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The clean transformation of building energy consumption is a critical pathway for achieving deep decarbonization. In light of the diversity of building energy needs, this paper proposes an integrated zero-carbon energy supply building architecture that involves the coupling of electrical, hydrogen, heat, and cooling energy sources. To address challenges in real-time supply-demand balancing and reliability of supply under extreme or boundary conditions due to complex energy couplings, the paper introduces an energy management framework with two control layers comprising mixed-integer linear programming (MILP) and model predictive control (MPC). This framework conducts hierarchical optimization to synergize economic and technical objectives. At the economic optimization layer, optimal economic parameters are formulated based on MILP; while at the MPC optimization layer, these parameters serve as reference states. By incorporating state relaxation method, the framework accommodates the uncertainties associated with system operation. Simulation analyses of typical weeks during summer and winter seasons demonstrate that the energy optimization management approach proposed in this paper can achieve highly reliable and economically stable operation of the building energy supply system. • A zero-carbon energy supply building energy structure is constructed. • Dynamic evolution model of electricity-hydrogen-heat-cooling multi-energy coupling system is established. • A double-layer energy management method including economic optimization and MPC optimization is proposed. [ABSTRACT FROM AUTHOR]

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC). • The LSTM-ARX model is proposed, it describes global nonlinearity dynamics and bears pseudo linear structure. • The explicit form of a deep learning based computational efficient MPC is proposed and its stability is proved. • A predictive functional control method is proposed to furtherly facilitate computational efficiency in real-time control. [ABSTRACT FROM AUTHOR]

The interstage subcooling vapor-injection technique has been introduced to transcritical CO 2 heat pumps to alleviate performance degradation under conditions of low ambient temperature and high water-outlet temperature. This type of solution holds considerable appeal and finds utility across a wide range of applications. However, the efficacy and efficiency of such systems are contingent upon their management, which frequently requires the implementation of suitable control systems. The primary objective is to enhance the overall system operational performance, resulting in tangible advantages in terms of economic viability, energy conservation, and environmental sustainability. To achieve these objectives, one can utilize both explicit and implicit optimization methods ranging from Model Predictive Control (MPC) to Extremum Seeking Control (ESC), each with its strengths and weaknesses. This paper compares the performance of an ad hoc integrated MPC strategy and a multi-variable ESC one. Based on the results of the system operation employing the latter strategy, a significant linear correlation was discovered between the optimal medium pressure and the optimal discharge and suction pressure. Consequently, an intrinsic self-optimization strategy for the medium pressure was developed. Subsequently, a dynamic system identification was performed on the new system, incorporating this proposed strategy. To optimize the system Coefficient of Performance (COP) while maintaining the water outlet temperature, ensuring the thermal load constraint, the MPC strategy was employed. The main focus of the MPC was to determine the optimal Electronic Expansion Valve (EEV) opening degree and water pump settings. The integrated MPC strategy was evaluated and compared to the multi-variable ESC strategy under various conditions, including fixed design conditions, changing ambient conditions, and step-change water outlet temperature. The results clearly demonstrated the effectiveness and superiority of the integrated MPC. [ABSTRACT FROM AUTHOR]

On-orbit servicing for the failed spacecraft involves two necessary stages: on-orbit observing and approaching. For these two stages, a novel approaching and pointing tracking control scheme under motion constraints and input saturation is proposed in this paper. For the security of approaching, a relative position guidance law for specific surface hovering is proposed. Then, a model predictive controller based on quadratic programming algorithm is designed. For the continuous observation task, the optimal maneuver angular velocity under the motion constraint is planned through model predictive control. In order to reduce the dimensionality of the optimization problem to reduce the computational effort, only the attitude kinematics equation is used as the process model. Thereafter, the adaptive anti-saturation attitude controller is designed to track the optimal maneuver angular velocity. The anti-saturation auxiliary system and the adaptive update law are used to cope with the control torque saturation and the integrated uncertainties, respectively. Finally, the stability of the closed-loop system is demonstrated by Lyapunov method. Simulation results demonstrate that the proposed control scheme accomplishes the approaching and pointing tracking task, and finally service spacecraft maintains hovering above the tumbling target and observing its specific surface. • The problem of approaching and pointing tracking for tumbling target is studied. • A relative pose guidance law for specific surface hovering is proposed. • A novel control scheme under motion constraints and input saturation is proposed. •.The control scheme accomplishes the tracking task with optimal angular velocity. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) is a valuable tool for economic optimization of chemical process systems. Incorporating plant dynamics and model predictive control (MPC) behavior into the RTO problem can improve its performance by accounting for plant transitions under the action of its control system, resulting in a closed-loop dynamic RTO (CL-DRTO) formulation. This paper extends the formulation for direct inclusion of uncertainty handling. A robust multi-scenario CL-DRTO scheme which models the dynamic behavior of the plant and its MPC system under uncertainty is introduced. The method is applied and its performance evaluated in two nonlinear case studies, where an input clipping approximation scheme is used to reduce the computation time. The effects of number of scenarios and multiple sources of uncertainty are also investigated. • Scenario based stochastic formulation for robust dynamic RTO (DRTO). • Dynamic RTO uses prediction of plant response under action of constrained MPC. • Input clipping approximation used to reduce computation time. • SISO and MIMO case studies, with single and multiple sources of uncertainty. • Robust DRTO shown to outperform DRTO that is based on a nominal model. [ABSTRACT FROM AUTHOR]

The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation. • A modified MPC strategy is proposed for the formation control. • The use of an FTDOB ensures that the estimation error converges to zero within a fixed time. • KCF is used to fuse the data shared in the formation with noise and uncertainty. [ABSTRACT FROM AUTHOR]

The application of reflective heat insulation coatings constitutes an effective means to reduce building energy consumption and carbon emissions. The current study investigated a novel multifunctional coating developed based on chemically bonded magnesium phosphate cement (MPC). The coating consists of MPC and functional compositions. MPC is the film-forming substance and functional compositions include titanium dioxide, hollow glass microspheres, sodium silicate, dried tangerine peel, and Sophora flavescens, etc. This coating was a novel water-based inorganic one with advantages such as outstanding reflective thermal insulation performance, good durability, mildew resistance, environmental friendliness, and sprayability. The results demonstrated that when the amount of titanium dioxide was 25 %, the amount of glass microspheres was 6 %, and the coating thickness was 600 μm, the optimal reflective thermal insulation performance could be attained, with a reflectivity as high as 73 % and a thermal insulation temperature difference of 35.7℃. The addition of sodium silicate mitigated the issue of difficult dispersion of titanium dioxide. When the boric acid content was 8 %, the sodium dodecahydrate phosphate content was 2 %, and the water-cement ratio was 0.36, the setting time could reach 25 min and the fluidity could reach 180 mm, fulfilling the spraying requirements and also possessing good durability. The most coatings use organic materials as film forming agents which generally release VOC (Volatile organic compounds). According to the results about the current water-based inorganic chemically reactive coating, a multifunctional coating with unique film forming mechanism, ecological and low carbon emission had been developed. • A novel multi-function inorganic reflective thermal insulation coating was developed. • The film-forming mechanism of the coating is based on chemical reaction of magnesium phosphate. • The coating slurry can be easily sprayed by the optimization of the workability. • The addition of tangerine peel and Sophora flavescens enhanced the mildew resistance of the coating. [ABSTRACT FROM AUTHOR]

Hepatic de novo lipogenesis (DNL) is a fundamental physiologic process that is often pathogenically elevated in metabolic disease. Treatment is limited by incomplete understanding of the metabolic pathways supplying cytosolic acetyl-CoA, the obligate precursor to DNL, including their interactions and proportional contributions. Here, we combined extensive 13C tracing with liver-specific knockout of key mitochondrial and cytosolic proteins mediating cytosolic acetyl-CoA production. We show that the mitochondrial pyruvate carrier (MPC) and ATP-citrate lyase (ACLY) gate the major hepatic lipogenic acetyl-CoA production pathway, operating in parallel with acetyl-CoA synthetase 2 (ACSS2). Given persistent DNL after mitochondrial citrate carrier (CiC) and ACSS2 double knockout, we tested the contribution of exogenous and leucine-derived acetoacetate to acetoacetyl-CoA synthetase (AACS)-dependent DNL. CiC knockout increased acetoacetate-supplied hepatic acetyl-CoA production and DNL, indicating that ketones function as mitochondrial-citrate reciprocal DNL precursors. By delineating a mitochondrial-cytosolic DNL substrate supply network, these findings may inform strategies to therapeutically modulate DNL. [Display omitted] • The MPC and ACLY gate the major hepatic lipogenic acetyl-CoA production pathway • ACSS2-dependent DNL operates in parallel to the MPC-CiC-ACLY DNL pathway • AACS mediates acetyl-CoA production and DNL from ketones • CiC loss increases acetyl-CoA production and DNL from ketones Rauckhorst et al. investigated the metabolic pathways supplying cytosolic acetyl-CoA for hepatic de novo lipogenesis (DNL) by performing 13C tracing experiments in liver-specific single- and double-knockout mouse models. Surprisingly, loss of the mitochondrial citrate carrier increased acetyl-CoA production and DNL from exogenous and leucine-derived acetoacetate. [ABSTRACT FROM AUTHOR]

The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users' identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation. To this end, this paper proposes a T raceable A nonymity-self-controllable V 2X A uthentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users' credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %. [ABSTRACT FROM AUTHOR]

This systematic review aims to analyze if the combination of MPC and DMAHDM can enhance the antibacterial potential of DMAHDM without impairing the adhesive system's bond strength. A search was conducted on PubMed, Embase, Scopus, and Web of Science databases. Study selection was performed in two stages by two calibrated reviewers independently. The meta-analyses were conducted with the RevMan 5.4 software and evaluated metabolic activity (MA), lactic acid production (LA), protein repellent function (PRF), and dentin shear bond strength (DSBS) comparing DMAHDM vs. DMAHDM + MPC. The measure of effect was the mean or standardized mean difference. Quality assessment was evaluated with an adapted tool from a previous systematic review. The initial search resulted in 158 articles. Four articles were included in the final sample. The meta-analyses evidenced a statistically significant difference favoring the use of MPC to enhance DMAHDM's antibacterial potential for inhibiting MA (SMD, 3.74; 95 % CI, 2.66, 4.83), LA (MD, −1.24; 95 % CI, −1.42, −1.06), while providing protein-repellent function (MD, −5.38; 95 % CI, −6.35, −4.41). The addition of MPC had a negative effect on DSBS in comparison to DMAHDM alone (MD, −4.23; 95 % CI, −5.63, −2.83). The addition of MPC to DMAHDM-containing adhesives enhances the antibacterial efficacy and provides protein-repellent function, but impairs the bond strength. Future studies should test the addition of 5 % MPC. • The combination of MPC with DMAHDM enhances the antibacterial potential of dental adhesives. • The combination of MPC with DMAHDM provides lower bacterial metabolic activity. • The combination of MPC with DMAHDM provides lower lactic acid production. • The inclusion of MPC provides protein-repellent function. • The inclusion of 7.5 % MPC impairs dental adhesive bond strength. [ABSTRACT FROM AUTHOR]

• A new optimal control model of hydropower station gate. • Considering the coupling effect of power generation and flood discharge enhances water level prediction accuracy. • Satisfy different scheduling requirements in multiple scheduling scenarios. • This model can reduce the water consumption rate of hydrostation and improve the power generation efficiency. • This model can effectively reduce the number of gate adjustment and ensure the stability of water level. In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP. [ABSTRACT FROM AUTHOR]

Production planning and inventory control are essential for the logistic performance of breeding companies. In this paper, we discuss such a system for perennial crop seeds in which production during multiple years and a number of growth cycles before production starts are characteristic. Large variations in yield and demand are typical and could easily lead to shortages or excess in seed stock. Both are costly phenomena. For these reasons, production planning as currently done by seed breeders without much technical support is extremely challenging. This paper describes and models the seed production process of a breeding company and examines its impact on inventory levels. The approach involves developing a time-discrete model parameterised with historical data. Subsequently, three control schemes are formulated: a classical feedback–feedforward PID controller, a feedback–feedforward PID controller with a Smith Predictor and a Model Predictive Control scheme. The goal of this paper is to present and validate a novel seed production–inventory model. Only aged plants are destroyed after a fixed number of production cycles. The ordering of new plants is the input control variable. The model represents the multi-year seed production of perennial crop seeds and expands upon the dead-time delay model, which typically does not account for production level uncertainty in production–inventory systems. The parameters of the model create a general approach; for both annual and perennial crop seeds. • A novel production–inventory model for seeds from ageing plants was introduced. • The model represents the multi-year seed production of perennial crop seeds • The model is an extension of the dead-time delay model without production level uncertainty typical used in production–inventory systems. • Large variations in yield and demand are typical and could easily lead to shortages or excess seed stock. • A planning algorithm is proposed to reduce these variations to control the level of seed inventory. • A real-life case study showed that the planning of perennial crop seeds can be executed effectively by planning and control. • The method avoids costs while minimising the destruction of healthy plants due to obsolete inventories levels. [ABSTRACT FROM AUTHOR]

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. [ABSTRACT FROM AUTHOR]

Users of geo-localized applications on mobile devices need protection to avoid threats to their privacy. Such protection should vary in time, to cope with the dynamical nature of mobility data. We present a method to protect the privacy of users of location-based services, based on Model Predictive Control techniques. We employ three different predictors for future movements: an exact predictor, which serves as the baseline for the best expected performance, and two additional predictors allowing for online implementation. One of these predictors assumes the user is moving in a way that minimizes privacy, while the other is a linear predictor. The method has been applied to two datasets, Privamov and Cabspotting, which contain mobility data collected from real users when using a mobile device. The method demonstrated an improvement in privacy compared to a state-of-the-art mechanism by approximately 12% increase for Privamov users and 5% for Cabspotting users, while maintaining the same level of utility. • A Model Predictive Method to protect users of location-based services. • Two datasets with real user movement data were used to evaluate the method. • To assess the method, three predictors of future user positions were utilized. • The method improves privacy values compared to the current state of the art. [ABSTRACT FROM AUTHOR]

The marine autonomous surface ship (MASS) has gained significant attention because of its wide application in waterborne transportation in recent years. The trajectory control of the MASS is crucial in ensuring safety, particularly in narrow navigation areas and urgent encounter situations. Model predictive control (MPC) is well known as a sufficient method to solve the tracking problem considering the actuator saturation with model uncertainties and environmental disturbances. However, due to the constraints on computing resources and hardware limits, the controller may fail to find a feasible solution for MPC within a reasonable amount of time, especially when the system models are coupled and complex. To improve the solution efficiency, this paper introduces adaptive constraints to reduce search space for optimization, i.e., the current tracking point's speed is transformed into input constraints in the MPC formulation to decrease the computation burden, as well as reduce mechanical wear on the thrusters with smaller amplitudes of control actions. Simulations are carried out to evaluate the effectiveness of the proposed method with different MPC forms. • A constraint-adaptive MPC strategy is proposed for nonlinear systems, which reduces the computational burden and improves tracking efficiency compared to traditional NMPC. • The ranges of adaptive constraints are constructed from the dynamic trajectories, which serves to further reduce the fluctuations of the ship and improve control smoothness. • The safety of MPC is guaranteed by considering the feasible region of control inputs, particularly when computation time is limited. [ABSTRACT FROM AUTHOR]

• Developed a dynamic model for a six-wheeled ground mobile robot with independent steering and driving. • Utilized MPC strategy for double lane change trajectory tracking at low speeds. • Integrated MPC with ANFIS for high-speed trajectory tracking. • Simulation and physical test results demonstrate the method's tracking performance at various speeds. This study focuses on the control strategy for a ground mobile robot (GMR) with independent three-axis six-wheel drive and four-wheel independent steering, performing double lane change trajectory tracking in complex scenarios. Initially, a dynamic model of the six-wheel independent drive and steering GMR was constructed. Utilizing Model Predictive Control (MPC) technology, the challenge of trajectory tracking at low speeds was effectively addressed. For high-speed conditions, by thoroughly analyzing the impact of the predictive time-domain, this study innovatively introduced an Adaptive Neuro-Fuzzy Inference System (ANFIS) to dynamically adjust the prediction horizon of the MPC. A novel trajectory tracking algorithm integrating MPC and ANFIS was developed, with the network structure being trained using backpropagation (BP) method and the least squares method. Compared to traditional MPC, this hybrid strategy significantly improves trajectory tracking accuracy and stability at high speeds, with computational efficiency increased by 48.65%. Additionally, the algorithm demonstrated excellent adaptability and control effectiveness in various rigorous tests, including different speed levels, complex steering paths, load changes, sudden obstacles, and variable terrain. A 70 km/h trajectory tracking experiment on a physical vehicle yielded a root mean square (RMS) error of 0.1904 m, verifying its superior tracking performance and practical reliability. This provides a pioneering solution for high-performance trajectory control of ground mobile robots. Research on Ground Mobile Robot Trajectory Tracking Control Based on MPC and ANFIS [Display omitted] [ABSTRACT FROM AUTHOR]

Most of existing heat pump controls have been treated as local control problems where their control actions are solely based on local information, e.g. indoor air temperature. This study presents a real-life online federated control that can account for the interconnected dynamic behaviors in outdoor environment, an indoor thermal zone, and HP systems assisted by PV systems. The in-house federated simulation model consists of three artificial neural networks (ANNs) that predict dynamic behavior of the thermal zone temperature, HP energy consumption, and electricity generated by the PV, respectively. In other words, the dynamic inter-influences of the three systems are taken into consideration in the federated model where the time-varying input-outputs are cross-related. As a result, the federated model predictive control (MPC) demonstrated net energy saving by 40.4% compared to the local control over a week-long real-time implementation (local: 7,138 Wh vs. federated: 4,254 Wh). This study showcases how to federate the aforementioned entangled dynamic interactions between different dynamic systems for better control strategies. In other words, energy saving potential of multi-dynamic systems, e.g. room-HP-PV, would not be properly recognized unless they are federated. In addition, this study also emphasizes variations in the effectiveness of the federated control. [ABSTRACT FROM AUTHOR]

This paper presents an optimal control architecture for Pegasus, a novel quadruped wheel-legged robot with hybrid locomotion capabilities. The proposed control architecture comprises of a hierarchical motion planner and a model predictive controller (MPC) that optimizes motion planning and control in various stages. A command-based motion planner is implemented to map desired robot states to optimal joint positions and velocities. This enables the MPC to seamlessly integrate legged and wheeled locomotion as a single task. The legs are modeled as N-link manipulators, and parallel tracking MPC controllers are implemented to optimize torques. This approach results in improved motion control and comprehensive four-wheel independent steering mechanism maneuvers. The experiments and results demonstrate the practical feasibility and robustness of the proposed control approach, with Pegasus exhibiting stable balancing, precise motion control, and the ability to navigate through challenging paths. Overall, the proposed control architecture provides a promising solution for achieving hybrid locomotion capabilities in quadruped wheel-legged robots. • Control scheme for a novel Bio-Inspired Quadruped robot. • Centroidal optimum controller system design of a wheel-legged robot. • Optimum tracking torque controller for each leg deployed in embedded controllers. • Command-based optimized motion planner. [ABSTRACT FROM AUTHOR]

The recent international agreements signed by the majority of developed countries, such as those reached at the Climate Change Conferences in Paris'15 and Dubai'23, propose an increasingly rapid transition toward an energy ecosystem with a clear predominance of renewable energy in electrical power systems. The development of such ambitious energy programs should deal with the inherited stochasticity that renewable energy systems entail, which, when coupled with uncertainty about the capacity of the grid to maintain a power supply owing to increasing demands, decarbonization processes and the widespread closure of nuclear power plants, pose a serious threat to the normal functioning of energy systems. When combined with the escalation of armed conflicts that imply the loss of supply as a result of attacks or cyberattacks on power plants and distribution networks, it becomes clear that the current energy paradigm in which there is a centralized grid supplying numerous consumers, many of whom do not have their own generation capacity, must shift toward increasing the deployment of renewable-energy-based self-consumption facilities. The continuous advances toward a decentralized energy system of this nature will also lead to more cooperation, increasing the presence of energy communities with a great need to strengthen internal resilience as a sustaining factor. Considering the challenging framework of smart grids and energy transition, Microgrids would appear to be the key technology for the aggregation of generation, load and energy storage systems, and a cornerstone with which to provide the resilience and flexibility required for this new renewable-energy-based scenario. In addition to the complexity of the microgrid control problem, the issue of resilience energy management also has to be considered, which refers to the ability to adapt and supply loads during a specified period after a disruptive event with a loss of grid supply. This paper introduces an innovative method based on Model Predictive Control (MPC) techniques with the aim of enhancing resilience in microgrids by maximizing the energy surplus and reducing the aforementioned critical instants at which the capacity to feed loads is minimum in the case of power grid outages. The results obtained show that the proposed algorithm enhances the energy resilience in microgrids while the overall operational cost is optimized. A method with which to enhance the resilience of interconnected microgrids through cooperative optimization methods is also developed and validated. [Display omitted] • New techniques for resilience enhancement in microgrids/ networks of microgrids. • Optimizing energy management by enhancing surplus during critical resilience points. • Cooperative resilience management in networked microgrids with cost efficiency. [ABSTRACT FROM AUTHOR]

The constraints, input and output, are both important for path following in practical applications; unfortunately, few works have reported the results for path following with both the input and output constraints. A feasible solution for path following with the input and output constraints is presented in this paper, that is the constrained line of sight (LOS) guidance-based model predictive control (MPC) methodology. It contains two layers: the guidance layer and the control layer. The guidance layer is the barrier Lyapunov function-based LOS guidance for handling output constraints. The control layer is the MPC for handling input constraints. The design intent is that the output constraints are handled in the guidance layer, and the input constraints are handled in the control layer, relaxing the complexity of controller. Furthermore, the extended state observer (ESO) is also incorporated into MPC for approximation, minimizing the discrepancy between predicted model and plant. The effectiveness and superiority of the constrained LOS guidance-based MPC methodology are demonstrated by the simulation experiments. • The constrained LOS is realized based on barrier Lyapunov method. • Both the input and output constraints are addressed simultaneously. • Constrained LOS is presented for path following with input saturation. [ABSTRACT FROM AUTHOR]

Ocean thermal energy conversion (OTEC) provides a feasible solution for sustainable and stable power supply in remote islands. In this paper, a thermodynamic model of the OTEC system is developed to study the dynamic response of power output and superheating degree to manipulated variables. Afterward, a control strategy of load following for the island OTEC system is proposed. Finally, two controllers, MPC and PI, are designed and compared under fast and slow load changes as well as disturbance rejection test. The results indicate that the power output of OTEC system is sensitive to the speed of working-fluid pump, reaching the adjustment range of 14.7 kW when the nominal speed decreases by 30%. In addition, model predictive control (MPC) shows better performance in both rapid and slow load-change modes. Especially, in the rapid load-change mode, the MPC controller could always keep the evaporator outlet superheating in the range of 2–4.5 °C, while the PI controller may introduce no superheat that may cause liquid impact. Both MPC and PI controller can stabilize the power output at the set point under one day's measured disturbance, while MPC controller makes the superheating degree of evaporator outlet within smaller temperature fluctuations (<0.2 °C). [ABSTRACT FROM AUTHOR]

This paper presents a Model Predictive Control (MPC) design for the test mass release phase of the LISA space mission. LISA is a gravitational wave observatory consisting of a triangular constellation of three spacecraft. The gravitational waves are detected by measuring the relative distance between free falling test masses by means of a laser interferometer. Each test mass is a cubic body located inside an electrostatic suspension that is initially locked by a clamp mechanism. Once the plungers are retracted, the test masses are released with high initial offsets and velocities. To detect the gravitational waves, each test mass must be accurately positioned at the cage centre and its attitude must be aligned with the local cage frame. However, the low actuation authority of the electrostatic suspension along with the critical initial conditions, make the attitude and translation control a difficult task. MPC is a suitable technique for this application because it can systematically account for command saturations, state constraints and can provide optimal (or sub-optimal) control inputs by solving an optimization problem online. In this paper, an MPC controller is designed and validated by means of Monte Carlo simulations, achieving satisfactory results. • Model Predictive Control is a suitable method for the test mass release control problem of the LISA mission. • Model Predictive Control can deal with the low actuation authority of the electrostatic suspensions. • Model Predictive Control can deal with the state constraints of the test mass inside the cage. • Model Predictive Control can manage the operating mode transitions from Wide Range to High Resolution. [ABSTRACT FROM AUTHOR]

This work aims to demonstrate the benefits and limitations of an on-board Guidance for reusable launch vehicles, as well as to tradeoff different Model Predictive Control (MPC)-based Guidance and Control (G&C) architectures, exploiting, in particular, recent advances on successive convexification algorithms for optimization problems. Leading space agencies and private companies are investing on the development of reusable space launchers to reduce the cost to access the space. Indeed, that cost is one of the major deterrents in space exploration and space utilization. Reusability is, therefore, the unanimous solution to lower costs, and get a reliable and fast space access. Among many technological enhancements, the guidance, navigation, and control plays a crucial role: precise pinpoint landing capabilities or mid-air recovery, in fact, are mandatory. Indeed, the capability for generating re-optimized guidance trajectories on-board in real-time based on current flight conditions promises to improve the system performance, allows for fault tolerance capabilities, and reduces mission preparation costs. The work focuses especially on the implementation of a successive convexification Model Predictive Control guidance algorithm which solves the 6 Degree-of-Freedom (DoF) Powered Descent Guidance problem (PDG). The novelty of that work is applying a model predictive-based technique to a complex dynamic environment, trading off different solutions to the problem and relying on results obtained by using an industrial simulation framework. The robustness of the proposed approach is tested in several operative scenarios and the feasibility of real-time implementation is studied. For what concerns the trajectory optimization routine, the formulation of the problem, while initially being non-convex, is convexified. This is performed by implementing a successive convexification algorithm, which obtains a sub-optimal solution of the original problem in a fraction of the time required by a global optimizer, by solving a Second Order Cone Programming (SOCP) problem. This method allows coping with different kinds of dynamics nonlinearities, as well as cost functions and constraints. By presenting the approach and critically discussing the obtained results, the work provides an overview of the different methodologies available in the literature and assesses the limits of those approaches when applied to highly nonlinear scenarios, with large dispersions of uncertain parameters, as it is the case of reusable launch vehicles. • Formulation landing problem in successive convexification framework. • Assessing a state-triggered constraint based on dynamic pressure. • Assessing two Model Predictive Control-based architectures in realistic scenarios. • Underlining the critical aspects of the on-board Guidance & Control solutions. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, more and more deep learning models are used in solar radiation (irradiation) prediction. There have been a lot of studies discussing the research of this type of model. However, how to better apply the deep learning model in the optimization method of building energy system, such as multi-step solar radiation (irradiation) prediction model in model predictive control (MPC), is still a challenging issue due to the complexity of the time series and the accumulation of errors in multi-step forecasts. In this research, a deep generative model based on LSTM is developed for multi-step solar irradiation prediction at least 24 h in the future. Measured data and temperature forecast data from the Tokyo Meteorological Agency were used for training and testing in this experiment. The results show that generating the model first can effectively avoid the problem of error accumulation. The generative model can produce an accuracy improvement of 7.7 % against traditional regression LSTM model. Secondly, the introduction of the temperature forecast data from the previous one day can increase the forecast accuracy by about 18% points. When the earlier temperature forecast is used, the forecast accuracy will gradually decrease, and the use of the temperature forecast released 3 days before can hardly improve the forecast effect. In the end, using hourly temperature forecasts will result in better forecast accuracy than using daily temperature forecasts. [ABSTRACT FROM AUTHOR]

Compared to omnidirectional wheel robots and Mecanum wheel robots, four-wheel independent steering (4WIS) robots are more efficient. In recent years, 4WIS robot become the best choice for high-speed maneuverable mobile robots. However, the delay of the steering motor action and the control command exceeding the maximum speed of the steering motor make it difficult for the 4WIS robot to perform high-precision high-speed trajectory tracking. This paper proposes a high-speed trajectory tracking method combining the dynamics of the 4WIS robot. The A* algorithm is used for path planning, and then combined with the robot dynamics performance for trajectory planning. A 4WIS robot kinematics model and a model predictive control (MPC) controller with dynamic constraints are established. Simulations and experiments support the effectiveness and practicability of the trajectory tracking method. The high-speed trajectory tracking control of the 4WIS robot is realized. • A trajectory planning method based on robot dynamic performance is proposed. • The MPC controller of 4 WIS robot for high speed trajectory tracking is proposed. • Simulation and experimental results verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper evaluates the benefits of explicitly integrating online mineral liberation data in control systems for grinding–separation circuits. Although liberation is a critical variable for separation processes, this endeavour has not been attempted mainly because sensors providing continuous online or even at line measurements are yet to be developed. The ore particle size is seen as the key variable influencing mineral liberation. In this study, a phenomenological two-stage comminution circuit simulator previously calibrated from industrial and laboratory data was supplemented with a three-cell flotation line in open circuit. An economic real-time optimisation (RTO) layer coordinates the setpoints of a linear model predictive controller (MPC) of the grinding circuit. Assumed measurable, mineral liberation data feeds the RTO to update the particle size target parameter in an internal model predicting the flotation concentrate mass flow rate, grade, and recovery. Profits, derived from concentrate production rate, grade, and metal recovery, can improve by up to +5% compared with the standard approach, i.e. keeping the flotation feed particle size target constant. • The study integrates mineral liberation (assumed measurable) into RTO for grinding–flotation plants. • Monitoring liberation after grinding enables to react early to changes affecting flotation. • Concentrate production rate, grade, and metal recovery can improve from 1% to 2.5%. • The overall value of the concentrate can increase up to 5%. [ABSTRACT FROM AUTHOR]

Addressing the challenges of suboptimal path planning and insufficient dynamic obstacle avoidance for Unmanned Underwater Vehicles (UUVs), this paper presents a composite strategy that merges an enhanced A* path planning algorithm with Model Predictive Control (MPC). This dual-faceted approach synthesizes path planning and trajectory tracking control. Firstly, the six-degree-of-freedom kinematic and dynamic model of the UUV is established based on the modeling method of underwater vehicles. Secondly, an enhanced A* algorithm is implemented to generate an optimal reference path for the UUV within a three-dimensional environment. Subsequently, MPC is employed for trajectory tracking control. When encountering unforeseen dynamic obstacles on the reference path, the system initiates a real-time dynamic re-planning process, modifying the trajectory to circumvent obstacles while optimizing the objective function to guarantee the UUV's safe passage and accurate arrival at the intended destination. The simulation results prove the efficacy of this integrated method, demonstrating notable enhancements in the UUV's capacity for dynamic obstacle avoidance and the execution of real-time path planning. • An enhanced A* algorithm is used to generate the optimal reference path for the UUV in a three-dimensional environment. • A method combining enhanced A* and Model Predictive Control (MPC) is proposed for trajectory tracking control. • Dynamic re-planning and safe obstacle avoidance are achieved in a dynamic environment. [ABSTRACT FROM AUTHOR]

This work introduces a multi-objective optimization strategy to handle conflicting set-point tracking and economic objectives in a two-layer hierarchical control framework. A dynamic multi-objective real-time optimizer (DMO), incorporated in the upper layer, handles multiple control objectives with set-point tracking being the higher priority objective and computes optimal plant trajectories. This plant-wide trajectory information is communicated to the lower-layer model predictive control (MPC) operating at a faster sampling rate. The conventional weight-based and lexicographical method for the DMO are discussed. A new algorithm is conceptualized based on the lexicographical method to handle prioritized objectives. The proposed algorithm modifies the higher priority tracking objective and establishes improved economic performance compared to the conventional techniques, with minimal effect on the conflicting tracking objective, through a systematic choice of the preferred Pareto solution. The proposed algorithm's efficacy, within the hierarchical framework, is analyzed using two case studies: A polymerization reactor and a multi-unit reactor–separator system. • Dynamic multi-objective control solved using two-layer hierarchical architecture. • Upper layer handles priority-based multi-objectives using lexicographic approach. • Pareto-based trade-off of tracking cost proposed to improve economic performance. • Optimal trajectory implemented by lower layer MPC. • Two case studies demonstrate flexibility and performance of proposed approach. [ABSTRACT FROM AUTHOR]

Traditional power management systems for hybrid vehicles often focus on the optimization of one particular cost factor, such as fuel consumption, under specific driving scenarios. The cost factor is usually based on the beginning-of-life performance of system components. Typically, such strategies do not account for the degradation of the different components of the system over their lifetimes. This study incorporates the effect of fuel cell and battery degradation within their cost factors and investigates the impact of different power management strategies on fuel cell/battery loads and thus on the operating cost over the vehicle's lifetime. A simple rule-based power management system was compared with a model predictive controller (MPC) based system under a connected vehicle scenario (where the future vehicle speed is known a priori within a short time horizon). The combined cost factor consists of hydrogen consumption and the degradation of both the fuel cell stack and the battery. The results show that the rule-based power management system actually performs better and achieves lower lifetime cost compared to the MPC system even though the latter contains more information about the drive cycle. This result is explained by examining the changing dynamics of the three cost factors over the vehicle's lifetime. These findings reveal that a limited knowledge of traffic information might not be as useful for the power management of certain fuel cell/battery hybrid vehicles when degradation is taken into consideration, and a simple tuned rule-based controller is adequate to minimize the lifetime cost. • Degradation is essential to the assessment of lifetime cost of fuel cell vehicles. • The evolution of different degradation mechanisms will affect power management. • MPC-based power management is not necessarily better than rule-based ones. • Rule-based power management performs well under limited connected vehicle scenarios. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. [ABSTRACT FROM AUTHOR]

Split range control is used to extend the steady-state operating range for a single output (controlled variable) by using multiple inputs (manipulated variables). The standard implementation of split range control uses a single controller with a split range block, but this approach has limitations when it comes to tuning. In this paper, we introduce a generalized split range control structure that overcomes these limitations by using multiple independent controllers with the same setpoint. Undesired switching between the controllers is avoided by using a baton strategy where only one controller is active at a time. As an alternative solution we consider model predictive control (MPC), but it requires a detailed dynamic model and does not allow for using only one input at a time. • Generalized control structure for two or more inputs and one output. • Control structure to extend the steady-state operating range of an output. • A simple baton strategy avoids undesired switching of the active input. • PID-based control structure that allows to individually tune the controller for each input. [ABSTRACT FROM AUTHOR]

The heavy earth moving machineries (HEMM) like hydraulic excavator play a major role in construction and mining industries. In this context, the energy saving strategies in hydraulic excavator needs to be addressed considering its vital importance. Since the hydraulic excavators are subjected to heavy loads, hence the opportunity to harness the potential gravitational energy (GPE) remains a key area which can be effectively explored in order to minimize the energy consumption in consideration with hydraulic excavator. In the projected system, the potential energy is stored as pressure energy in hydro-pneumatic accumulator. The upward movement of the boom is executed with the help of prime mover during the starting of the first duty cycle. In the latter duty cycles, the stored pressurized energy is utilized together with the prime mover energy capable to execute the upward movement of the boom. The position of the boom cylinder is controlled by using the conventional PID controller using proportional flow control valve (PFCV) and accumulator. The error between the actual position and demand position of the linear actuator is minimized along with attainment of superior controlled performance while utilizing Model Predictive Controller (MPC). The pressurized accumulator with PFCV has been utilized to cater the different position demands. This has been also justified both experimentally and analytically with the error in the permissible range of 2%. It has been observed that the proposed system is 10% more efficient in contrast to the conventional system. • Boom potential energy regeneration using accumulator when actuator move backward. • Actuator position control by supplying pressurized stored in accumulator using proportional flow control flow. • Energy efficient hydraulic circuit for excavator application. • Stability analysis like bode plot, Nyquist plot and Root locus for the designed controller. • Increase in energy efficiency up to 10% for a complete duty cycle of proposed circuit compared with conventional circuit. [ABSTRACT FROM AUTHOR]

• Proposition of a reactive scheduling approach for combating uncertainty in renewable energysources • Hierarchical decomposition of the scheduling and control tasks • Control based integration of uncertain renewable energy sources with deterministic energygeneration and storage devices • Reactive feedback in an MLD based rolling horizon framework for integration of multiple powersources • Demonstration of the proposed approach on several scenarios in energy uncertainty, with andwithout reactive feedback The main technological barrier in relying solely on renewable energy resources is that the sources such as wind and solar are highly intermittent in availability and result in uncertainty in demand satisfaction. This paper focuses on the integration of these uncertain renewable energy sources along with relatively deterministic energy sources such as reformer based fuel cell and battery. The power mix scenario between these multiple renewable energy sources along with the reformer based fuel cell system, coupled with an energy storage option is envisaged in this paper to ensure undisrupted power supply, to combat the possible intermittent nature of these renewable sources. An appropriate scheduling layer which provides a detailed plan of the optimum contribution of the various available power sources is considered over one week (7 days) duration. A model predictive control (MPC) scheme is deployed at the lower level control layer that receives a measurement of the possible fluctuations or uncertainties in the renewable power sources and maintains a smooth operation of the power generation system through appropriate decisions on generation via the reformer based fuel cell or by exploiting the battery storage, to ensure a delay-free delivery of power to the external load. During real-time operation of the plant, due to the uncertainties in the contribution from solar and wind sources, the power demanded from the fuel cell and the battery is varied accordingly by the MPC layer to meet the overall power demand. The performance of the designed MPC to maintain a smooth delivery of power in both the absence and presence of uncertainties in the renewable energy sources, with and without a reactive feedback between the scheduling and control layers, is illustrated using case studies. [ABSTRACT FROM AUTHOR]

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control. • Built an LSTM-based MPC for energy management on oil-cooled motors in HEV. • Constructed an empirical model for oil-cooled motors. • Proposed a novel metaheuristic algorithm for solving optimization problems in MPC. • The time cost of the algorithm is the lowest among metaheuristic algorithms. • The algorithm showed minimal correlation between runtime and operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a transfer learning (TL)-based control-oriented model development framework. In particular, this study examines the transferability from virtual (source) to existing (target) buildings to overcome the data imbalance issue of the data-driven approach. The target system is a cooling system comprising two supply air fans and four condensing units. First, synthetic data rich enough to provide fundamental knowledge about the target system were generated using the EnergyPlus model. A data-driven model was subsequently developed to learn the underlying dynamics of the system. By adopting TL using an imbalanced dataset measured from the target system, the knowledge that the model learned from the virtual data was transferred to the target system of the existing building. The results showed that the transfer learning model could accurately describe the dynamic behavior of the target system and predict the supply air temperature with marginal errors (CVRMSE: 5.4%, MAE: 0.96 ℃). In other words, the TL from virtual to existing buildings can overcome the data imbalance issue for developing a reliable data-driven model. [ABSTRACT FROM AUTHOR]

In the transition from fossil fuel to electrified heating, a concerning trend is emerging in certain regions of the US. Owners of buildings with gas-based systems leave them in place after adding heat pumps (HPs). Existing control solutions for these hybrid (dual fuel) systems are rudimentary and fall short of realizing the full carbon reduction potential of these systems. Model predictive control (MPC) is often regarded as the benchmark for achieving optimal control in integrated systems. However, in the case of small-medium commercial buildings (SMCBs), the control and communication infrastructure required to facilitate the implementation of such advanced controls is often lacking. This paper presents a field implementation of easy-to-deploy MPC for a dual fuel heating system consisting of HPs and a gas-fired furnace (GF) for SMCBs. The control system is deployed on an open-source middleware platform and utilizes low-cost sensor devices to be used for real SMCBs without major retrofits. We demonstrated this MPC in a real office building with 5 HPs and 1 GF for 2 months. The test results showed that MPC reduced 27% of cost while completely eliminating GF usage by shifting 23% of the thermal load from occupied-peak time to non-occupied-non-peak times. • Implementation of a practical MPC for a heat pump-based dual fuel system in a small commercial building. • Experimental demonstration of a practical MPC with low-cost sensor retrofits for 2 winter months. • Achievement of 27% energy cost saving and 23% load shifting from occupied-peak time to non-occupied-non-peak time. • Elimination of natural gas usage with GHG emissions reduction of ∼ 52 kgCO2/month. [ABSTRACT FROM AUTHOR]

[Display omitted] • MPC method can be applied to the automatic control of cooldown process which has strong non-linear and large hysteresis characteristics. • Fast calculation for the automation of superconducting cavity cooldown process using ANN were obtained. • Experiment test results demonstrated the improved automatic cooldown method saved 40% more time and saved energy than the original manual control method. Superconducting cavity is the key equipment of the superconducting accelerator, which provides higher acceleration voltage and higher frequency power per unit length, and saves equipment space. Superconducting cavities need to be gradually cooled from ambient temperature (300 K) to the superconducting temperature (4.2 K or below) during the test and operation. The temperature difference on the cavity must be strictly limited during the cooldown process to prevent excessive thermal stress on the surface of the superconducting cavity. Since this cooldown process for the superconducting cavity is a typical large hysteresis, non-linear process that is difficult to control automatically using decoupled proportion integral derivative (PID) methods directly, a less efficient manual control scheme is normally adopted. In this paper, 3D numerical simulation, 1D pipe and 0D tank model with artificial neural network (ANN) were combined to generate a two-layer surrogate model that can balance computational accuracy and speed, to improve the automation and cooling efficiency of the superconducting cavity cooldown process. In order to achieve automatic control of the cooling procedure for the superconducting cavity, a model predictive control (MPC) approach was also built on the basis of this two-layer surrogate model. According to the results of the experiment test, the improved method could realize a quick and smooth cooldown process of the superconducting cavity, during which the temperature difference on the cavity could satisfy the requirements. Additionally, the improved automatic cooldown method was more adaptable and saved 29 % more time than the original manual control method. The foundation for a more intelligent automated control of future large cryogenic systems or other system with the large hysteresis, non-linear properties, was laid. [ABSTRACT FROM AUTHOR]

• An optimal active power control scheme is proposed for DFIG wind farms with distributed energy storage systems. • Distributed ESSs help wind farms better follow the active power set-point from the system operator. • Fatigue loads of wind turbines are minimized. • The state of charge of ESSs is kept close to the middle operating point to have better regulation capability. An optimal active power control scheme based on model predictive control (MPC) is proposed for a doubly-fed induction generator (DFIG)-based wind farm equipped with distributed energy storage systems (ESSs). A two-stage optimal control scheme is proposed. In the first stage, the power reference of each WT and the total power command of ESSs are generated, aiming to reduce the fatigue load of WTs by minimizing variations of thrust force and shaft torque. In the second stage, the charge/discharge power of ESSs are optimized to achieve the fair power sharing and maximize the capacity margin. An MPC based optimization problem is formulated for the constrained multiple input and multiple output (MIMO) wind farm system. The dynamics of converters and WTs are taken into account by the MPC. With the proposed control scheme, the active power references are optimized between WTs and ESSs according to their local wind conditions. Fatigue loads of WTs are reduced efficiently by coordinating the DFIG-based WTs and distributed ESSs. A wind farm with 10 DFIG-based WTs was used to validate the control performance of the proposed optimal active power control scheme. [ABSTRACT FROM AUTHOR]

• A bi-level active and reactive power control based on the consensus and MPC is proposed for the WFC. • The upper level is to optimally distribute active power among wind farms and maintain voltage at the POC. • The fair active and reactive power distribution among wind farms is realized using the consensus protocol. • The lower level is to optimally control active and reactive power within the wind farm based on MPC. This paper proposes a bi-level decentralized active and reactive power control (DARPC) for the large-scale wind farm cluster (WFC) composed of several wind farms. The WFC tracks the active power reference from the transmission system operator (TSO) while controlling the bus voltage of the point of connection (POC), and maintaining the wind turbine (WT) terminal voltages stable in each wind farm. In the upper level, a distributed active and reactive power control scheme based on the consensus protocol is designed for the WFC, which can achieve fair active and reactive power sharing among multiple wind farms, and generates active and reactive power references for each wind farm. In the lower level, a centralized control scheme based on Model Predictive Control (MPC) is proposed, which can effectively regulates active and reactive power outputs of all WTs within the wind farm. The proposed centralized control scheme can maintain WTs terminal voltage close to the rated voltage while tracking the power reference from the upper level control. The DARPC can effectively reduce the computation burden of the WFC controller by distributing the computation and monitoring tasks to several wind farm controllers. Moreover, the communication cost is reduced. A WFC with 8 wind farms and totally 128 WTs was used to validate the proposed DARPC scheme. [ABSTRACT FROM AUTHOR]

This paper examines the prediction of disturbances based on their past measurements using k -nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model. [ABSTRACT FROM AUTHOR]

• Propose priority-based infrastructure and control aware user heterogeneity, i.e. value-of-time. • Develop a joint optimization framework for traffic signal control and priority-based pricing. • Utilize connected vehicles to inform traffic flow and value-of-time information. • Test the proposed joint framework in real-city settings that resemble Zurich network. • Demonstrate traffic performance can be significantly improved while equitable savings achieved. Real-time control of large-scale urban networks has been attracting significant research attention. This paper, using the information provided by connected vehicles, proposes a novel control approach based on the concept of perimeter control to maximize the social welfare of all vehicles. The contributions of this paper are threefold. First, we consider vehicle heterogeneity (i.e. corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.) and integrate a priority scheme into perimeter control to improve both the traffic performance and the social welfare. This is achieved by installing priority lanes at some of the perimeter intersections. Unlike the existing research works that provide priority to certain traffic modes, we dynamically identify the groups of vehicles that we should prioritize, in order to maximize the social welfare. Second, we develop a model predictive control approach to simultaneously optimize the toll for using the priority lanes and the traffic signal timings at the perimeter intersections. This approach can explicitly handle the constraint of the storage capacity of each intersection link. Third, we propose a recursive estimation algorithm to update our knowledge on the distribution of the value of times (VOTs), using the lane choice information of connected vehicles. The proposed approach is tested in a simulated network which resembles the main features of the city center of Zurich, Switzerland. By using the proposed strategy, the traffic accumulation inside the network is still stabilized, and the monetary costs due to delay are significantly reduced at the entire network (up to 25.8%) compared to the strategy without priority. The distribution of the combined cost (including cost due to delay and tolls) is more uniform across VOT groups than that resulting from the strategy without priority. It is also shown that the proposed recursive estimation algorithm quickly converges and further improves the social welfare. [ABSTRACT FROM AUTHOR]

• Basic/advanced process control strategies for CO 2 removal via absorption/stripping. • Plant operability via proper control of overall water balance and fluid inventories. • Plant performance via CO 2 capture rate, reboiler temperature, energy performance. • Advanced MPC to account for constraints on the controlled and manipulated variables. CO 2 removal via absorption/stripping system using chemical solvents is a widely acknowledged technology for CO 2 capture, either from natural gas or post-combustion processes. It offers higher capture efficiency. However, one of its main drawbacks is the high energy consumption in the regeneration step. Besides, for solvent-based absorption/stripping plant, the units feature nonlinearities as well as high process interactions. Hence, control strategies are crucial in the operational optimization of process set-point changes and disturbance rejections as well as reduction in the operational costs of such systems. Process control systems are key in processing plants as they direct production processes, minimise variations and regulate product consistency. In this paper, an overview on the related efforts that have been carried out in terms of basic and advanced process control strategies are reviewed to provide further understanding on the key features that are required to optimize the operation of the absorption/stripping system. [ABSTRACT FROM AUTHOR]

• A framework to automatically detect stiction using only the process variable and controller output while compensating for stiction with the MPC. • The proposed IAM has 65% accuracy for 17 industrial loops with stiction, at par with currently available methods. • The dual-mode MPC manages to eliminate oscillation caused by stiction and does not result in chattering. Due to its continuous motion, control valve performance tends to deteriorate over time due to the presence of static-friction or also known as stiction. This, in turn, leads to high variability in product quality and an increased frequency of valve maintenance. Model Predictive Control (MPC) based stiction compensation methods can remove oscillations caused by stiction but it assumes that stiction is known a –priori to exist in the related loops. To overcome this limitation, an integrated framework is proposed to automate the detection of stiction using only process variable and controller output while compensating for stiction with MPC. The detection algorithm, which was validated using industrial data, uses an adaptive neuro-fuzzy inference system (ANFIS). Out of the 78 benchmark industrial loops tested, the proposed Intuitive ANFIS-based Method (IAM) has a detection accuracy of 65%, placing it on par with the best of the currently available methods reported in the literature for loops with stiction. Within the proposed framework, the detection component only activates the MPC-based compensation when needed. In a simulation of a multivariable process, it is demonstrated that the dual-mode MPC manages to eliminate oscillation caused by stiction with no chattering. This results in better overall performance when controlling a loop throughout the service life of the valve. [ABSTRACT FROM AUTHOR]