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This paper presents a model predictive control for nonlinear asynchronous switched systems, aiming to achieve H∞ performance and addressing feasibility issues. The method proposed guarantees the recursive feasibility of each sub‐system after switching moments in the presence of exogenous disturbances and unknown matched and mismatched time intervals due to asynchronous switching. The boundaries of the feasibility region for each sub‐system are determined in order to impose constraints on decision parameters and switching signals. These constraints aim to maximize the mentioned region while ensuring stability. Additionally, by identifying the common region of feasibility among different sub‐systems, conditions are established for initializing the sub‐systems at the switching moments to ensure that the system states remain within this feasibility region even under the worst switching scenario. Therefore, by ensuring the initial feasibility at the switching moments and demonstrating recursive feasibility, the overall feasibility of the developed MPC is guaranteed at all times. The benefits of the proposed approach are thoroughly investigated through the simulation of a chemical system. The results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

Summary: The LC filter integrated quasi‐Z source indirect matrix converter (QZS‐IMC) filters the input voltages and currents while highly expanding voltage boost ratio through the combined QZS network, avoiding the additional input filter while maintaining full‐silicon solution. The traditional proportional‐integral (PI) control method of IMC is realized through the PI regulators with pulse width modulation (PWM), which is complicated due to elaborated vectors computation and duty cycle composition of IMC and extra control loops design is mandatory. This paper proposes a model predictive control (MPC) for the LC filter integrated QZS‐IMC. Based on the established discrete models of circuit voltages and currents, the proposed control method measures the present circuit variables and predicts the variables in the next sampling period. Then, a predefined cost function is evaluated with the predictions and references at each active switching states of the IMC. An optimal switching state that ensures the smallest value of the cost function is applied to the power devices at the beginning of next sampling time. Simulation and experiment are carried out to demonstrate effectiveness of the proposed MPC for the LC filter integrated QZS‐IMC. [ABSTRACT FROM AUTHOR]

The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach. [ABSTRACT FROM AUTHOR]

Summary: To enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system. [ABSTRACT FROM AUTHOR]

In this paper, a single objective variable double voltage vector model predictive control (SOV‐DVVMPC) to lower the computational burden for single‐phase cascaded H‐bridge (CHB) converters is proposed. The proposed algorithm is based on a hierarchical structure to control the grid‐connected current and capacitor voltage without a weighting factor. Firstly, layer I controls the grid‐connected current to select the optimal region, the cost function related to the voltage region is designed and the optimal state is calculated directly by analyzing the linear relationship between the divided region and its adjacent voltage levels, which can reduce the computational burden. Correspondingly, the optimal vectors are symmetrically distributed over the entire control cycle by a modulation principle, fulfilling the fixed switching frequency (FSF) of the system. Then, layer II uses redundant switching states to maintain capacitor voltage balancing. Finally, a single‐phase CHB converter experiment setup is constructed, the results show that compared with DVV‐MPC, the execution time of SOV‐DVVMPC is reduced by 26.4%, and compared with traditional MPC, the switching frequency is fixed and the output current quality is improved. [ABSTRACT FROM AUTHOR]

In this paper, we propose the design of a robust control strategy for the trajectory tracking problem in perturbed unicycle mobile robots. The proposed strategy comprises the design of a robust control law, which is based on an Integral Sliding‐Mode Control (ISMC) approach together with an interval predictor‐based state‐feedback controller through a Model Predictive Control (MPC) scheme. The robust controller deals with some perturbations in the kinematic model, which represent slipping effects, and with state and input constraints that are related to restrictions on the workspace and saturated actuators, respectively. The proposed approach guarantees the exponential convergence to zero of the tracking error. The performance of the proposed approach is validated through some simulations tracking a lemniscate and a circumference curve. [ABSTRACT FROM AUTHOR]

With the rapid increase of wind power penetration, the frequency indicators of power system, encompassing frequency deviation and rate of change of frequency (RoCoF), are prone to exceed the pertinent relay thresholds, thus leading to serious power outages. Wind farms can participate in primary frequency regulation (PFR) to alleviate the above problem. However, wind farms reserve capacity (WFRC) presented by overspeed control and pitch angle control is the important factor for frequency safety especially when large disturbance occurs, and it varies obviously in different conditions. Thus, WFRC should be utilized optimally to avoid insufficient or excessive response of wind farms in PFR. To overcome the challenge, this paper proposes frequency‐trajectory‐oriented control (FTOC) for wind farms based on a two‐fold optimization framework, seeking to obtain the best frequency control performances in PFR through realizing the optimal compromise between frequency results and the consumption of WFRC. First, when the frequency indicators deviate significantly from the normal values, the optimal frequency trajectory is planned periodically according to the local standards and operation conditions, thereby minimizing the total consumption of WFRC on the premise of frequency safety. Second, based on model predictive control and a real‐time power system parameters estimator, the total reference power of wind farms, which helps the frequency track the latest planned trajectory, is optimally distributed among wind generators according to their reserve capacity. In this case, through the optimal frequency trajectory planning and tracking, the new method in this paper not only effectively prevents the frequency results from exceeding the local standards under diverse conditions, but also finds the optimal balance among frequency indicators safety, frequency trajectory smoothness, and the reserve capacity consumption of wind power generators. Finally, the simulation results prove the effectiveness and advancement of FTOC. [ABSTRACT FROM AUTHOR]

Summary Virtual synchronous generators (VSGs) can provide damping and inertia for new power systems are of wide interest. Model predictive control (MPC) has the advantages of simple structure, easy to deal with constraints, and can achieve multi‐objective control. However, the conventional MPC has the problems of relying on the model parameters as well as the difficulty of taking the values of the weighting factors. Based on this, this paper proposes a robust MPC control strategy without weighting factors, which improves the system robustness by identifying the model parameters through model reference adaptive system (MRAS); and introduces fuzzy decision making (FDM) to avoid the selection of weighting factors. First, the mathematical model of VSG is established and the prediction model of VSG multi‐objective control is derived. Second, a detailed analysis is conducted to examine the impact of parameter mismatch on VSG. Third, the expression for online identification of parameters is derived based on Popov's superstability theory, using FDM to optimize multi‐objective control and using decision functions instead of cost functions to avoid the selection of weighting factors. Finally, the effectiveness of the proposed control strategy is verified through the RT‐LAB semi‐physical platform. [ABSTRACT FROM AUTHOR]

This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system to construct a data‐driven model that captures the non‐linear behaviour and fault characteristics. The fault influence is addressed through an online estimation of a time‐varying Koopman predictor, which allows for adjusting the MPC control law to counteract the fault effects. This estimation is performed in a higher dimensional Koopman feature space, where the dynamics behave linearly. As a result, the non‐linear fault‐tolerant MPC optimization problem can be replaced with a more practical and feasible linear time‐varying one using the approximated Koopman predictor. Moreover, by incorporating the online update procedure, the time‐varying Koopman predictor can represent the dynamics of the faulty system. Hence, the controller can adapt and compensate for the faults in real‐time, integrating the fault diagnosis module in the MPC framework and eliminating the need for a separate fault detection unit. Finally, the efficacy of the proposed approach is demonstrated through case study results, which highlight the ability of the controller to mitigate faults and maintain desired system behaviour. [ABSTRACT FROM AUTHOR]

Aiming at the current control steady‐state error caused by sampling delay, parameters mismatch of direct‐drive permanent magnet synchronous motor (PMSM). This paper proposed a method of dual model predictive control (MPC) of direct‐drive PMSM based on sliding mode disturbance observer (SMDO). Deadbeat predictive current control (DPCC) is used for current loop, a SMDO is proposed to observe the system disturbance, the estimation is compensated to DPCC by feedback to decrease the current control steady‐state error. For speed loop, MPC with an internal model control observer (IMCO) can observe and compensate the load disturbance feedforward. The experimental results show that, the dynamic response speed of the proposed strategy is fast, the speed recovery time after sudden load torque is short, and the anti‐load disturbance performance and current tracking performance are effectively improved. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a novel learning‐based model predictive control (LMPC) scheme for discrete‐time nonlinear systems. It overcomes the challenge of manually designing the terminal conditions for traditional MPC and enhances the control performance. The scheme employs the value iteration (VI) in reinforcement learning (RL), and autonomously designs the terminal cost by iteratively performing value function learning and policy update under known dynamics and constraints. In contrast to the existing schemes that combine RL with MPC, the proposed scheme explicitly considers the approximation errors in each iteration. Further, a rigorous theoretical analysis is provided, including the convergence of VI, the stability and performance of the closed‐loop system. In addition, the influences of the prediction horizon and the initial terminal cost on performance are also investigated. Simulation results of a linear system verify the theoretical properties of the LMPC and show that it achieves (near‐)optimal performance. Moreover, its unique superiority over traditional MPC is fully demonstrated in a nonholonomic vehicle regulation example. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete‐time systems in the presence of disturbance. To this end, Takagi‐Sugeno (T‐S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non‐parallel distributed compensation (non‐PDC) technique is applied to design improved RFPMC with better performance. The optimization problem of RFMPC is represented in terms of linear matrix inequalities (LMIs) so as to decrease online computational burden. Besides, feasibility and stability which are decisive factors in MPC theory are investigated for the proposed method. Ultimately, two examples including cart‐damper‐spring system are simulated to evaluate the results of this research work. [ABSTRACT FROM AUTHOR]

Summary: The permanent magnet synchronous motor (PMSM) drive system with small dc‐link capacitor will have the problem of unstable drive system due to the small damping of the LC filter and the negative impedance characteristics of the constant power load. The input LC filter can suppress interference and is essential for the normal operation of the drive, but it may not match the impedance of the post‐stage inverter, causing the drive system to become unstable. In this paper, by constructing the dc‐link voltage prediction model, combined with the PMSM current predictive control, the weighted cost function of the model predictive control (MPC) strategy is designed to realize the motor optimal control and the dc‐link voltage fluctuation suppression control. To avoid introducing additional sensors when constructing the dc‐link voltage model, a source state observer that can estimate the dc‐link voltage and input current is constructed. Simulation and experimental results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a harmonic suppression strategy for photovoltaic‐voltage source converters (PV‐VSC). The proposed method utilizes a harmonic state space (HSS) model‐combine model predictive control (MPC) algorithm, which provides a comprehensive representation of the global characteristics of AC/DC harmonic coupling impedance in photovoltaic systems. The HSS‐based converter model operates as a linear time‐varying periodic system, which allows for the implementation of MPC to optimize the HSS equation. This optimization yields voltage increments across various harmonic orders, providing feedforward compensation for the current controller. By incorporating feedback signals into the current control, harmonic compensation is achieved, resulting in improved power quality. Furthermore, a decoupled proportional resonance‐linear adaptive disturbance rejection control (PR‐LADRC) is employed to effectively mitigate steady‐state errors and decrease total harmonic distortion in the presence of DC‐side disturbances. This is accomplished by decoupling the disturbance observer from the controller and integrating the increment feedback signal derived from the MPC‐HSS. Finally, the efficacy of the proposed method and the analysis results are corroborated through numerical simulations. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we discuss how to synthesize stabilizing model predictive control (MPC) algorithms based on convexly parameterized integral quadratic constraints (IQCs), with the aid of general multipliers. Specifically, we consider Lur'e systems subject to sector‐bounded and slope‐restricted nonlinearities. As the main novelty, we introduce point‐wise IQCs with storage in order to accordingly generate the MPC terminal ingredients, thus enabling closed‐loop stability, strict dissipativity with regard to the nonlinear feedback, and recursive feasibility of the optimization. Specifically, we consider formulations involving both static and dynamic multipliers, and provide corresponding algorithms for the synthesis procedures. The major benefit of the proposed approach resides in the flexibility of the IQC framework, which is capable to deal with many classes of uncertainties and nonlinearities. Moreover, for the considered class of nonlinearities, our method yields larger regions of attraction of the synthesized predictive controllers (with reduced conservatism) if compared to the standard approach to deal with sector constraints from the literature. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the robustness qualities of Model Predictive Control (MPC) algorithms applied for Linear Parameter Varying (LPV) systems. Specifically, we analyze gain‐scheduled LPV MPC schemes, that is, those that use model predictions based on the LPV scheduling variables available at each sampling instant. Accordingly, we extend previous results on finite‐horizon robustness analysis of linear time‐variant (LTV) systems, employing Integral Quadratic Constraints (IQCs) to describe the input‐output behavior of prediction uncertainties. We provide two main novelties in our formulation: (i) we propose a parameter‐dependent Karush–Kuhn–Tucker (KKT) inequality to describe the existence and feasibility of the LPV MPC control inputs; and (ii) we model the uncertainties that arise due to the unavailability of the scheduling trajectory along the prediction horizon as a bounded interconnection in the form of a Linear Fractional Transformation (LFT). Accordingly, we use dissipativity arguments (p$$ p $$‐hard IQCs) in order to compute robust induced gains of the closed‐loop system (specifically, the ℒ2$$ {\mathcal{L}}_2 $$ and ℒ2$$ {\mathcal{L}}_2 $$‐to‐Euclidean metrics), taking into account the MPC prediction uncertainties. We also generate the set of reachable states from a given initial condition. A benchmark example is used to illustrate the proposed analysis procedure. [ABSTRACT FROM AUTHOR]

Summary In this paper, the distributed adaptive model predictive noncooperative game problem is proposed for the discrete‐time multi‐player systems (MPSs) with parametric uncertainties and undirected graph. The state and input constraints are considered along with the parametric uncertainties. An adaptive parameter estimator is proposed for the parametric uncertainty based on a nonlinear scheduling function. Furthermore, the cost function is constructed based on the estimated parametric uncertainties, which is used to replace the cost function of the MPSs with parametric uncertainties. An iterative algorithm is developed such that the adaptive model predictive dynamic game (AMPDG) could converge to the so‐called ε$$ \varepsilon $$‐Nash equilibrium in a distributed manner. We establish sufficient conditions to guarantee the convergence of the proposed algorithm. In addition, the easy‐to‐check conditions are also provided to ensure the uniform boundedness of the studied MPSs. Finally, a numerical example of a group of spacecrafts is provided to verify the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper designs an ellipsoidal tube‐based output feedback robust model predictive control approach for linear systems with bounded disturbances and noises subject to physical constraints. The ellipsoidal tube‐based output feedback robust model predictive control approach combines the off‐line optimization to compute controller parameters and deal with system uncertainties, and the on‐line optimization to stabilize the nominal system with time‐varying tightened constraints. In the off‐line optimization, the state observer gain, ancillary feedback controller gain, and tightened constraint sets on the nominal system are synthesized in one optimization. Then, an ellipsoidal terminal constraint set with terminal controller gain and terminal cost matrix, and tightened constraint sets related to different sizes of estimation error bounds are computed. In the on‐line model predictive control optimization with time‐varying tightened constraints, a sequence of nominal control inputs is receding horizon optimized to steer the nominal system state to the ellipsoidal terminal constraint set. When the current nominal system state is bounded within the ellipsoidal terminal constraint set, the nominal control inputs are switched to the feedbacks on the closed‐loop nominal system states. Recursive feasibility of the proposed algorithm and robust stability of the controlled system are guaranteed. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive controller for tracking periodic parametric reference curves. The controller is formulated in a single layer so that the time‐parameterization of the reference curve, the trajectory planning, and the trajectory tracking tasks are solved in a single optimization problem which is computed at each sampling time. The auxiliary state and input trajectories are introduced into the optimization problem as decision variables, which become the planned trajectory to be effectively followed. By design, the closed‐loop system guarantees of recursive feasibility of the optimization problem while being able to handle arbitrary changes of reference during execution time. Such properties are discussed in detail, and new results on the asymptotic average performance are presented. Simulation results show the benefits of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, a fault isolation, diagnosis and fault tolerant control algorithm is proposed for nonlinear multiple multiplicative faults stochastic distribution control systems employing Takagi–Sugeno fuzzy system. To obtain the detailed fault information, a fault detection algorithm is introduced to discover the fault occurrence time. Then a fault isolation observer is built to produce the residual, and the error system is separated to subsystems affected only by disturbance and multiplicative faults. Moreover, a fault estimation scheme is presented to obtain the fault magnitude information. When faults occur, the system output probability density function will deviate from the desired distribution. So the model predictive control fault tolerant control scheme is needed to minimize the impact of faults as much as possible to make sure that the post fault output probability density function track the desired probability density function. The validity of the designed algorithm is demonstrated through a simulation example, where the fault tolerant control algorithm ensures that the system output probability density function still track the given output probability density function despite the complex case of multiple multiplicative faults occurring simultaneously. [ABSTRACT FROM AUTHOR]

Research on global harmonic mitigation against distributed and high‐proportioned nonlinear loads in modern distribution networks has received extensive attention. In this paper, a distributed compensation method for active power filters (APFs) is proposed, which utilizes a small number of APFs with adjustable reference to alleviate the global voltage distortion caused by a large number of distributed nonlinear loads. The model predictive control (MPC) is employed to calculate the optimal current reference for compensators at each optimization moment by predicting the system's performance in a certain future time period. Furthermore, the Kalman filter (KF) algorithm is used to estimate the initial harmonic voltage for the MPC, reducing the unfavorable effects of the model uncertainty and measurement noise. The test results based on a seven‐bus distribution network in MATLAB are provided to verify the effectiveness of the proposed method. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Advanced control schemes such as model predictive control can be used to minimize the use of resources while guaranteeing the specified product quality. In this paper, we consider an industrial mother liquor distillation column varying flow rate and composition of the feed. There are specifications of the composition for all product streams. To address this challenging control problem, we employ a nonlinear model‐predictive controller using a hybrid model, which consists of a simple phenomenological model augmented by a data‐based component to compensate the plant‐model mismatch. The trustworthiness of the data‐based model is addressed using a domain of validity of the data‐based model, which is estimated using a one‐class support vector machine. During operation, it may turn out that the model is also reliable in a wider range, therefore, data of recently visited operating points is recorded and the domain of validity is extended if the model is sufficiently accurate. To improve the performance of the controller, an artificial neural network model is used to estimate the product composition from available measurements. [ABSTRACT FROM AUTHOR]

This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time‐varying disturbance observer for a discrete‐time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air–ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time‐varying air‐ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time‐varying model, a multiple harmonics disturbance observer with time‐varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, nonlinear model predictive control (NMPC) is proposed for autonomous vehicle drifting, that is, stabilizing the vehicle at a desired unstable equilibrium point. Firstly, a three‐degree‐of‐freedom vehicle model with a nonlinear tire model is introduced, and the equilibrium points are calculated. The relationship between the desired unstable equilibrium point and the lateral stability region is analyzed based on the phase plane method. Secondly, NMPC is designed to force vehicle states to stay around the desired unstable equilibrium point, that is, to keep the vehicle in sustained drifting. The terminal region and terminal constraint of NMPC are determined off‐line to guarantee stability. Thirdly, Koopman operator theory and dynamic mode decomposition with control are introduced to obtain an approximately linear model, by which the nonlinear optimization problem is converted to a quadratic programming problem. Finally, comparative experiments are conducted by simulation, in which various model uncertainties are considered. The effectiveness of the proposed approach to achieve sustained autonomous drifting and to ensure vehicle safety is illustrated, and the efficient implementation of the proposed approach is also shown. [ABSTRACT FROM AUTHOR]

In this paper, the problem of dynamic output feedback model predictive control (MPC) for the Takagi‐Sugeno (T‐S) model with bounded disturbance and redundant channels is investigated based on an event‐triggered scheme. The redundant channels, described by a set of mutually independent Bernoulli processes, are applied to improve the reliability of data transmission. The state immeasurable is considered, and an event‐triggered state estimator with a time‐varying threshold is designed to estimate the true system state. The estimator and controller gains are parameterized as a non‐convex optimization problem. In order to online solve the resulting optimization problem, the cone complementarity approach (CCA) with iteration optimization is adopted. The mean‐square quadratic boundedness (MSQB) is utilized to characterize the invariance of the system state and guarantee the closed‐loop stability, while the recursive feasibility is ensured via online refreshing the estimation error set. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed theoretical method. [ABSTRACT FROM AUTHOR]

Model predictive control has been wildly used in the modern power electronic converter with the advantages of the multi‐objective optimization, no additional modulator is required and small grid current harmonic. However, the switching operation is not constant and higher sampling frequency must be required. A novel direct power control using model predictive control (DPC‐MPC) strategy is presented in this paper, which achieve the optimal vector prediction scheme with constant switching frequency operation. The calculation process is greatly simplified due to the optimal voltage vector generated directly, then, it applied to the control of a three‐phase three‐level T‐type grid converter. A cost function is first built to balance the neutral‐point‐potential voltage. After then, the cost function is updated by considering the delay caused by the calculation, sampling and gate driver. Finally, a comprehensive comparative study with the conventional DPC‐MPC strategy is introduced to verify the superiority of the presented DPC‐MPC. The simulation and experimental results show that the steady and the transient performance of the grid current with the proposed DPC‐MPC control strategy are effectively improved. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a DC microgrid integrated series active power filter (Se‐APF) to improve the voltage profile of the utility distribution system under sag, swell, and harmonics scenario. The DC microgrid encompasses a solar photovoltaic generation unit and a composite energy storage unit (CESU). A lithium‐ion battery and supercapacitor as a CESU are envisioned in this work. Compared to conventional Se‐APF supported by the capacitor/battery storage, the integrated DC microgrid prolonged the operational features of the Se‐APF for voltage quality improvement and feeding the localized dc loads or charging the CESU occasionally from the PV generation system. Further, a power management algorithm is implemented to coordinate the operations of the sources in the DC microgrid to accomplish the above‐mentioned features. The Se‐APF and associated power converters in the DC microgrid are regulated by a nonlinear controller based on optimal switching vector‐model predictive control algorithm. The proposed control method can be used to help distribution networks with voltage fluctuations by offering much faster and more precise voltage regulation. The feasibility and effectiveness of the proposed control strategy are verified in MATLAB/Simulink platform and then validated in a real‐time simulation platform using Opal‐RT Lab real‐time simulator OP4510. [ABSTRACT FROM AUTHOR]

For the power system integrated with the doubly‐fed induction generators (DFIGs), the frequency regulation may be provided by the DFIGs in addition to the synchronous generators (SGs). With the model predictive control (MPC), the active power output of the DFIG is adjusted by predicting the system frequency with the dynamic power flow (DPF) algorithm. In this paper, the governor delay of the SGs is included to improve the accuracy of the DPF‐based prediction model. The DPF predicts the frequency response with the desired active power response of the DFIG. To reach the reference with the high accuracy and efficiency, the MPC‐based direct rotor current control is applied to adjust the active power output in coordination with the frequency regulation, forming the proposed hierarchical frequency control strategy. The mechanical transient of the DFIGs involving the shaft and pitch angle system is included in the DPF model to introduce the mechanical constraints of the DFIGs to the MPC. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper is concerned with model predictive control of piecewise affine systems with dead zone constraints based on the mixed logical dynamical modeling approach. A predictive control strategy based on a mixed logic dynamical model is proposed. The strategy contains two steps: the first step is to transform the piecewise affine systems into mixed logic dynamical models; the second step is to apply a predictive control scheme based on this model. Then, the optimal control sequence can be obtained by solving the constructed mixed‐integer quadratic/linear programming problem. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

The control of field outlet temperature of parabolic trough solar field (PTSF) faces great challenges as the PTSF suffers from multiple field disturbances. To address the issue, this paper proposes an optimal disturbance predictive and rejection controller in a composite scheme, in which the field disturbances are firstly reconstructed into a single lumped one by model transformation, the lumped disturbance is then estimated by a third‐order extended state observer with only the input–output data. Based on the current and past estimated lumped disturbance values, an incremental least‐square support vector machine based predictor is designed to adaptively predict the future dynamics of the lumped disturbance. More importantly, an integration method of the estimated and predicted lumped disturbance sequence and optimal model predictive control is proposed based on real‐time correction of steady‐state sequence to compensate for the lumped disturbance feedforward while maintaining the optimality of the solution. The composite optimal control law demonstrates that the future dynamics of the lumped disturbance play an active role in disturbance rejection. The asymptotical practical stability analysis of the closed‐loop system is presented rigorously. Some simulation cases are conducted on a verified analytical PTSF model to demonstrate the disturbance rejection and target tracking performance of our proposed design. [ABSTRACT FROM AUTHOR]

This article proposes a new control strategy to regulate the six‐phase induction machine fed by a nine‐switch converter. The traditional model predictive control method is a favorable feedback control routine in variable‐drive applications. However, the lack of the term that assess the closed‐loop stability in the conventional cost function formulation may cause potential closed‐loop instability and be incapable of tolerating the parameter mismatch. Furthermore, the conventional model predictive control formulation cannot ensure global optimality in case of deviation from the nominal operating point. In this paper, the closed‐loop stability is enhanced by including a sliding mode stability term in the objective function. The traditional cost function is redeveloped as a sliding mode control stability term, and the selection of the control input that satisfies the closed‐loop loop stability condition is ensured. The robustness performance of the system is improved, and a better steady‐state performance is achieved. The proposed control strategy is proved using a hardware setup including a six‐phase 30° spatially shifted induction motor and nine‐switch converter prototype. The experimental results demonstrate that a secured six‐phase induction motor operation is guaranteed while improving the closed‐loop robustness. [ABSTRACT FROM AUTHOR]

A novel isolated 7‐level (7L) multi‐level inverter (MLI) topology for grid‐connected medium and high voltage power applications is presented in this paper. The topology consists of two isolated transformers with different winding ratio and four switches connected to a single‐phase grid. This topology provides a great advantage over conventional MLIs by reducing passive and active components to allowing a 7L output voltage. Moreover, model predictive control (MPC) method is implemented to 7L‐MLI to generate gate signals of switches and ensure capacitor voltage balancing and grid connection. Performance of proposed topology has been confirmed by in‐depth analysis under simulation and laboratory setup with existing studies. Experimental results of a 1.5 kW 220 Vrms prototype are presented to support the simulation results. The prototype shows that the harmonic results match the limits of the IEC 61000‐3‐2 standard and show a better performance for grid connection and capacitor voltage balancing than conventional ones. [ABSTRACT FROM AUTHOR]

Summary: The renewable energy (e.g., solar photovoltaic)‐based grid‐connected microgrid (MG) with composite energy storage system (CESS) is feasible to ensure sustainable and quality power to the commercial and domestic load demands. Effective control systems provide the dynamic performance of such deployed MGs. This paper investigates the application of the finite control‐set model predictive controller (FCS‐MPC) for solar photovoltaic‐based grid‐connected MGs with composite energy storage. FCS‐MPC considers the discrete nature of interfacing (DC–AC and DC–DC) converters into the control algorithm to predict the optimal control command for reference current tracking. Compared to classical control approaches, FCS‐MPC provides a fast‐dynamic response, discards the modulation stage, and effectively handles the effects of dynamic source and load variations and nonlinear loads. The control system encapsulates a proportional–integral (PI) controller, low‐pass filter (LPF), power management algorithm (PMA) unit, and followed by the FCS‐MPC approach. The CESS unit consists of a battery and supercapacitor storage system. The efficacy of the FCS‐MPC approach is verified through the MATLAB/Simulink results. Solar photovoltaic variation due to irradiance, utility grid availability, electricity pricing, and dynamic load variations are considered in the implementation. Furthermore, real‐time simulation of the proposed method is validated using OPAL‐RT OP4510 real‐time simulator. [ABSTRACT FROM AUTHOR]

With increase in the number of photovoltaic system installations, the prospect of voltage deviation from the acceptable range in a distribution system is increasing. Voltage distribution can be controlled using step voltage regulators (SVRs) and static var compensators (SVCs); however, for their coordination, it is necessary to stringently adjust the set value for each distribution line. In this study, a method of applying model predictive control to the coordination between an SVR and an SVC is investigated. In this paper, we first describe the proposed method and subsequently present the results of the performance comparison of the proposed and conventional methods to validate the effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC) systems with sample‐and‐hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we consider model predictive control for a class of constrained discrete‐time Markov jump linear systems with multiplicative noises. A generalized performance criterion is composed of a weighted sum of a linear combination of the (a) expected value of quadratic forms of state and control vectors, (b) quadratic forms of the expected value of the state vector, and (c) the linear component of the expected value of the state vector. The goal of the present paper is to design optimal control strategies subject to hard constraints on the input manipulated variables and to provide a numerically tractable algorithm for practical applications. The results are applied to a problem of online investment portfolio selection. Our approach is tested on a set of a real data from the New York Stock Exchange. [ABSTRACT FROM AUTHOR]

The correct management of power exchange between the doubly‐Fed induction generator (DFIG) and the grid depends on the effective optimal operation of the DFIG based wind energy conversion system (WECS). A modified optimal model predictive controller (MPC) architecture for WECS is proposed in this paper. The proposed scheme is carried out in two stages, as follows: (a) Using the MPC model and the tracking factor, an optimal control law is created, and (b) the aforementioned optimized issue is addressed using the linear matrix inequality (LMI) approach. Some improvements were made to the conventional LMI‐based MPC technique to lower the high computational complexity. To perform active and reactive power control in DFIG, the proposed approach is applied on the rotor‐side converter (RSC) control. The control law is divided into two sections. The first section deals with rotor current, while the second section deals with the RSC switching states. The LMI is used in the second portion of the control law to find the best switching state solution. The proposed method was created using the MATLAB/SIMULINK environment and tested in a 1hP DFIG‐WECS setup. By comparing the obtained results to conventional LMI‐PI, MPC‐based WECS, the effectiveness of the obtained results is verified. Variable wind speed conditions are used to vary all the results. Integral absolute error, integral time absolute error, and Integral square error measurements are used to compare the tracking performance index of all methods. [ABSTRACT FROM AUTHOR]

Various control methods which utilize online optimization have been developed. A quadratic programming (QP) problem is typically solved online in such methods. In this paper, we derive a regional sector condition for a QP problem. Then, based on the regional sector condition, we derive a condition to analyze regional l2‐performance of a feedback system with QP. The analysis condition is expressed as a set of linear matrix inequalities. Then, we show methods for applying the analysis condition to a model predictive control system and a feedback system with a control allocator. A numerical example is provided to show effectiveness of the result. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a novel control method based on model predictive control (MPC) and torque sharing function (TSF) is addressed to minimize the torque ripple of switched reluctance motor (SRM). Firstly, an accurate SRM model is established based on flux‐linkage characteristic curves obtained from the locked rotor test, which can predict the future operation state of the SRM drive system. Secondly, an improved TSF curve is proposed, and genetic algorithm is used to optimize TSF parameters to reduce torque ripple in commutation region. Besides, the MPC is integrated into the TSF control framework to replace the traditional hysteresis controller, and establishes a new TSF‐based model predicted torque control (MPTC) method, which avoids the frequency conversion problem caused by torque or current hysteresis controller. Further, a sector division scheme is addressed to decrease the number of candidate voltage states, whereby the computation of the controller in each sampling period is effectively reduced. Finally, in order to verify the correctness of the MPTC method, it is compared with the traditional direct instantaneous torque control (DITC). Both simulations and experiments on a three‐phase 12/8 pole SRM have confirmed that MPTC scheme can not only decreases torque ripple and stator copper losses, but also has higher efficiency than DITC. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we define several instances of model predictive control (MPC) for linear systems, including both deterministic and stochastic formulations. We show by explicit computation of the associated control laws that, under certain conditions, different formulations lead to identical results. This paper provides insights into the performance of stochastic MPC. Amongst other things, it shows that stochastic MPC and traditional MPC can give identical results in special cases. In cases where the solutions are different, we show that the explicit formulation of the problem can give insight into the performance gap. [ABSTRACT FROM AUTHOR]

Summary: Previous works have presented the output feedback min‐max model predictive control (MPC) for the discrete‐time system with both polytopic uncertainty and bounded persistent disturbance, where the controller parameters are optimized at each sampling instant. This paper proposes the corresponding offline approach in order to reduce the online computational burden. Such offline MPC, when the state is measurable and there is no disturbance, has been constructed in the work of Wan and Kothare (An efficient off‐line formulation of robust model predictive control using linear matrix inequalities. Automatica. 2003;39(5):837‐846). Since this paper considers the case when the true state is unknown, the ellipsoidal regions of attraction (applying only to the estimated state) lose their asymptotic invariance property, and the estimation error set (EES) has a major effect on the control performance. This paper refreshes EES invoking the one‐step reachable set and guarantees that the signals being penalized in the performance cost function to converge to a neighborhood of the equilibrium point. Two examples are given to illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a modified single‐phase five‐level inverter is proposed with a single DC source and six switches. Comparing with the presented converters, the proposed topology has the advantage of reduced device count and the fundamental switching frequency for high blocking voltage switches. The proposed converter implements without clamping diodes and transformers, which reducing the volume, moreover mass of the inverter. In order to achieve the optimum output voltage, the model predictive controller is proposed as a rapid dynamic and semi‐low‐computation digital control system. This closed‐loop system is implemented in TMS320f28335 digital signal processor to assess the performance of the proposed converter under nonlinear and linear loads. Analytical investigation, including simulation and an experimental result, shows compliance with IEC 62040‐3 for inverters in standalone applications. [ABSTRACT FROM AUTHOR]

This paper proposes a method to design the search space of Model Predictive Control (MPC) for improving the steady current control performance of Permanent Magnet Synchronous Motor (PMSM) current control system. A current control system based onMPC predicts the future current behaviors for considerable input voltage and determines the optimum input voltage by evaluating these behaviors. However, the basic MPC has inferior steady current control performance depending on the operating points. This is because the search space of basic MPC represented by voltage vectors is not suitable appropriate for PMSM current control. To overcome this problem, we consider difference of search space of basic MPC and current vector control with a modulator such as Pulse Width Modulation (PWM) or Space Vector Modulation (SVM) and clarify the necessary search space for improving the steady current control performance. Then, we propose a designmethod for the search space, and through experimentation, we show that proposed design method can improve the steady current control performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel Instantaneous Symmetrical Component Active Power theory with Finite Control State based Model Predictive Control (ISCAP‐FCS‐MPC) strategy is proposed for Distribution‐Static‐Compensator (DSTATCOM) to enhance the power quality in distribution system. Unlike conventional control schemes, the proposed method does not necessitate modulators for switching pulse creation and the discrete implementation of the system is offered to assess the forthcoming actions of the source currents for each of the eight achievable switching actions. These switching actions are generated with regard to turbulences raised in load as well as source sides. The proposed methodology is justified via MATLAB/Simulink environment and a relative analysis is realized through conventional Proportional Integral based Hysteresis Current Control (PI‐HCC) strategy. Further, an experimental investigation is accomplished with consideration of Real‐Time Digital Simulator (RTDS) incorporated with front end processor Xilinx spartan‐3 XC3S5000. [ABSTRACT FROM AUTHOR]

Summary: Wind turbine components are subject to considerable fatigue because of extreme environmental conditions to which they are exposed, especially those located offshore. Wind turbine blades are under significant gravitational, inertial, and aerodynamic loads, which cause their fatigue and degradation during the wind turbine operational life. A fatigue problem is often present at the blade root because of the considerable bending moments applied to this zone. Interest in the integration of control with fatigue load minimization has increased in recent years. This paper investigates the fatigue assessment using a rainflow counting algorithm and the blade root moment information coming from the sensor available in a high‐fidelity simulator of a utility‐scale wind turbine. Then, the integration of the fatigue‐based system health management module with control is proposed. This provides a mechanism for the wind turbine to operate safely and optimize the trade‐off between components' life and energy production. In particular, this paper explores the integration of model predictive control with the fatigue‐based prognosis approach to minimize the damage of wind turbine components (the blades). A control‐oriented model of the fatigue based on the rainflow counting algorithm is proposed to obtain online information of the blades' accumulated damage that can be integrated with model predictive control. Then, the controller objective function is modified by adding an extra criterion that takes into account the accumulated damage. The scheme is implemented and tested in a well‐known wind turbine benchmark. [ABSTRACT FROM AUTHOR]

Summary: The present paper addresses an observer‐based output feedback robust model predictive control for the linear parameter varying system with bounded disturbance and noise subject to input and state constraints. The main contribution is that the on‐line convex optimization problem not only simultaneously optimizes the observer and controller gains to stabilize the augmented closed‐loop system but also incorporates the refreshment of bounds of the estimation error set. The optimization problem steers the nominal augmented closed‐loop system to converge to the origin, and the real augmented closed‐loop system bounded within robust positive invariant set converges to a neighborhood of the origin such that recursive feasibility of the optimization and robust stability of the controlled system are ensured. Two numerical examples are given to illustrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we present a novel nonlinear model predictive control (NMPC) formulation for the transient control of a DC‐DC converter. We demonstrate that a real‐time implementation of the proposed NMPC scheme using the PANOC solver can be efficiently applied to control DC‐DC converters in the microsecond range. Moreover, an embedded, code‐generated version of PANOC can be implemented using microcontrollers or digital signal processors. The algorithm is incorporated in the transient simulator of PWM DC‐DC converters, and the operation of the simulator on the boost converter's example is presented, comparing the performance of our NMPC‐controller with that of a classical PID controller. The operation of the boost converter controlled using the proposed NMPC algorithm is validated experimentally. [ABSTRACT FROM AUTHOR]

Summary: This paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the resilient control problem for constrained continuous‐time cyber‐physical systems subject to bounded disturbances and denial‐of‐service (DoS) attacks. A sampled‐data robust model predictive control law with a packet‐based transmission scheduling is taken advantage to compensate for the loss of the control data during the intermittent DoS intervals, and an event‐triggered control strategy is designed to save communication and computation resources. The robust constraint satisfaction and the stability of the closed‐loop system under DoS attacks are proved. In contrast to the existing studies that guarantee the system under DoS attacks is input‐to‐state stable, the predicted input error caused by the system constraints can be dealt with by the input‐to‐state practical stability framework. Finally, a simulation example is performed to verify the feasibility and efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]