drug concentration was within the mutant selection window (fTMSW) and above the MPC (fT>MPC) in both plasma and epithelial lining fluid were determined from simulations of 10,000 subject profiles based on regimens by renal function categories. This combination reduced the MIC of aztreonam and amoxicillin/clavulanate to values below their clinical breakpoint in 7/9 K. pneumoniae and 8/9 E. coli, depending on the β-lactamase genes detected in the isolate. In the majority of the tested isolates, the combination resulted in fT>MPC > 90% and fTMSW < 10% for both aztreonam and amoxicillin/clavulanate. Clinical dosing regimens of aztreonam and amoxicillin/clavulanate were sufficient to provide mutant restriction coverage for MPC and MIC ≤ 4 mg/L. This combination has limited coverage against NDM- and extended-spectrum β-lactamase co-producing E. coli and K. pneumoniae and is not effective against isolates carrying plasmid-mediated AmpC and KPC-2. This study offers a potential scope and limitations as to where the aztreonam/amoxicillin/clavulanate combination may succeed or fail.

Die vorliegende Arbeit beschäftigt sich mit der modellprädiktiven Regelung (MPC = Model Predictive Control) der Temperaturen eines Schneckenextruders. Die Aufgabe eines Extruders ist es, aus dem eingefüllten Granulat eine Schmelze zu erzeugen und fördert diese für die Verarbeitung zur nächsten Maschine. Dafür wurde ein thermisches Modell herangezogen, welches mittels Finite-Volumina-Methode hergeleitet wurde. Mit Hilfe dieser Regelung wird nun die Temperatur des Extruderzylinders auf eine vorgegebene Temperatur gebracht, um ein qualitatives Produkt zu erhalten. Da bei einem MPC stets eine Optimierungsaufgabe im Hintergrund gelöst wird, wird zuerst die vorliegende Problemstellung analysiert und untersucht, um festzustellen, welches Optimierungsverfahren mit der dazugehörigen Gütefunktion und den Nebenbedingungen zum gewünschten Ziel führt. Danach wird eine passende Softwarebibliothek zur Lösung der entsprechenden Optimierungsaufgabe gefunden, damit eine simulative Analyse und eine Implementierung auf einer Hardware möglich wird. Nach der Analyse in einer Simulationsumgebung wird der Regler an einem Workstation-PC mit Echtzeit-Patch mit der Programmiersprache C++ realisiert und getestet. Abschließend wird anhand Extrusionsexperimente an dem industriellen Extruder der modellprädiktive Regler validiert und mit dem bereits implementierten Regler verglichen. This thesis deals with the model predictive control (MPC) of the temperatures of a screw extruder. The task of an extruder is to produce a melt from the filled granulate and conveys it to the next machine for processing. For this purpose, a thermal model was used, which was derived using the finite volume method. With the help of this control, the temperature of the extruder cylinder is now brought to a specified temperature in order to obtain a qualitative product. Since there is always an optimisation task being solved in the background of an MPC, the problem at hand is first analyzed and investigated to determine which optimisation method with the associated quality function and constraints leads to the desired goal. Afterwards, a suitable software library for solving the corresponding optimisation task is found so that a simulative analysis and an implementation on a hardware becomes possible. After the analysis in a simulation environment, the controller is implemented using the programming language C++ and tested on a workstation PC with real-time patch. Finally, the model predictive controller is validated by using extrusion experiments on the industrial extruder and compared with the already implemented controller. eingereicht von Samet Taskiran, BSc Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Masterarbeit Universität Linz 2023

A temperature control concept for a highly efficient and therefore well insulated extruder without active cooling is presented. The purpose of the control concept developed is to ensure a suitable production condition, which is defined by a constant melt temperature within a desired temperature range. The desired melt temperature is critical for the product quality and is specified by the operator. The control concept is model based. Two mathematical models are derived to describe the thermal dynamics of plastic production machines. The accuracy of the derived finite volume model is validated by a high-order finite element model. The developed control concept consists of several superimposed layers. The first layer includes heating tape temperature control which is implemented as a simple PI controller. The next layer consists of an observer (smart sensor) and a model predictive controller. The smart sensor is a disturbance observer that estimates the heat flow between the extruder cylinder and the processed material, including the thermal influence of the conveying screw. This configuration has the advantage of decoupling the thermal control problem from the specific granulate. A model predictive controller calculates appropriate temperature trajectories for the heating tape temperatures to achieve the desired temperature profiles within the extruder, while meeting any defined process or manufacturer related restrictions. A developed concept of set point adjustment during operating point changes enables fast operating point changes by determining optimal cylinder temperature trajectories. A material independent melt temperature controller forms the top layer of the developed concept. The melt temperature controller varies the desired cylinder temperatures to achieve the desired melt temperature. To prove that the concept is applicable to different extruder types, experiments on two different extruder types are discussed. The performance of the proposed control concept during preheating processes, extrusion startups, operating point changes and disturbance scenarios is analysed by simulations but mainly by experiments on industrial extruders. Es wird ein Temperaturregelungskonzept für einen hocheffizienten und damit gut isolierten Extruder ohne aktive Kühlung vorgestellt. Aufgabe des entwickelten Regelungskonzepts ist es, einen adäquaten Produktionszustand zu gewährleisten, der durch eine konstante Temperatur der Schmelze innerhalb eines gewünschten Temperaturbereichs definiert ist. Die Solltemperatur der Schmelze ist entscheidend für die Produktqualität und wird von der Bedienungsperson festgelegt. Es handelt sich um ein modellbasiertes Regelungskonzept. Es werden zwei mathematische Modelle, um die thermische Dynamik einer Kunststoffproduktionsmaschine zu beschreiben, hergeleitet. Das Finite Volumen Model wird durch ein hoch genaues Finite Elemente Modell validiert. Das entwickelte Regelungskonzept besteht aus mehreren überlagerten Schichten. Die erste Schichte umfasst die Heizbandtemperaturregelung, die als PI Regler implementiert ist. Die nächste Schicht besteht aus einem Beobachter (Smart Sensor) und einem modellprädiktiven Regler. Der Smart Sensor ist ein Störgrößenbeobachter, der den Wärmefluss zwischen dem Zylinder und dem verarbeiteten Material, einschließlich der thermischen Einwirkung der Förderschnecke, schätzt. Diese Konfiguration erlaubt eine materialunabhängige Regelungsstrategie. Ein modellprädiktiver Regler berechnet geeignete Temperaturvorgaben für die Heizbandtemperaturen, um die gewünschten Temperaturprofile im Extruder zu erreichen und gleichzeitig alle definierten prozess- oder herstellerspezifischen Beschränkungen zu erfüllen. Ein weiteres überlagertes Konzept ermöglicht schnelle Betriebspunktwechsel durch Ermittlung optimaler Zylindertemperaturverläufe. Ein materialunabhängiger Schmelzetemperaturregler bildet die oberste Schicht des entwickelten Konzepts. Die gewünschten Zylindertemperaturen werden variiert, um eine gewünschte Schmelzetemperatur zu erreichen. Um die Anwendbarkeit des Konzepts auf unterschiedliche Extrudertypen zu untermauern, werden Versuche auf zwei verschiedenen Extruderaufbauten durchgeführt und diskutiert. Die Performance des entwickelten Regelungskonzepts während Aufheizprozessen, Extrusionsstarts, Betriebspunktänderungen und Störungsszenarien wird durch Simulationen, vor allem aber durch Versuche an industriellen Extrudern analysiert. submitted by Kevin Schwarzinger Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Dissertation Universität Linz 2023

In this paper, a novel MPC controller for Wind turbine system (WTS) is proposed to control the turbine actuation system using intelligent fuzzy logic. The main limitation of wind turbine system is power regulation and frequent transient operation which defines the WTS as unstable among other renewable energy sources. The proposed controller defines the WTS to operate in nominal conditions with high improved performance during capture of wind energy system which maintains the load safe and operate-able to all condition. The controller proposed with the small actuator that defines with WTS will tackle losses associated during operation and rotation of WTS. The complete WTS with actuator model and fuzzy control based MPC controller designed in MATLAB/SIMULINK environment. The simulation outcomes suggest that a better transient response and high energy delivery obtained during the usage of proposed controller and further reduces ripples, further the proposed work tested with various operating ranges in domestic loads holds better results compare to conventional one.

With the increasing employment of robots in various industrial fields, many previously hazardous tasks have become safe and better controlled. However, there still remain a number of works that pose threats to human operators and that cannot yet be taken over by robots. Particular examples include working at height or in difficult to access or otherwise dangerous environments. Additionally, these environments can give rise to inefficiencies due to the requirements for further means, such as scaffolds, cranes, or transport by helicopter. We see aerial robots as a promising tool to assist human workers in these tasks in order to increase their safety and the viability of their work. Today, drones for both industry and end-consumers already exist in various types. Their hardware design, however, restricts them to operate in free flight, i.e., they are not designed for physical interaction with their environment. This is due to their underactuation, which does not allow to compensate for reaction forces that arise during an interaction task. Recently, more advanced aerial vehicles (Aerial Manipulators, AM) have been designed with the purpose of aerial interaction. Some of these robots are characterized by their higher degree of actuation (i.e., overactuation), which allows them to control both their pose as well as the interaction forces while being in physical contact with a structure. In this thesis, we aim to investigate different control approaches for an Overacuated Micro Aerial Vehicle (OMAV) in the pursuit of enabling manipulation of objects from the air. For that, we see the main challenges in finding optimal and safe control strategies for OMAVs, given their high degree of actuation, potential external disturbances, and fast dynamics. Further challenges lie in compensating for interaction forces and in reliable motion planning during complex manipulation tasks. We structure our investigations in a manner of increasingly complex control tasks, with the goal of developing a robot for complex aerial manipulation tasks. To this end, we first focus on the optimal control of an OMAV in free flight. Namely, we first present a PID and LQRI controller that enables an OMAV to accurately track pose trajectories while achieving secondary tasks by exploiting the platform’s overactuation. We also present a model-predictive optimal controller to execute the same task of pose tracking. In the second part we move towards the interaction with static objects. Specifically, we address push-and-slide tasks on a rigid surface with a rigid manipulator, which is a common problem for industrial inspection tasks. We show how the interaction dynamics can be modeled in an analytical way and how certain conditions can be used to distinguish between different interaction modes. Furthermore, we employ a force-torque sensor to track reference forces during the interaction. Lastly, in the third part, we focus on active manipulation of dynamic objects. We present an approach that ensures safety during the task by reducing interaction forces according to Passivity-Based Control theory. Additionally, we show a sampling-based framework that finds optimal motion policies in order to manipulate a workpiece according to a given task description. In summary, this thesis contributes to the advancement of the control of aerial manipulators and motion planning towards a higher degree of pose- and force tracking accuracy, safety, and autonomy.

A forwarder named XT28 has been developed in joint collaboration between eXtractor AB, Skogforsk AB, KTH, Linköpings Unversity, Sveaskog, and Bosch Rexroth. The forwarder is equipped with six hydraulic active suspension pendulum arms. This allows for a lower impact on the soils in the forest but also increases the productivity and operation of the forwarder. Attempts have been done to find a controller design in order to control the pendulum arms effectively. Even though the pendulum arms have successfully been controlled using combinations of P- and PID controllers, these controllers have shown to not be effective enough for end use. Therefore there is still a need for a more appropriate controller. This is what this thesis work set out to solve using a real-life downscaled model of the XT28 forwarder. During this thesis work the pendulum arms of the downscaled XT28 forwarder model were redesigned in order to fit passive springs and accommodate load cell force sensors to the pendulum arms. This was done to reduce the stiffness of the chassis and allow for new controller design approaches. Four controllers were implemented during the project based on simplified vertical full-vehicle models, including two Offset-free Model Predictive controllers and two Linear Quadratic Controllers with integral action, with both load cell force- and piston rod position control of the linear actuators in the pendulum arms. The controllers were tested in real-time using a self-built test track with obstacles. A quantitative approach was used to minimize the roll- and pitch angle changes, and their derivatives, of the vehicle body and was compared between the implemented controllers when the forwarder drove through the test track in real time. Furthermore, the effect on controller performance by implementing springs and load cells in the system was also investigated. The results showed that it was possible to successfully implement the proposed controllers using simplified vertical full-vehicle models. The best-performing controllers, which were shown to be the Model Predictive controllers with load cell force- and piston rod position control performed the best with both the springs and load cells implemented. Further, the load cells showed to have a vital part in the success of all of the controllers. En skotare vid namn XT28 har utvecklats utav eXtractor AB, Skogforsk AB, KTH, Linköpings Universitet, Sveaskog och Bosch Rexroth. Skotaren är utrustad med sex hydrauliska pendelarmar som används som dess fjädringssytem. Dessa pendelarmar förminskar påverkan på mark och jord i skogen och ökar även produktiviteten och styrbarheten av fordonet. Försök har gjorts för att hitta en regulator som kan styra pendelarmarna effektivt. Bland annat en kombinerad P- och PID regulator har lyckats styra pendelarmarna, men inte effektivt nog för att kunna användas i slutprodukten. Därför behövs fortfarande en effektiv regulator för att kunna styra dessa pendelarmar. Det här mastersarbetet har som mål att hitta en effektiv regulator med hjälp av en verklig nedskalad modell av XT28 skotaren. Under detta mastersarbete så designades pendelarmarna om för att möjliggöra implementeringen av passiva fjädrar samt lastcell kraftsensorer i pendelarmarna på den nerskalade modellen. Detta gjordes för att minska styvheten av modellen men också för att tillåta nya tillvägagångssätt vid implementering av nya regulatorer. Fyra regulatorer implementerades under projektet baserade på simplifierade vertikala hela fordons modeller. Två av dessa var Modell Prediktiva regulatorer varav två var Linjär Kvadratiska regulatorer med både lastcells kraft- och kolvstång position reglering av de linjära aktuatorerna i pendelarmarna. Alla regulatorer testades i realtid med hjälp av en egentillverkad testbana med hinder. En kvantitativ metod användes för att jämföra hur effektivt de olika regulatorerna kunde minimera rull- och stigningsvinklarna, och dess derivator, av fordonet då det körde genom testbanan i realtid. Hur prestandan av regulatorerna påverkas av implementering av de passiva fjädrarna och lastcellerna undersöktes också. Resultaten av dessa tester visade att det gick att implementerade de förslagförda regulatorerna baserade på simplifierade modeller. Regulatorerna som visade sig ha bäst prestanda var de Modell-Prediktiva regulatorerna med både lastcell kraft- samt kolvstång position reglering då både de passiva fjädrarna och lastcellerna var implementerade. Det kunde även noteras att lastcellerna hade en vital del i hur alla regulatorer presterade.

Die Bewegungsplanung für automatisierte Fahrzeuge (AVs) in gemischtem Verkehr ist eine herausfordernde Aufgabe. Hierbei bezeichnet gemischter Verkehr, Verkehr bestehend aus von Menschen gefahrenen Fahrzeugen sowie automatisierten Fahrzeugen. Um die Komplexität der Aufgabe zu reduzieren, verwenden state-of-the-art Planungsansätze oft die vereinfachende Annahme, dass das zukünftige Verhalten umliegender Fahrzeuge unabhängig vom Plan des AVs vorhergesagt werden kann. Während die Trennung von Prädiktion und Planung für viele Verkehrssituationen eine hilfreiche Vereinfachung darstellt, werden hierbei Interaktionen zwischen den Verkehrsteilnehmern ignoriert, was besonders in interaktiven Verkehrssituationen zu suboptimalem, übermäßig konservativem Fahrverhalten führen kann. In dieser Arbeit werden zwei interaktionsbewusste Bewegungsplanungsalgorithmen vorgeschlagen, die in der Lage sind übermäßig konservatives Fahrverhalten zu reduzieren. Der Kernaspekt dieser Algorithmen ist, dass Prädiktion und Planung gleichzeitig gelöst werden. Mit diesen Algorithmen können anspruchsvolle Fahrmanöver, wie z. B. das Reißverschlussverfahren in dichtem Verkehr, durchgeführt werden, die mit state-of-the-art Planungsansätzen nicht möglich sind. Der erste Algorithmus basiert auf Methoden der Multi-Agenten-Planung. Interaktionen zwischen Verkehrsteilnehmern werden durch Optimierung gekoppelter Trajektorien mittels einer gemeinsamen Kostenfunktion approximiert. Das Kernstück des Algorithmus ist eine neuartige Multi-Agenten-Trajektorienplanungsformulierung, die auf gemischt-ganzzahliger quadratischer Programmierung (MIQP) basiert. Die Formulierung garantiert global optimale Lösungen und ist somit in der Lage das kombinatorische Problem zu lösen, welches kontinuierliche Methoden auf lokal optimale Lösungen beschränkt. Desweiteren kann durch den vorgestellten Ansatz ein manöverneutrales Verhalten erzeugt werden, das Manöverentscheidungen in ungewissen Situationen aufschieben kann. Der zweite Ansatz formuliert Interaktionen zwischen einem menschlichen Fahrer und einem AV als ein Stackelberg-Spiel. Im Gegensatz zu bestehenden Arbeiten kann der Algorithmus allgemeine nichtlineare Zustands- und Eingabebeschränkungen berücksichtigen. Desweiteren führen wir Mechanismen zur Integration von Kooperation und Rücksichtnahme in die Planung ein. Damit wird übermäßig aggressives Fahrverhalten verhindert, was in der Literatur als ein Problem interaktionsbewusster Planungsmethoden identifiziert wurde. Die Wirksamkeit, Robustheit und Echtzeitfähigkeit des Algorithmus wird durch numerische Experimente gezeigt.

EPC4SES Open access data for an open access publication

Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of the driving strategy. Thus, model predictive control based advanced driver assistance systems to reduce the energy consumption during driving gains a significant interest in the literature. However, this results in a complex control system with many parameter dependencies that could possibly affect the energy efficiency of the vehicle. Most of these parameters are subject to uncertainties. Thus, the important question remains how these parameter uncertainties affect the energy efficiency of the system and how a driver assistance system should be designed to be robust against these uncertainties. To answer this question this thesis applies variance-based sensitivity analyses to design an appropriate driver assistance system and to quantify the influences of the uncertain system and controller parameters. First, a detailed vehicle and powertrain model of a battery electric vehicle is evolved and verified on component test benches. The parameter uncertainties and their sensitivities were investigated on typical urban and interurban commuter routes using quantitative variance-based sensitivity analysis methods. Based on these findings an economic nonlinear model predictive control eco-cruise control is derived which takes the identified parameter dependencies into account. The developed economic nonlinear model predictive control system is evaluated on artificial drive cycles and compared to a linear model predictive control approach as often outlined in the literature. Afterwards, the closed loop control system, consisting of the developed economic nonlinear model predictive control and the detailed vehicle model, is analyzed on typical urban and interurban commuter routes using variance-based sensitivity analysis. The findings and parameter dependencies are outlined and discussed. It has been shown, that vehicle parameters as well as controller parameters impact the energy consumption and the driving time of the vehicle. It has been outlined that if the as influential identified parameters are optimized, an average energy-saving potential on the investigated routes of 10.5% exists by only increasing the driving time of 0.7%.

Указывается, что в настоящее время положение с уровнем загрязнения акватории бассейна р. Терек на территории Чеченской Республики и Ингушетии, особенно основных рек – Сунжа и Терек, и деградацией его экологического состояния остается весьма актуальным. Целью работы является оценка загрязненности акватории рек Сунжа и Терек в пределах Чечни и Ингушетии, состояния их водных ресурсов и биоразнообразия. Использован метод сравнительного анализа. Отмечается, что промедление с принятием срочных мер по оздоровлению экосистемы Терека ведет к невозможности восстановления ее оптимального состояния. Показано, что все это весьма негативно сказывается на способности биологического разнообразия региона к самовозобновлению, отрицательно влияет на устойчивость и развитие продуктивности экосистем. Утверждается необходимость активизации научно-исследовательской деятельности по оценке экологического состояния природных биоценозов бассейна р. Терек, разработок и реализации необходимых рекомендаций и мер по очищению водных ресурсов, созданию благоприятных условий для существования и функционирования биоразнообразия – основы устойчивого развития данной территории. Для выполнения такой деятельности по очищению водной среды от загрязняющих веществ и экологическому ее оздоровлению должны быть получены результаты исследований по характеру, степени и путям загрязнения рек Сунжа и Терек, выявлены источники, определены показатели деструктивности воздействия негативных факторов на все параметры экосистем бассейна р. Терек. В связи с этим отмечается необходимость исследования гидробиологического режима и состояния биоразнообразия бассейна р. Терек, в том числе и на территории Чеченской Республики и Республики Ингушетия., It is indicated that at present the situation with the level of pollution of the Terek River basin in the territory of the Chechen Republic and Ingushetia, especially the main rivers – Sunzha and Terek, and the degradation of its ecological state remains very relevant. The aim of the work is to study the environmental problems of the Terek river basin, the state of its water resources and biodiversity. The method of comparative analysis was used. It is noted that the delay in taking urgent measures to improve the ecosystem of the Terek leads to the impossibility of restoring its optimal state. It is shown that all this has a very negative effect on the ability of the biological diversity of the region to self-renewal, negatively affects the stability and development of the productivity of ecosystems. The need to intensify research activities to assess the ecological state of natural biocenoses of the Terek River basin, the development and implementation of the necessary recommendations and measures to purify water resources, create favorable conditions for the existence and functioning of biodiversity – the basis for sustainable development of the territory. To carry out such activities to purify the aquatic environment from pollutants and its ecological improvement, the results of studies on the nature, degree and ways of pollution of the Sunzha and Terek rivers should be obtained, sources identified, indicators of the destructiveness of the impact of negative factors on all parameters of the ecosystems of the Terek river basin should be determined. In this regard, it is noted that it is necessary to study the hydrobiological regime and the state of biodiversity in the Terek River basin, including in the territory of the Chechen Republic and the Republic of Ingushetia., Грозненский естественнонаучный бюллетень, Выпуск 1 (31) 2023, Pages 69-74

Today, the control of heat pumps aims to first and foremost maintain a comfortable indoor temperature. This is primarily done by deciding input power based on outside temperature. The cost of electricity, which can be rather volatile, is not taken into account. Electricity price can be provided on an hourly rate, and since a house can store thermal energy for a duration of time, it is possible to move electricity consumption to hours when electricity is cheap. In this thesis, the strategy used in the developed controller is Model Predictive Control (MPC). It is a suitable strategy because of the ability to incorporate an objective function that can be designed to take the trade-off between indoor temperature and electricity cost into account. The MPC prediction horizon is dynamic as the horizon of known electricity spot prices varies between 12 and 36 hours throughout the day. We model a residential house heated with a ground source heat pump for use in a case analysis. Sampled weather and spot price data for three different weeks are used in computer simulations. The developed MPC controller is compared with a classic \textit{heat curve} controller, as well as with variations of the MPC controller to estimate the effects of prediction and model errors. The MPC controller is found to be able to reduce the electricity cost and/or provide better comfort and the prioritization of these factors can be changed depending on user preferences. When shifting energy consumption in time it is necessary to store thermal energy somewhere. If the house itself is used for this purpose, variations in indoor temperature must be accepted. Further, accurate modeling of the Coefficient of Performance (COP) is essential for ground source heat pumps. The COP varies significantly depending on operating conditions and the MPC controller must therefore have a correct perception of the COP. Publicly available weather forecasts are of sufficient quality to be usable for future prediction of outside temperature. For future studies, it would be advantageous if better models can be developed for prediction of global radiation. Including radiation in the MPC controller model would enable better comfort with very similar operating costs compared to when the MPC controller does not take radiation into account.

Multicopters such as quadcopters are a popular tool within industries such as mining, shipping and surveillance where a high level of autonomy can save time, increase efficiency and most importantly provide safety. While Unmanned Aerial Vehicles have been a big area in research and used in the mentioned industries, the level of autonomy is still low. Simple actions such as loading and offloading payload or swapping batteries is still a manual task performed by humans. If multicopters are to be used as an autonomous tool the need for solutions where the machines can perform the simplest task such as swapping batteries become an important stepping stone to reach the autonomy goals. Earlier works propose landing solutions focused on landing autonomous vehicles but the lack of accuracy is hindering the vehicles to safely dock with a landing platform. This thesis combines multiple areas such as trajectory generation, visual marker tracking and UAV control where results are shown in both simulation and laboratory experiments. With the use of a Model Predictive Controller for both trajectory generation and UAV control, a multicopter can safely land on a small enough platform which can be mounted on a small mobile robot. Additionally an algorithm to tune the trajectory generator is presented which shows how much weights can be increased in the MPC controller for the system to remain stable.

This paper presents the enhanced operation of the path planner integrated with a predictive controller for a self-driving vehicle to accomplish trajectory planning and avoid obstacles. The path planner used the Batch Informed Trees (BIT*) planning algorithm approach and the tracking controller is designed based on the model predictive control (MPC). BIT* algorithm is used to find the best path between the start and the goal nodes. Then the MPC tracks the route and controls the vehicle's movement to its destination. Path planning control is vital point in avoiding autonomous car the obstacles during serious traffic scenarios. The MPC controls the main parameters of the vehicle: velocity, acceleration, and orientation. The traditional BIT* operation is enhanced by subjecting the generated trajectory to a basis spline (B-Spline) planner. This enhancement solves the hard angle and manoeuvre presented in the path, improves the trajectory points connections, and then swiftly obtains a collision-free trajectory. In addition, this paper tackles the issues related to avoiding local obstacles and the follow up of dynamic goal points in a complex and dynamic world. The model predictive controller is used to track the enhanced trajectory plan generated by the BIT*planner approach by using the kinematic model of the vehicle. A modal description of the approach for building the graph-search for these cases and displaying simulated and real-world examining data shows this method's practical application. In the simulation, the controller selects the best trajectories as references. Also, it enhances the performance of trajectory planning and ensures that the casual obstacle can be avoided in real-time and the robot can arrive at the final point smoothly. The results of the simulation show a reasonable accomplishment in navigation performance, the planned path is softer, and the efficiency of the search is higher in composite environments and different scenarios. Also, the test shows that the autonomous car can pursue the reference path accurately, even with sharp corners.

The cryogenic refrigerators in the grand research instruments (e.g. LHC in CERN and JT60SA in Japan, ITER in France) require more and more reliability in the various operating phases, especially in transient phases or in the presence of variable thermal loads. Such systems consists of several cryogenic sub-processes which are controlled by the local controllers (normally PID controllers). Such decentralized strategy is sufficient as long as the system always stay around the nominal points. In addition, the coupling of the dynamics of the controllers as well as the absence of anticipation make this type of control not very suitable during transient phases or in the presence of highly variable thermal loads. In this thesis, a recently proposed hierarchical control framework will be developed for the 400 W at 1.8 K refrigerator at CEA (France). In this framework, an coordinator is developed to coordinate the local controllers in order to optimize the global performance. Such framework is demonstrated by being applied to control the 400 W at 1.8 K refrigerator, in which nonlinear models, actuator constraints and real-time implementation are considered. Fast-NMPC and deep learning approaches are also studied and implemented at the local controllers in order to reduce the computation time and make the proposed framework feasible in real-time. Finally, this framework is applied to control a more complex refrigerator. To make this happen, some developments, which concerns an method that converges the communication between the coordinator and the subsystems and an optimization solver for the coordinator, are implemented.; Les réfrigérateurs cryogéniques des grands instruments de recherche (par exemple, le LHC au CERN et le JT60SA au Japon, ITER en France) exigent une fiabilité de plus en plus grande dans les différentes phases de fonctionnement, notamment dans les phases transitoires ou en présence de charges thermiques variables. Ces systèmes se composent de plusieurs sous-processus cryogéniques qui sont contrôlés par des régulateurs locaux (normalement des régulateurs PID). Une telle stratégie décentralisée est suffisante tant que le système reste toujours autour des points nominaux. De plus, le couplage de la dynamique des contrôleurs ainsi que l'absence d'anticipation rendent ce type de contrôle peu adapté lors des phases transitoires ou en présence de charges thermiques très variables. Dans cette thèse, un cadre de contrôle hiérarchique récemment proposé sera développé pour le réfrigérateur de 400 W à 1,8 K du CEA (France). Dans ce cadre, un coordinateur est développé pour coordonner les contrôleurs locaux afin d'optimiser la performance globale. Ce cadre est démontré en étant appliqué au contrôle du réfrigérateur de 400 W à 1,8 K, dans lequel les modèles non linéaires, les contraintes d'actionnement et la mise en œuvre en temps réel sont pris en compte. Des approches de Fast-NMPC et d'apprentissage profond sont également étudiées et mises en œuvre au niveau des contrôleurs locaux afin de réduire le temps de calcul et de rendre le cadre proposé réalisable en temps réel. Enfin, ce cadre est appliqué pour contrôler un réfrigérateur plus complexe. Pour ce faire, certains développements, qui concernent une méthode qui converge la communication entre le coordinateur et les sous-systèmes et un solveur d'optimisation pour le coordinateur, sont mis en œuvre.

Possibly the most critical phase of an Unmanned Air Vehicle (UAV) flight is landing. To reduce the risk due to pilot error, autonomous landing systems can be used. Environmental disturbances such as wind shear can jeopardize safe landing, therefore a well-adjusted and robust control system is required to maintain the performance requirements during landing. The paper proposes a loop-shaping-based Model Predictive Control (MPC) approach for autonomous UAV landings. Instead of conventional MPC plant model augmentation, the input and output weights are designed in the frequency domain to meet the transient and steady-state performance requirements. Then, the H∞ loop shaping design procedure is used to synthesize the state-feedback controller for the shaped plant. This linear state-feedback control law is then used to solve an inverse optimization problem to design the cost function matrices for MPC. The designed MPC inherits the small-signal characteristics of the H∞ controller when constraints are inactive (i.e., perturbation around equilibrium points that keep the system within saturation limits). The H∞ loop shaping synthesis results in an observer plus state feedback structure. This state estimator initializes the MPC problem at each time step. The control law is successfully evaluated in a non-linear simulation environment under moderate and severe wind downburst. It rejects unmeasured disturbances, has good transient performance, provides an excellent stability margin, and enforces input constraints.

This paper presents a torque controller for the energy optimization of the powertrain of an electric Formula Student race car. Limited battery capacity within electric race car designs requires energy management solutions to minimize lap time while simultaneously controlling and managing the overall energy consumption to finish the race. The energy-managing torque control algorithm developed in this work optimizes the finite onboard energy from the battery pack to reduce lap time and energy consumption when energy deficits occur. The longitudinal dynamics of the vehicle were represented by a linearized first-principles model and validated against a parameterized electric Formula Student race car model in commercial lap time simulation software. A Simulink-based model predictive controller (MPC) architecture was created to balance energy use requirements with optimum lap time. This controller was tested against a hardware-limited and torque-limited system in a constant torque request and a varying torque request scenario. The controller decreased the elapsed time to complete a 150 m straight-line acceleration by 11.4% over the torque-limited solution and 13.5% in a 150 m Formula Student manoeuvre.

The SARS COV2 pandemic hits the life and livelihoods of millions and consequently slows down the world economy. The pandemic hits hard the specific social groups due to travel restrictions/bans and other regulations that affect their income and consumption patterns. The goal of this paper is to find out whether the pandemic has any effect on consumption and income patterns among consumers in rural settings. To implement this study, structured questionnaires were sent to respondents and collect data from 180 samples living in rural areas of four different administrative districts in Bangladesh such as Rajshahi, Bogura, Naogaon, and Natore. Using paired sample T-test (parametric) and Wilcoxon signed ranked test (nonparametric) test found that pandemics had a significant effect on the pattern of consumption and income in the northern area of Bangladesh. While the Keynesian method of income determination shows that the MPC before the COVID-19 pandemic was 0.31 and during it was 0.37. 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This paper presents an $O(\log\log \bar{d})$ round massively parallel algorithm for $1+\epsilon$ approximation of maximum weighted $b$-matchings, using near-linear memory per machine. Here $\bar{d}$ denotes the average degree in the graph and $\epsilon$ is an arbitrarily small positive constant. Recall that $b$-matching is the natural and well-studied generalization of the matching problem where different vertices are allowed to have multiple (and differing number of) incident edges in the matching. Concretely, each vertex $v$ is given a positive integer budget $b_v$ and it can have up to $b_v$ incident edges in the matching. Previously, there were known algorithms with round complexity $O(\log\log n)$, or $O(\log\log \Delta)$ where $\Delta$ denotes maximum degree, for $1+\epsilon$ approximation of weighted matching and for maximal matching [Czumaj et al., STOC'18, Ghaffari et al. PODC'18; Assadi et al. SODA'19; Behnezhad et al. FOCS'19; Gamlath et al. PODC'19], but these algorithms do not extend to the more general $b$-matching problem., Comment: This paper appeared in Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) 2022

Este documento presenta un marco de gemelo digital dentro del dominio de la agricultura inteligente. Diseña un gemelo digital basado en el control predictivo autónomo de un invernadero. El diseño de un entorno digital para crear un sistema ciberfísico capaz de controlar los parámetros ambientales del invernadero de forma remota, y para permitir al usuario entender mejor el invernadero y su entorno. El gemelo digital presentado usa un control de modelo predictivo basado en la anticipación de las previsiones meteorológicas, y servicios para recuperar y guardar los datos de la nube. This document presents a digital twin framework in the smart farming domain. It designs a digital twin based on the autonomous predictive control of a greenhouse. The design of a digital environment to create a cyber-physical-system able to control the greenhouse ambient parameters remotely, and to allow the user to understand better the greenhouse and its environment. The digital twin presented uses model predictive control based on previewing weather forecasts, and services to retrieve and store the data in the cloud. Aquest document presenta un marc de bessó digital dins del domini de l’agricultura intel·ligent. Dissenya un bessó digital basat en el control predictiu autònom d’un hivernacle. El disseny d’un entorn digital per crear un sistema ciberfísic capaç de controlar els paràmetres ambientals de l’hivernacle de forma remota i, per permetre a l’usuari entendre millor l’hivernacle i el seu entorn. El bessó digital presentat usa un control de model predictiu basat en l’anticipació de les previsions meteorològiques, i serveis per recuperar i guardar les dades del núvol. Outgoing

This paper presents a microgrid energy flow optimization algorithm with variable battery storage efficiency in order to achieve energy savings and expand the lifespan of the components. The converter efficiency curve is deduced from converter's datasheets and approximated with mathematical functions. The power loss on the battery internal resistance is also included in order to achieve a more accurate model of the complete storage system. The obtained nonlinear model is used in model predictive control formulation and solved by using a sequential linear program (SLP) algorithm. The SLP algorithm iteratively linearizes the model around the current solution and uses corresponding efficiencies over the prediction horizon. Simulations in MATLAB are performed for a 7-day period and compared with a conventional, constant- efficiency battery system model. The results show an improved performance regarding the charging and discharging battery power and the overall savings of 7% in comparison with the conventional model used in model predictive control.

This study aims to evaluate the interior permanent magnet synchronous motor (IPMSM) drive performance for various load conditions under steady state and dynamic conditions. Therefore, this paper proposes finite set model-predictive control (FS-MPC) for IPMSM with maximum torque per ampere (MTPA) for electric tractor application. The MTPA control technique is used to obtain maximum torque while maintaining a minimum current constraint. In addition to MTPA control, the MPC scheme is used as the suitable alternative control strategy in the electric tractor application, which eliminates the occurrence of torque ripples during the dynamic speed tracking under variable load conditions. The MPC is used to improve the dynamic response of the motor drive and reduce torque ripples under variable load conditions. MPC–MTPA is developed in the MATLAB/SIMULINK and validated in the real-time environment using the hardware-in-the-loop (HIL) simulator (OPAL-RT OP5700). The results prove that MPC improves the dynamic performance and MTPA reduces the stator copper loss and increases the drive efficiency.

The article highlights the ecological danger of military operations on the territory of Ukraine. The results of experimental studies of the content of gross forms of heavy metals in ten soil samples taken from agricultural lands of Sumy and Okhtyrka districts of Sumy region are presented. Three samples were taken from the places where air bombs had fallen, seven from the places of broken military equipment. As a result of the conducted research, it was established that the background level in terms of lead content was exceeded in all soil samples, the average content of the element in the polluted territories was 5.4 times higher than the background value. Exceeding the background level in terms of zinc content was noted in nine out of ten soil samples, the average content of the element in the polluted areas was 3.9 times higher than the background value. The average content of cadmium in the polluted territories was 1.4 times higher than the background value (the excess was noted in eight soil samples). Exceeding the background value of the gross copper content was found in eight soil samples, on average, according to the experiment, the copper content is 4.6 times higher than the background value. Exceeding the background value for the content of nickel and iron was noted only in three soil samples, and the average content of the elements exceeded the background value by 1.2 and 1.1 times. The average manganese content in the polluted areas was 4.8 times higher than the background value (the excess was noted in six soil samples). Based on the results of the research, it was established that the gross content of lead (in six soil samples), zinc and manganese (in two soil samples), and copper (in five soil samples) from the sites of hostilities exceeded the permissible limits. The content of gross forms of cadmium and nickel did not exceed the MPC. A significantly higher coefficient of variation of the content of gross forms of heavy metals in the combat zone, compared to the content outside the combat zone (background value), may indicate the intensity of the influence of a negative factor on the soil cover. The highest degree of disturbance of the ground cover as a result of hostilities was noted in the places of burned equipment (the villages of Kosivshchyna, Stare Selo, Nyzy, Sumy district, Sumy region). According to the results of the correlation analysis, an extremely strong dependence was established between the content of the studied forms of heavy metals., У статті висвітлено екологічну небезпеку військових дій на території України. На- ведено результати експериментальних досліджень вмісту валових форм важких металів у десяти пробах ґрунтів, відібраних на землях сільськогосподарського при- значення Сумського та Охтирського р-нів Сумської обл. Три проби відібрано з місць падіння авіабомб, сім — із місць розбитої військової техніки. У результаті проведених досліджень встановлено перевищення фонового рівня за вмістом свинцю в усіх зразках ґрунту, середній вміст елементу на забруднених територіях у 5,4 раза перевищував фонове значення. Перевищення фонового рівня за вмістом цинку відмічено у дев’яти з десяти зразків ґрунту, середній вміст елементу на забруднених територіях у 3,9 раза перевищував фонове значення. Середній вміст кадмію на забруднених територіях у 1,4 раза перевищував фонове значення (перевищення відмічено у восьми пробах ґрунту). У восьми пробах ґрунту встановлено перевищення фонового значення валового вмісту міді, в середньому по досліду — вміст міді в 4,6 раза перевищує фонове значення. Пере- вищення фонового значення за вмістом нікелю та заліза відмічено лише в трьох про- бах ґрунту, а середній вміст елементів в 1,2 і 1,1 раза перевищував фонове значення. Середній вміст марганцю на забруднених територіях у 4,8 раза був вищий за фонове значення (перевищення відмічено у шести пробах ґрунту). За результатами досліджень встановлено перевищення ГДК валового вмісту свинцю (у шести пробах ґрунту), цинку та марганцю (у двох пробах ґрунту), міді (у п’яти пробах ґрунту) з місць бойових дій. За вмістом валових форм кадмію та нікелю перевищення ГДК не виявлено. Значно вищий коефіцієнт варіації вмісту валових форм важких металів у зоні бойових дій, порівнюючи зі вмістом поза зоною бойових дій (фонове значення,) може свідчити про інтенсивність впливу негативного чинника на ґрунтовий покрив. Найвищий ступінь порушення ґрунтового покриву внаслідок бойових дій відмічено у місцях згорілої техніки (с. Косівщина, с. Старе Село, с. Низи Сумського р-ну Сумської обл.). За результата- ми кореляційного аналізу встановлено надзвичайно сильну залежність між умістом досліджуваних форм важких металів.

This paper proposes a model-based predictive control strategy based on mixed-integer linear programming for a photovoltaic power plant with battery energy storage. The control objective is to maximize the revenues from energy delivered from both photovoltaic panels and batteries to the grid in a deregulated electricity market. For each control interval, the proposed algorithm incorporates information on solar radiation, market prices, and the state of charge of the batteries to determine the intervals of energy injection into the grid to maximize the economic benefits. The proposed strategy considers the rate-based variable efficiency in the battery model and time-varying energies prices, thus providing a more general implementation than previous schemes proposed in the literature for the same purpose. Simulations considering the operational procedures of the Spanish market as a case study show that, by integrating the battery efficiency in the model, the proposed control strategy increments the economic benefits in 21% compared to previous results reported in the literature for the same operational conditions. Additionally, the proposed approach reduces the number of charge and discharge cycles, potentially extending the lifespan of batteries.