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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.

The study of approximate matching in the Massively Parallel Computations (MPC) model has recently seen a burst of breakthroughs. Despite this progress, we still have a limited understanding of maximal matching which is one of the central problems of parallel and distributed computing. All known MPC algorithms for maximalmatching either take polylogarithmic time which is considered inefficient, or require a strictly superlinear space of n1+Ω(1) per machine. In this work, we close this gap by providing a novel analysis of an extremely simple algorithm, which is a variant of an algorithm conjectured to work by Czumaj, Lacki, Madry, Mitrovic, Onak, and Sankowski [15]. The algorithm edge-samples the graph, randomly partitions the vertices, and finds a random greedy maximal matching within each partition.We show that this algorithm drastically reduces the vertex degrees. This, among other results, leads to an O(log log Δ) round algorithm for maximal matching with O(n) space (or even mildly sublinear in n using standard techniques). As an immediate corollary, we get a 2 approximate minimum vertex cover in essentially the same rounds and space, which is the optimal approximation factor under standard assumptions. We also get an improved O(log log Δ) round algorithm for 1 + ε approximate matching. All these results can also be implemented in the congested clique model in the same number of rounds. [ABSTRACT FROM AUTHOR]

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range. [ABSTRACT FROM AUTHOR]

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella's operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC's decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments. [ABSTRACT FROM AUTHOR]

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources. [ABSTRACT FROM AUTHOR]

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved. [ABSTRACT FROM AUTHOR]

In the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. To achieve this, an effective load-frequency control (LFC) system, which serves as the secondary control, must be implemented. However, the presence of renewable energy sources such as wind turbines and photovoltaic systems adds complexity to the operation of the LFC system due to their inherent uncertainty. To enhance the performance of the LFC system in the two-area microgrid, this paper proposes a reduction in the number of controllers employed, aiming for a less complex structure. Specifically, Model Predictive Control (MPC) is utilized for LFC, and the weight parameters of the MPC controller are determined using Craziness-based Particle Swarm Optimization (CRPSO). The proposed method is compared with alternative approaches, including PID controller optimized with Social Spider Optimization (SSO), Fractional Order Fuzzy PI (FOFPI), and conventional MPC. The effectiveness of the proposed method is evaluated in various scenarios, considering load variations and the presence of distributed microgrid generation resources. The results demonstrate that the proposed method outperforms the other controllers in terms of speed of response, reduction of overshoot and undershoot, and overall complexity. Importantly, the proposed method significantly improves the frequency stability of the two-area microgrid. The simulation and analysis are conducted using MATLAB software, providing a comprehensive understanding of the system dynamics and the performance of the proposed controller.

In PMSM motor drive systems, control accuracy and resistance have always been the most important challenges for researchers. This paper addresses these two challenges and presents a control structure that makes the system performance robust against model uncertainties of system disturbances as well as sudden load changes. In other words, in this paper, an improved MPC controller has been presented, in which model uncertainties and possible disturbances on the system are considered during design. In addition, in order to apply the physical limitations of the electric motor during the design of this improved controller, the input limitations of the system have also been considered. Therefore, the desired MPC controller is a resistant and constrained MPC, it is resistant to uncertainties and disturbances. In addition, in order to further strengthen the control loop against sudden load changes, a sliding estimator has also been designed to estimate the load torque. This estimator itself is also resistant to the uncertainties of the model, which makes the entire control structure have a very robust performance. The performance of the method presented in this article has been compared with a conventional sliding method during a series of practical tests. The results showed that the proposed method has a higher accuracy of 3 rpm and a shorter convergence time of 2 seconds.

Abstract In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types.

Introduction: Traditional adaptive cruise control systems ignore the impact of the driver's intentions and driving behavior on system performance. Methods: In response to this issue, this study designs a new adaptive cruise control system by combining personalized driving style recognition, dynamic distance control, prospective energy management, and a model predictive control framework that integrates long short-term memory neural networks and ensemble learning. Results: It was verified that the accuracy of the algorithm was 96.2%. In addition, experts had average ratings of 95, 96, and 98 for the economy, safety, and comfort of the system, respectively. Discussion: This model is expected to achieve comprehensive performance optimization and improvement of EREV in complex driving environments, injecting new vitality and power into the intelligent development of electric vehicles. [ABSTRACT FROM AUTHOR]

In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types. [ABSTRACT FROM AUTHOR]

Despite the urgent need for new antibiotics, very few innovative antibiotics have recently entered clinics or clinical trials. To provide a constant supply of new drug candidates optimized in terms of their potential to select for resistance in natural settings, in vitro resistance-predicting studies need to be improved and scaled up. In this review, the following in vitro parameters are presented: frequency of spontaneous mutant selection (FSMS), mutant prevention concentration (MPC), dominant mutant prevention concentration (MPC-D), inferior-mutant prevention concentration (MPC-F), and minimal selective concentration (MSC). The utility of various adaptive laboratory evolution (ALE) approaches (serial transfer, continuous culture, and evolution in spatiotemporal microenvironments) for comparing hits in terms of the level and time required for multistep resistance to emerge is discussed. We also consider how the hit-to-lead stage can benefit from high-throughput ALE setups based on robotic workstations, do-it-yourself (DIY) continuous cultivation systems, microbial evolution and growth arena (MEGA) plates, soft agar gradient evolution (SAGE) plates, microfluidic chips, or microdroplet technology. Finally, approaches for evaluating the fitness of in vitro-generated resistant mutants are presented. This review aims to draw attention to newly emerged ideas on how to improve the in vitro forecasting of the potential of compounds to select for resistance in natural settings. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process. [ABSTRACT FROM AUTHOR]

In this work we introduce PERK a compact digital signature scheme based on the hardness of a new variant of the permuted kernel problem (PKP). PERK achieves the smallest signature sizes for any PKP-based scheme for NIST category I security with 6 kB, while obtaining competitive signing and verification timings. PERK also compares well with the general state-of-the-art. To substantiate those claims we provide an optimized constant-time AVX2 implementation, a detailed performance analysis and different size-performance trade-offs. Technically our scheme is based on a Zero-Knowledge Proof of Knowledge following the MPC-in-the-Head paradigm and employing the Fiat–Shamir transform. We provide comprehensive security proofs, ensuring EUF-CMA security for PERK in the random oracle model. The efficiency of PERK greatly stems from our particular choice of PKP variant which allows for an application of the challenge-space amplification technique due to Bidoux–Gaborit (C2SI 2023). Our second main contribution is an in-depth study of the hardness of the introduced problem variant. First, we establish a link between the hardness of our problem variant and the hardness of standard PKP. Then, we initiate an in-depth study of the concrete complexity to solve our variant. We present a novel algorithm which outperforms previous approaches for certain parameter regimes. However, the proximity of our problem variant to the standard variant can be controlled via a specific parameter. This enables us to effectively safeguard against our new attack and potential future extensions by a choice of parameters that ensures only a slight variation from standard PKP. [ABSTRACT FROM AUTHOR]

Ensuring efficiency and safety is critical when developing charging strategies for lithium-ion batteries. This paper introduces a novel method to optimize fast charging for cylindrical Li-ion NMC 3Ah cells, enhancing both their charging efficiency and thermal safety. Using Model Predictive Control (MPC), this study presents a cost function that estimates the thermal safety boundary of Li-ion batteries, emphasizing the relationship between the temperature gradient and the state of charge (SoC) at different temperatures. The charging control framework combines an equivalent circuit model (ECM) with minimal electro-thermal equations to estimate battery state and temperature. Optimization results indicate that at ambient temperatures, the optimal charging allows the cell's temperature to self-regulate within a safe operating range, requiring only one additional minute to reach 80% SoC compared to a typical fast-charging protocol (high current profile). Validation through numerical simulations and real experimental data from an NMC 3Ah cylindrical cell demonstrates that the simple approach adheres to the battery's electrical and thermal limitations during the charging process. [ABSTRACT FROM AUTHOR]

Introduction: Actinobacillus pleuropneumoniae (APP) is a serious pathogen that affects the development of livestock breeding. Due to excessive use of antimicrobial drugs, many multidrug-resistant bacteria have emerged and spread, which have threatened the livestock industry. Therefore, we established a peristaltic pump infection model (PPIM) to evaluate the susceptibility change and pharmacokinetic/pharmacodynamic (PK/PD) integration of tulathromycin against APP during the mutant selection window (MSW) for preventing the emergence of mutant-resistant bacteria. Methods: The 99% minimum inhibitory concentration (MIC99) and mutant prevention concentration (MPC) of tulathromycin against APP were measured using the agar-plate method. After the model of dynamic infection had been established based on tulathromycin data in lungs, different dosages were administered to make the drug concentrations located in different parts of the MSW. The population and sensitivity of APP were monitored. Tulathromycin concentrations were measured by high-performance liquid chromatographytandem mass spectrometry. Finally, a sigmoid Emax model was used to analyze the relationships between PK/PD parameters and antibacterial effects. Results and discussion: The values of MIC, MIC99, and MPC of tulathromycin against APP were 2, 1.4, and 44.8 µg/mL, respectively. The PPIM was stable. An elimination effect without regrowth was observed at 5.6 to 44.8 µg/mL (-4.48 to -7.05 Log10 CFU/mL, respectively). The MIC of APP increased 32-fold at 8 MIC99. AUC168 h/MIC99 had the best fit with the antibacterial effect (R2 = 0.9867). The AUC168 h/MIC99 required to achieve bacteriostatic, bactericidal, and clearance effects were 1.80, 87.42, and 198 h, respectively. Our results could provide guidance for the clinical application of tulathromycin to treat APP infection and avoid the generation of drug-resistant bacteria. [ABSTRACT FROM AUTHOR]

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPCintegrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs. [ABSTRACT FROM AUTHOR]

This paper studies the consensus problem of Multi-Agent Systems (MASs) with input-delay using the Model Predictive Control (MPC) approach. Due to challenges such as input delay and communication graph, the traditional MPC is not efficient for the considered systems. In this regard, a novel MPC is developed for MASs with input delay. The main advantage of this paper is to design distributed controllers based on minimizing given cost functions which result in the improvement of the transient responses. For this purpose, first, distributed observers are derived to estimate the leader's states, perfectly. Then, distributed controllers are achieved through the MPC approach. Finally, numerical and practical examples are simulated to affirm the efficiency and applicability of the presented scheme. [ABSTRACT FROM AUTHOR]

The rising electricity costs, cost of space heating, and domestic hot water end up driving consumers toward reducing expenses by generating their electricity through devices like photovoltaic systems and efficient combined heat and power plants. When coupled with thermal systems via an energy management system (EMS) in a Multi-Energy System (MES), this self-produced electricity can effectively lower electricity and heating bills. However, MESs with EMSs can serve various purposes beyond cost reduction via self-consumption, such as reacting to variable electricity prices, meeting special grid connection conditions, or minimizing CO2 emissions. These diverse strategies create unique prosumer profiles, deviating significantly from standard load profiles. The potential threat to the power grid arises as grid operators lack visibility into which consumers employ which control strategies. This paper investigates the impact of controlled MESs on the power grid compared to average households and answers whether new control strategies affect the planning strategies of low voltage grids. It proposes a comprehensive four-step toolchain for the detailed simulation of thermal–electrical load profiles, MES control strategies, and grid dynamics. It includes a new method for the grid impact analysis of extreme and average bulk values. As a result, this study identifies three primary factors influencing distribution power grids by MESs. Firstly, the presence and scale of photovoltaic (PV) systems significantly affect extreme values in the grid. Secondly, MESs incorporating combined heat and power (CHP) and heat pump (HP) units impact the overall grid performance, mainly reflected in bulk values. Thirdly, the placement of an MES with heating systems, especially when concentrated in one feeder, plays a crucial role in grid dynamics. Despite the three distinct factors identified as impactful on the power grid, this study reveals that the various control strategies, despite leading to vastly different grid profiles, do not exhibit divergent impacts on buses, lines, or transformers. Remarkably, the impact of MESs remains consistently similar across the range of control strategies studied. Therefore, different control strategies do not pose an additional challenge to the grid integration of MESs. [ABSTRACT FROM AUTHOR]

Ethereum, one of the most prominent and widely deployed blockchain systems, is undergoing a significant upgrade that adopts sharding for capacity expansion and secure multi-party computation (MPC) to enable distributed validator technology (DVT). However, it faces a data availability problem, where an adversary can cheat honest-but-lazy validators to propagate invalid blocks, thereby exposing Ethereum Sharding to vulnerabilities. To address this issue, proof of custody (PoC) was proposed. Regrettably, no practically effective MPC protocol is designed to securely and distributively compute PoC. In this paper, we present a concrete and efficient MPC protocol that enables the secure computation of PoC in the dishonest-majority malicious setting. First, we construct an efficient conversion protocol to convert secret sharings in two different prime fields using the doubly authenticated bits (daBits) technique. Second, an efficient MPC protocol is designed to compute scalar multiplication over an elliptic-curve group without requiring MPC to compute branching programs. Furthermore, we employ affine coordinates to compute group operations over an elliptic curve, leading to significant performance boosts compared to other coordinate systems. Finally, a concrete end-to-end implementation of the protocol is built, and its performance is evaluated. When the operations of a validator are collaboratively performed by seven parties, the online (resp., total) running time to generate one proof of custody is 0.02 s (resp., 559.6 s) in LAN and 0.39 s (resp., 7904.2 s) in WAN. The results demonstrate the practicality of the proposed protocol for Ethereum Sharding over a long period of time, such as 73 days. [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]

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain. [ABSTRACT FROM AUTHOR]

This article explores novel in-orbit drag-free technology that can be utilized for deep space detection scientific missions. In this study, we considered a two-test-mass drag-free method and analyzed the design of the drag-free and attitude control system for the TianQin mission. The entire control system was comprehensively designed, including an actuator allocation design and controllers for two test masses and one spacecraft, with a total of 18 degrees of freedom. Furthermore, stability analysis was conducted. Based on our design, numerical analysis and simulations were performed assuming geocentric orbit conditions in the TianQin mission, confirming the feasibility of this aerospace engineering concept. The versatility of the design allows for its application to scientific observations across various disciplines by modifying the structure of the simulation environment, and consequently, the approach discussed in this study holds significant practical implications for effectively accomplishing deep space observation tasks. [ABSTRACT FROM AUTHOR]

This paper is based on the work presented at EVS36 in Sacramento. The core of the work deals with the cabin climate control of battery electric vehicles (BEV) using model predictive control (MPC) approaches. These aim to reduce the energy demand for cabin air conditioning while maintaining comfort and air quality. The first step briefly overviews model predictive control approaches and the respective fundamentals. Afterward, the modeling for the system dynamics is explained. The challenge for the system model considering humid air is discussed, and the first implementation method is presented. With the added equations for the air quality and humidity, a logic to prevent window fogging was developed to improve safety. Ultimately, model-in-the-loop (MiL) investigations identified an energy-saving potential of up to 15.4% for cold and 39.7% for hot conditions compared to a rule-based strategy. In addition, the investigations carried out showed that it was also possible to improve indoor comfort by specifically influencing the air quality and humidity. Together with the safety criteria introduced to prevent window fogging, it was possible to present a strategy that can significantly improve thermal management for the cabin in modern BEVs. [ABSTRACT FROM AUTHOR]

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is characterized by an accumulation of pathologic amyloid-beta (Aβ) and Tau proteins, neuroinflammation, metabolic changes and neuronal death. Reactive astrocytes participate in these pathophysiological processes by releasing pro-inflammatory molecules and recruiting the immune system, which further reinforces inflammation and contributes to neuronal death. Besides these neurotoxic effects, astrocytes can protect neurons by providing them with high amounts of lactate as energy fuel. Astrocytes rely on aerobic glycolysis to generate lactate by reducing pyruvate, the end product of glycolysis, through lactate dehydrogenase. Consequently, limited amounts of pyruvate enter astrocytic mitochondria through the Mitochondrial Pyruvate Carrier (MPC) to be oxidized. The MPC is a heterodimer composed of two subunits MPC1 and MPC2, the function of which in astrocytes has been poorly investigated. Here, we analyzed the role of the MPC in the pathogeny of AD, knowing that a reduction in overall glucose metabolism has been associated with a drop in cognitive performances and an accumulation of Aβ and Tau. We generated 3xTgAD mice in which MPC1 was knocked-out in astrocytes specifically and focused our study on the biochemical hallmarks of the disease, mainly Aβ and neurofibrillary tangle production. We show that inhibition of the MPC before the onset of the disease significantly reduces the quantity of Aβ and Tau aggregates in the brain of 3xTgAD mice, suggesting that acting on astrocytic glucose metabolism early on could hinder the progression of the disease.

Abstract Podocytes are specialized terminally differentiated cells in the glomerulus that are the primary target cells in many glomerular diseases. However, the current podocyte cell lines suffer from prolonged in vitro differentiation and limited survival time, which impede research progress. Therefore, it is necessary to establish a cell line that exhibits superior performance and characteristics. We propose a simple protocol to obtain an immortalized mouse podocyte cell (MPC) line from suckling mouse kidneys. Primary podocytes were cultured in vitro and infected with the SV40 tsA58 gene to obtain immortalized MPCs. The podocytes were characterized using Western blotting and quantitative real-time PCR. Podocyte injury was examined using the Cell Counting Kit-8 assay and flow cytometry. First, we successfully isolated an MPC line and identified 39 °C as the optimal differentiation temperature. Compared to undifferentiated MPCs, the expression of WT1 and synaptopodin was upregulated in differentiated MPCs. Second, the MPCs ceased proliferating at a nonpermissive temperature after day 4, and podocyte-specific proteins were expressed normally after at least 15 passages. Finally, podocyte injury models were induced to simulate podocyte injury in vitro. In summary, we provide a simple and popularized protocol to establish a conditionally immortalized MPC, which is a powerful tool for the study of podocytes.

[Introduction] In order to reduce frequency disturbance caused by photovoltaic (PV) generation to power grid and further improve the frequency modulation effect, an optimization strategy for collaborative frequency modulation of PVs-ESs based on MPC (Model Predictive Control) is proposed. The basic principles, control flow, constraints, target function and weight coefficient of the strategy are analyzed. [Method] A model of optical-storage network based on MPC was constructed and the nonlinear state space equation was derived. In order to verify the frequency modulation effect of this strategy, four different simulation environments were set up: no energy storage, battery storage, mixed energy storage and the strategy proposed in this paper. [Result] Simulation results show that compared with other scenarios, the proposed strategy is optimal in terms of frequency modulation and the worst in the absence of energy storage. In addition, the hybrid energy storage method is superior to the battery energy storage method. [Conclusion] The effectiveness of the proposed strategy is validated on the MATLLAB/Simulink platform. In photovoltaic power generation system, optimal control of energy storage and PV maximum power point tracking can make the grid frequency more stable and improve the stability of the whole system. The results of this study can be used as a reference for PV connection.

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

Podocytes are specialized terminally differentiated cells in the glomerulus that are the primary target cells in many glomerular diseases. However, the current podocyte cell lines suffer from prolonged in vitro differentiation and limited survival time, which impede research progress. Therefore, it is necessary to establish a cell line that exhibits superior performance and characteristics. We propose a simple protocol to obtain an immortalized mouse podocyte cell (MPC) line from suckling mouse kidneys. Primary podocytes were cultured in vitro and infected with the SV40 tsA58 gene to obtain immortalized MPCs. The podocytes were characterized using Western blotting and quantitative real-time PCR. Podocyte injury was examined using the Cell Counting Kit-8 assay and flow cytometry. First, we successfully isolated an MPC line and identified 39 °C as the optimal differentiation temperature. Compared to undifferentiated MPCs, the expression of WT1 and synaptopodin was upregulated in differentiated MPCs. Second, the MPCs ceased proliferating at a nonpermissive temperature after day 4, and podocyte-specific proteins were expressed normally after at least 15 passages. Finally, podocyte injury models were induced to simulate podocyte injury in vitro. In summary, we provide a simple and popularized protocol to establish a conditionally immortalized MPC, which is a powerful tool for the study of podocytes. [ABSTRACT FROM AUTHOR]

In recent years, the pulse-width-modulation (PWM) converter has been found to have extensive applications in renewable energy, industrial fields, and others. The high efficiency requirement is crucial to operating a PWM rectifier in various applications, in addition to the fundamental control objectives of sinusoidal grid currents and the correct DC bus voltage. Additionally, in practical application, another issue arises when the grid voltage frequently experiences distortion, leading to a distorted grid current and a significant rise in total harmonic distortion (THD). To resolve these problems, a model predictive virtual flux-based direct power control (MPVFDPC) with improved power loss performance is proposed based on an integrated switching state predetermination strategy. The proposed MPVFDPC for PWM rectifier inherits the merits of both virtual flux control and direct power control, which have fast dynamic performance and the grid current THD is considerably decreased under distorted grid voltage states. The proposed technique aims to minimize switching loss under ideal and distorted grid voltage states by exploiting the discontinuous modulation concept by using a switching state predetermination strategy. The MPVFDPC with switching state predetermination strategy is proven by employing it in experiments as well as simulations in comparison with previous models: predictive direct power control (Conv. MPDPC) and conventional MPVFDPC (Conv. MPVFDPC). The acquired waveforms and quantitative data are employed to prove the effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

Bipedal walking robots are advancing research by performing challenging human-like movements in complex environments. Particularly, wheeled-bipedal robots are used in many indoor environments by overcoming the speed and maneuverability limitations of bipedal walking robots without wheels. However, when both wheels lose contact with the ground, maintaining lateral balance becomes challenging, and there is an increased risk of toppling over. Furthermore, utilizing robotic arms similar to human arms, in addition to wheel-based balance, could enable more precise and stable control. In this paper, we develop a wheeled-bipedal robot that is able to jump and drive while also being able to cross its legs and balance on one leg (the OLEBOT). The OLEBOT is designed with a hand-fan-shaped end-effector capable of generating compensatory torque. By tilting the hand-fan-shaped end-effector in the opposite direction, OLEBOT achieves pitch control and single-leg balance. In jumping scenario, it imitates the arm movements of a person performing stationary high jumps, while utilizing a cam-based leg joint system to boost jump height. In addition, this paper develops a control architecture based on model predictive control (MPC) to ensure stable posture in driving, jumping, and one-legged balancing scenarios for OLEBOT. Finally, the experimental results demonstrate that OLEBOT is capable of maintaining a stable posture using a wheeled-bipedal system and achieving balance in a one-legged stance. [ABSTRACT FROM AUTHOR]

Pradofloxacin is the newest of the veterinary fluoroquinolones to be approved for use in animals—initially companion animals and most recently food animals. It has a broad spectrum of in vitro activity, working actively against Gram-positive/negative, atypical and some anaerobic microorganisms. It simultaneously targets DNA gyrase (topoisomerase type II) and topoisomerase type IV, suggesting a lower propensity to select for antimicrobial resistance. The purpose of this study was to determine the rate and extent of bacterial killing by pradofloxacin against bovine strains of Mannheimia haemolytica and Pasteurella multocida, in comparison with several other agents (ceftiofur, enrofloxacin, florfenicol, marbofloxacin, tildipirosin, tilmicosin and tulathromycin) using four clinically relevant drug concentrations: minimum inhibitory and mutant prevention drug concentration, maximum serum and maximum tissue drug concentrations. At the maximum serum and tissue drug concentrations, pradofloxacin killed 99.99% of M. haemolytica cells following 5 min of drug exposure (versus growth to 76% kill rate for the other agents) and 94.1–98.6% of P. multocida following 60–120 min of drug exposure (versus growth to 98.6% kill rate for the other agents). Statistically significant differences in kill rates were seen between the various drugs tested depending on drug concentration and time of sampling after drug exposure. [ABSTRACT FROM AUTHOR]

The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability. [ABSTRACT FROM AUTHOR]

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC's effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments' leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration. [ABSTRACT FROM AUTHOR]

Introduction: Virtual Synchronous Generators (VSGs) are used in Voltage Source Converter-based Multi-Terminal High-Voltage Direct Current (VSC-MTDC) systems to enhance power system stability. However, the MTDC framework can lead to instability due to reduced inertia in certain grid areas, especially during load switching at VSC stations. This instability is exacerbated by untimely adjustments of the VSG's power setpoint, leading to voltage and frequency oscillations. Methods: This study introduces a cooperative control approach for the DC voltage of the VSG, employing a consensus algorithm and Model Predictive Control (MPC). This method aims to achieve incremental power for the VSG and provide interactive power commands for both the grid side and the wind farm side. The consensus algorithm ensures coherent system adjustments, while the MPC algorithm tracks DC-side voltage changes in real time. Results: The application of this cooperative control approach significantly enhances DC voltage regulation performance. It effectively reduces the extent of frequency drops and mitigates secondary frequency drop (SFD) issues, particularly those arising from the use of wind farms for frequency regulation and the associated speed recovery in wind turbine units. Discussion: The increase in supplemental power effectively utilizes the energy stored in DC-side capacitors for power balance regulation and introduces additional inertial power into the system. Electromagnetic transient simulations have confirmed the effectiveness of the Consensus MPC-VSG method, demonstrating its ability to optimize the dynamic performance of VSC-MTDC systems and promote stability in DC voltage and frequency. Conclusion: The findings suggest that employing the Consensus MPC-VSG method offers a promising solution for enhancing the stability and operational efficiency of VSC-MTDC systems, addressing the challenges posed by the inherent segmentation of the grid and the integration of renewable energy sources like wind farms. [ABSTRACT FROM AUTHOR]

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In this study, groundbreaking software has been developed to automate the generation of equations of motion for manipulator robots with varying configurations and degrees of freedom (DoF). The implementation of three algorithms rooted in the Lagrange–Euler (L-E) formulation is achieved through the utilization of.m files in MATLAB R2020a software.This results in the derivation of a symbolic dynamic model for industrial manipulator robots. To comprehend the unique features and advantages of the developed software, dynamic simulations are conducted for two 6- and 9-DoF redundant manipulator robots as well as for a 3-DoF non-redundant manipulator robot equipped with prismatic and rotational joints, which is used to simplify the dynamic equations of the redundant prototypes. Notably, for the 6-DoF manipulator robot, model predictive control (MPC) is employed using insights gained from the dynamic model. This enables optimal control by predicting the future evolution of state variables: specifically, the values of the robot's joint variables. The software is executed to model the dynamics of different types of robots, and the CPU time for a MacBook Pro with a 3 GHz Dual-Core Intel Core i7 processor is less than a minute. Ultimately, the theoretical findings are validated through response graphs and performance indicators of the MPC, affirming the accurate functionality of the developed software. The significance of this work lies in the automation of motion equation generation for manipulator robots, paving the way for enhanced control strategies and facilitating advancements in the field of robotics. [ABSTRACT FROM AUTHOR]

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines. [ABSTRACT FROM AUTHOR]

Doubly fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional model predictive controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. To validate the proposed control approach, the results are compared with PID based controller in MATLAB/Simulink. The simulation results showed that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input. [ABSTRACT FROM AUTHOR]

The objective of energy transition is to convert the worldwide energy sector from using fossil fuels to using sources that do not emit carbon by the end of the current century. In order to achieve sustainability in the construction of energy-positive buildings, it is crucial to employ novel approaches to reduce reliance on fossil fuels. Hence, it is essential to develop buildings with very efficient structures to promote sustainable energy practices and minimize the environmental impact. Our aims were to shed some light on the standards, building modeling strategies, and recent advances regarding the methods of control utilized in the building sector and to pinpoint the areas for improvement in the methods of control in buildings in hopes of giving future scholars a clearer understanding of the issues that need to be addressed. Accordingly, we focused on recent works that handle methods of control in buildings, which we filtered based on their approaches and relevance to the subject at hand. Furthermore, we ran a critical analysis of the reviewed works. Our work proves that model predictive control (MPC) is the most commonly used among other methods in combination with AI. However, it still faces some challenges, especially regarding its complexity. [ABSTRACT FROM AUTHOR]

The study focuses on improving drone landing gear dynamics through an innovative auxetic foot design, leveraging Spider Monkey Optimization for Model Predictive Control adjustment, facilitated by an Arduino-MATLAB interface. The auxetic foot design incorporates materials with a negative Poisson ratio, which allows the foot to expand and enhance energy absorption during landings. This design improves stability and safety during the perched landing process. The SMO-MPC approach is used to optimise the control of the perched landing gear. SMO, inspired by spider monkey search behaviour, optimises auxetic foot control input sequences with the limits of rotational displacement (theta = 30 deg to -30 deg) on the prediction horizon to improve landing gear performance. The real-time implementation of SMO-MPC is achieved through an Arduino-MATLAB interface on quadcopter drone. A comparative analysis is conducted to evaluate the benefits of SMO-MPC compared to conventional MPC methods. The results show that the SMO-MPC approach with auxetic foot design surpasses conventional MPC methods in terms of landing performance with 14.6 % improvement in damping force control and control of aerodynamic stability with pitch of 34.16 %, yaw of 16.87 %, and roll of 31.74 %.

This article presents a novel strategy for designing weighting factors in the model predictive control (MPC) of a three-level inverter-fed permanent magnet synchronous motor (PMSM) by combining multi-objective particle swarm optimization (MOPSO) and artificial neural network (ANN). A cost function containing three weighting factors is designed to formulate a multi-objective optimization scheme. The MOPSO algorithm is employed to establish the Pareto front and calculate the optimal weighting factors for each operating condition. Additionally, an ANN is introduced for online tuning of the optimal weighting factors. Simulation and experimental results demonstrate that the proposed control scheme can solve the design problem of weighting factors, meet the real-time requirements of the inverter system, and exhibit excellent dynamic and steady performance.

This paper introduces a tube-based Model Predictive Control (MPC) formulation for high-order uncertain Linear Parameter-Varying (LPV) systems, utilizing efficient set representations. Initially, a feedback gain is designed based on a new set of LMIs in order to compensate the mismatch between the uncertain LPV system and a nominal model. Thereafter, aiming to reduce the computational cost, we propose an offline computation of the reachable set based on zonotopes and considering the feedback gain previously computed. In addition, from the feedback gain, the reachable set, and considering the admissible state and control sets as constrained zonotopes, we ensures the existence of the nominal control and state sets to be used within the nominal MPC. Lastly, a novel MPC formulation based on constrained zonotopes is developed to enhance the computational cost of the entire methodology. Accordingly, the optimal control problem is redesigned as a function of the constrained zonotope structure. We evaluate the performance of our proposed approach controlling a tiltrotor unmanned aerial vehicle (UAV) carrying a suspended load, a high-order system with twenty-four states, and fast dynamic behavior. A hardware-in-the-loop (HIL) framework is used to carry out the experiments, where control algorithms are implemented on an embedded computer, and the dynamics of the tiltrotor UAV with suspended load are emulated using the high-fidelity ProVANT Simulator.

The current tendency toward increases in energy prices makes it necessary to discover new ways in which to provide electricity to end consumers. Cooperation among the various self-consumption facilities that form energy communities based on networked microgrids could be a more efficient means of managing the renewable resources that are available. However, the complexity of the associated control problem is leading to unresolved challenges from the point of view of its formulation. The optimization of energy exchanges among microgrids in the day-ahead electricity market requires the generation of an optimal profile for the purchase of energy from and sale of energy to the main grid, in addition to enabling the community to be charged for any deviation from the schedule proposed in the regulation service market. Microgrids based on renewable generation are systems that are subject to inherited uncertainties in their energy forecast whose interconnection generates a distributed control problem of stochastic systems. Microgrids are systems of subsystems that can integrate various components, such as hybrid energy storage systems (ESS), generating multiple terms to be included in the associated cost function for their optimization. In this work, the problem of solving complex distributed stochastic systems in the Mixed Logic Dynamic (MLD) framework is addressed, as is the generate of a tractable formulation with which to generate deterministic values for both exchange and output variables in interconnected systems subject to uncertainties using hybrid, stochastic and distributed Model Predictive Control (MPC) techniques.

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.