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The control of quadrotor vehicles under state and parameter uncertainty is a well studied problem that is vitally important to the deployment of these systems under real world conditions. In this article, we propose a linearization‐based extension to nonlinear systems of the existing scenario model predictive control (MPC) framework, which quantifies the impact of uncertainty on the vehicle dynamics through repeated sampling of the uncertainty space. Given the computational costs of such an approach, we also propose two simplifications of the scenario MPC algorithm that are significantly more tractable. In order to evaluate the performance of the algorithms, the specific problem of the control of a bidirectionally actuated quadrotor vehicle is considered. Simulations are carried out for each scenario MPC scheme as well as for a reference deterministic MPC scheme. When a sufficiently large sample count is considered, each of the scenario MPC algorithms achieves safer performance than the deterministic formulation without sacrificing any optimality. Additionally, the approximate solution techniques conclusively outperform the original nonlinear scenario MPC formulation for the same computational cost. [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]

This article introduces a control method for trajectory tracking of underactuated unmanned marine vehicles (UMVs), employing the sliding mode predictive control (SMPC) scheme. To address the challenges of demonstrating system stability with a local feedback controller for underactuated UMVs in model predictive control (MPC), this article proposes an auxiliary controller design method based on sliding mode control. A sliding mode dynamic is derived through an error system and sliding surface equations. Compared to existing literature, which predominantly emphasizes demonstrating input‐state stability, this strategy ensures the asymptotic stability of the closed‐loop system by introducing a novel method for selecting weight matrices. Furthermore, extended terminal sets and feasible sets constructed via sliding variables are provided, thereby reducing conservatism. Ultimately, the SMPC scheme is validated through simulation and hardware experiments providing quantitative evidence of its effectiveness in real‐world applications. [ABSTRACT FROM AUTHOR]

ABSTRACT This article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%. [ABSTRACT FROM AUTHOR]

This article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [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]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force. [ABSTRACT FROM AUTHOR]

A model predictive control (MPC) method based on Q-learning algorithm, named QMPC, is proposed for weakly damped, nonlinear and open-loop unstable magnetic levitation platform (MLP) systems. In addition, the design of MPC controller for the MLP system, the state space of the MLP system airgap, the action space of the predictive horizon and control horizon, the reward and punishment function are also included in this research. Based on the Simscape and MATLAB/Simulink, the joint simulation of the MLP control system is realized. Compared with PID controller and traditional MPC controller, the simulation results show that QMPC controller has better disturbance rejection ability and tracking performance under six working conditions. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these challenges for a three-phase inverter with an output LC filter. The first part of the control scheme is a linear and modified model-free approach based on the auto-regressive structure (ARX) with exogenous input. The second part is the computationally efficient optimization algorithm based on the active set method to solve the optimization problem of the MFPC. The objective of the control scheme is to regulate the output voltages of the inverter in the presence of constraints. The constraints are the maximum admissible filter current and optimal duty cycle to avoid any damage to the system. To validate the performance of the proposed control scheme, simulations and hardware-in-loop (HIL) real-time investigations have been performed, comparing the results of the proposed approach with the model-based predictive control. The results showcase the computational efficiency and effectiveness of the MFPC approach, demonstrating its potential for overcoming the limitations of traditional MPC in power electronics systems. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle's 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system's stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov's theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity. [ABSTRACT FROM AUTHOR]

With the widespread integration of distributed resources, optimizing the operation of urban distribution networks faces challenges including uneven source-load-storage distribution, fluctuating feeder power flows, load imbalances, and network congestion. The urban snow-shaped distribution network (SDN), characterized by numerous intra-station and inter-station tie switches, serves as a robust framework to intelligently address these issues. This study focuses on enhancing the safe and efficient operation of SDNs through a two-phase optimal scheduling model that coordinates source-network-load-storage. In the day-ahead scheduling phase, an optimization model is formulated to minimize operational costs and mitigate load imbalances. This model integrates network reconfiguration, energy storage systems (ESSs), and flexible load (FL). During intra-day scheduling, a rolling optimization model based on model predictive control adjusts operations using the day-ahead plan to minimize the costs and penalties associated with power adjustments. It provides precise control over ESS and FL outputs, promptly correcting deviations caused by prediction errors. Finally, the proposed model is verified by an actual example of a snow-shaped distribution network in Tianjin. The results indicate significant improvements in leveraging coordinated interactions among source-network-load-storage, effectively reducing spatial-temporal load imbalances within feeder clusters and minimizing the impact of prediction inaccuracies. [ABSTRACT FROM AUTHOR]

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge ( S O C ) constraint of the battery, partition the S O C into different states. Using S O C as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery S O C to prevent S O C from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. [ABSTRACT FROM AUTHOR]

This article proposes a novel event‐triggered economic model predictive control (EMPC) scheme with shrinking prediction horizon of nonlinear systems subject to the constraints on the state and control. Some auxiliary positive‐definite functions at economic optimal equilibrium points are defined for economic performance functions. The optimal value function of the auxiliary function is used to design an adjustable stability constraint, which is imposed on the original EMPC optimization problem. The stability constraint is relaxed to design the triggered scheme avoiding Zeno behavior, while the prediction horizon is shrinked in different degrees by judging whether the last state within the triggered interval is in the terminal region. The involved parameter can be used to achieve a sensible compromise between system performance and computational complexity. The sufficient conditions guaranteeing the recursive feasibility and stability of the EMPC are derived. Finally, the effectiveness of the algorithm is illustrated by a continuously stirred tank reactor example. [ABSTRACT FROM AUTHOR]

Pneumatic actuators exhibit significant potential across various applications owing to their compliance, yet achieving precise motion control remains challenging due to rate-dependent and asymmetric hysteresis. While the Prandtl–Ishlinskii model adeptly captures intricate hysteresis traits, its practical control usage often necessitates intricate inversions, resulting in elevated computational burden and limited accommodation of system uncertainties and model inaccuracies. This study introduces an online, rate-dependent modified generalized Prandtl–Ishlinskii model derived via the gradient descent algorithm. This model is seamlessly amalgamated with a model predictive control strategy, addressing the inversion challenge inherent in the Prandtl–Ishlinskii model. Leveraging integration with a three-layer fuzzy neural network controller, the proposed approach achieves closed-loop trajectory tracking control for a soft bending pneumatic muscle. Convergence analysis, grounded in Lyapunov theory, underscores the efficacy of the proposed model. Comprehensive real-world comparative experiments affirm the approach's effectiveness and reliability. [ABSTRACT FROM AUTHOR]

We propose a novel robust Model Predictive Control (MPC) scheme for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The proposed algorithm is a combination of funnel MPC, i.e., MPC with a particular stage cost, and the model-free adaptive funnel controller. The new robust funnel MPC scheme guarantees output tracking of reference signals within prescribed performance bounds--even in the presence of unknown disturbances and a structural model-plant mismatch. We show initial and recursive feasibility of the proposed control scheme without imposing terminal conditions or any requirements on the prediction horizon. Moreover, we allow for model updates at runtime. To this end, we propose a proper initialization strategy, which ensures that recursive feasibility is preserved. Finally, we validate the performance of the proposed robust MPC scheme by simulations. [ABSTRACT FROM AUTHOR]

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid. [ABSTRACT FROM AUTHOR]

In this paper, a new model predictive control termed as intelligent model predictive control (IMPC) combined with an improved new competitive swarm optimizer (CSO) is designed. The analytical predictive model is not necessarily established a priori in the proposed IMPC algorithm, and the control plant can be used directly as the predictive model to reduce the complexity of the algorithm. In addition, two new techniques, dynamic initialization and back steps methods, are proposed and utilized to improve the traditional CSO to realize constraints management during the optimization process. An application to aeroengine transient-state control is studied to verify the effectiveness of the presented IMPC algorithm. It is shown that, benefitting from the IMPC algorithm, the control task is well completed and all the constraints are satisfied. Practical Applications: This paper proposes a new optimization algorithm to solve some complex optimization problems with constraints. The designed new optimization algorithm is simple and easy to implement and it is more suitable for applications with black-box models or complex optimization objective functions compared with some existing optimization algorithms. This paper may provide some help to researchers who are interested in model predictive control or metaheuristic algorithms and to people whose work contains some complex optimization problems. This paper also provides a design method of aeroengine transient-state control plans. The simulation results showed that compared with some existing control methods or optimization algorithms, the aeroengine could accelerate to the desired steady-state point with shorter transient time and all constraints are satisfied when utilizing the proposed new optimization algorithm. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) emerges as an attractive alternative for the control of modular multilevel converters (MMCs) owing to its superior dynamic performance and ease of implementation. Nevertheless, conventional MPC's performance could be further improved by reducing dependence on weighting factors, switching frequency, and computational burden. To achieve these objectives, an arm‐current sensorless MPC is developed for a grid‐interfacing MMC in this article. The MPC is integrated with an arm‐current sensorless reduced switching frequency voltage balance algorithm that requires only m+1 switching vectors (where m= number of submodules per arm) to optimize the cost function. This method does not require knowledge of arm‐current direction to decide the charging and discharging state of the submodule capacitors. The developed control not only reduces the switching frequency of devices but also eliminates the need to sense the arm currents. As a result, it reduces the system's cost, complexity, computational burden, switching losses and provides a more reliable control method for MMCs with superior dynamic performance. Further, the effect of parameter mismatch on the developed MPC's dynamic performance and stability is studied. The developed MPC's effectiveness is validated through both simulation and experimental results and also compared with existing methods under different conditions. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is recognized as an efficient control method for the modular multilevel converter (MMC), owing to its advantages, such as good robustness, rapid dynamic response, and multi-objective control. However, due to the coupling relationship between the ac-side current and the circulating current, the existing MPC has an impact on the ac-side current while suppressing the circulating current. In this paper, the relationship between the ac-side current performance and circulating current suppression is discussed in detail, and a three-stage MPC (TS-MPC) strategy is proposed to optimize the comprehensive performance. With the ac-side current control, circulating current control, and comprehensive optimization control, the optimum performance of both the ac-side current and circulating current suppression is realized while maintaining a low computational burden. Moreover, a grouping sorting algorithm is designed to reduce the calculation burden and to balance the capacitor voltages. The steady-state and transient performances of the proposed TS-MPC strategy have been verified by experimental results, which validates its correctness and effectiveness. [ABSTRACT FROM AUTHOR]

In this study, model predictive control (MPC) was considered for a class of discrete time-delay systems with external disturbances. Equivalent input disturbance (EID) is a commonly utilized active disturbance rejection technique that enhances the rejection of system disturbances. Any disturbance can be effectively suppressed using the EID approach, leaving no traces of the disturbance. This paper proposes a novel MPC control approach that combines the EID method with the MPC principle. An MPC law with disturbance-rejection performance was presented after obtaining and combining the EID estimates with performance indicators using the EID method. Optimizing the performance index is then converted into a semi-definite problem comprising of an objective function and linear matrix inequality through building a Lyapunov function. Based on this basis, an MPC design algorithm is proposed. A numerical simulation was conducted to confirm superiority and efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stable economic model predictive control (EMPC) strategy for constrained nonlinear systems with changing economic criteria. The traditional EMPC may lead to infeasibility and even instability of the closed-loop system when the criterion has been changed. Firstly, a generalised terminal constraint is introduced to ensure the recursive feasibility of the economic optimisation problem, which enforces the terminal states to converge to an arbitrary equilibrium state rather than a predetermined fixed one at the initial time. Then the stability constraint is constructed by solving an auxiliary optimisation problem online, which is the key to guaranteeing the asymptotic stability of the closed-loop system. Finally, sufficient conditions for feasibility and asymptotic stability are derived in the context of changing economic criteria. The main feature of the proposed strategy is that it does not need to know the changes of economic criterion in advance, and its properties and effectiveness are exemplified by simulations on a nonlinear chemical process example. [ABSTRACT FROM AUTHOR]

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the path tracking controller, particularly in complex driving environments. To address this issue, a tube-based model predictive control strategy (Tube MPC) is proposed. Initially, nominal dynamic modeling of intelligent vehicles is conducted, followed by linearized derivations. Leveraging the resulting linearized nominal model, the MPC cost function is solved to derive the control law for the nominal system. Subsequently, an error model between the actual and nominal systems is established, integrating various target constraints for path tracking and formulating a robust objective function. Utilizing the linear matrix inequality optimization method, a state feedback-assisted control law is derived to mitigate steady-state lateral position error. This approach ensures a more precise alignment between the actual and nominal states, ultimately enhancing tracking accuracy. Finally, experimental validations are conducted in the Carsim/Simulink simulation environment, encompassing diverse vehicle speed conditions and driving scenarios to assess the proposed controller against traditional Model Predictive Control. The experimental results demonstrate that, while ensuring vehicle stability, this control method exhibits superior tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper explores event-triggered model predictive control (MPC) for constrained switched systems, incorporating a buffer-based anti-attack (BBAA) approach to ensure network security. Since the system state is unknown and disturbed, an observer-based $ H_{\infty } $ H ∞ MPC approach is used to estimate the system state and suppress disturbance. Furthermore, the estimated state and control input can be optimised by minimising the performance function. Considering that the system state is subject to denial of service (DoS) attacks in the feedback channel, the BBAA approach is used to reconstruct the estimated state, thereby reducing the impact of attacks on system performance. To improve the utilisation of network resources, a set of event-triggering mechanisms (ETMs) is used. In addition, a set of matrix inequality conditions is given to constrain the gains of controllers and observers. Then, the system $ H_{\infty } $ H ∞ performance is demonstrated by applying the average dwell time (ADT) and the Lyapunov function method, and the complex coupling between model switching, DoS attacks and event-triggering is analysed. Simulation results validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal lane-changing and control strategy to solve the collision avoidance of intelligent vehicles in highway driving. A quintic polynomial lane-changing trajectory model is established in the Frenet frame, and the trajectory planning problem is transformed into the optimisation problem of lane-changing target coordinates. To consider the multiple environmental factors on vehicle lane-changing, the feasible region of the lane-changing target point was generated based on the lateral acceleration limitation and the safe distance constraints. The performance evaluation indexes were established, considering efficiency, comfort, lateral slip, and lane offset. The multi-objective optimisation problem is solved in the feasible region, and the optimisation results were applied to the collision detection process. In addition, considering the nonlinear characteristics of tire force caused by sudden lateral movements of vehicles at high-speed, a vehicle dynamics model based on the Magic Formula is established, and Model Predictive Control is applied to track the expected trajectory. Simulation results show that this strategy can plan suitable lane-changing trajectories in high-speed working conditions and achieve vehicle collision avoidance. [ABSTRACT FROM AUTHOR]

When vehicles with traditional passive suspension systems are driving on complex pavement, the large vibration of the body will result in relatively negative effects on ride comfort, vehicle handling, and stability of passengers and goods. Body attitude tracking control based on tandem active–passive suspension can improve vehicle attitude stability and passability by enabling the body attitude to track an ideal position. In addition, the performance limitations of the actuator are considered in the design of the attitude tracking control algorithms. The attitude tracking performances are investigated in both simulations and real car tests. Two control algorithms which adopt linear quadratic regulator (LQR) and model predictive control (MPC) algorithms, are compared and analyzed in terms of theory and control performance. The simulations and real car tests results show that both attitude tracking control algorithms can effectively track the ideal body attitude with acceptable errors under different pavements, and the control effect of MPC is slightly better than that of LQR. In this way, attitude tracking of car body shows a lot of potential when a vehicle is in harsh environments. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven modeling method for lateral motion control of unknown vehicle models. Vehicle's motion can be modeled linearly but this model has complex and nonlinear characteristic. Therefore, it is necessary to know the exact information of the car chassis and requires a knowledge and understanding of dynamics. To solve these drawbacks, we linearly represent full vehicle's lateral dynamics which include nonlinear behavior using dynamic mode decomposition (DMD), one of the data driven modeling methods. To determine the validity of the model obtained using the DMD method, we conducted a simulation of the comparison of the output states between the existing model and the model obtained through DMD modeling, using the scenario of a dynamic maneuver called a double line change during lateral motion of a vehicle. After determination of validation is completed, we designed a lane keeping system by applying a model predictive control to specifically evaluate the model of the proposed method. Performance was derived by comparing the error caused by the vehicle driving on the course with the controller of the simulation. The performance of the proposed approach has been evaluated through simulations and is useful when the model is inaccurate. [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]

This article investigates an observer-based robust model predictive control (RMPC) design to control the uncertain discrete-time linear systems with disturbances. To make a more practical scheme, it is supposed that the uncertain system has been faced with unknown disturbance and input constraints. The proposed RMPC approach is based on a state feedback control design that ensures the H ∞ performance criterion to attenuate disturbance affections. Furthermore, in view of practical application, the control law is constructed based on the estimated states obtained from the Luenberger observer. Based on Lyapunov's theory, the input to state practically stability (ISPS) of the closed-loop system is ensured. Appropriate conditions for the ISPS of the closed-loop system and the estimation error are obtained in terms of online linear matrix inequalities (LMIs) which lead to obtaining the time-varying gain matrices of both controller and observer. Finally, to validate the obtained results, the proposed approach is applied to a numerical example and it is compared with the existing control scheme and the superiority is proved. [ABSTRACT FROM AUTHOR]

A tracking control algorithm for autonomous vehicles using state expansion is proposed. This algorithm, based on a three degrees of freedom (3-DOF) vehicle lateral dynamics model, extends the state variables of the traditional model predictive control (MPC) algorithm to an input-output dual feedback form based on the state expansion method. A dual feedback model predictive control (DF-MPC) algorithm was constructed. Based on this, taking the current speed and the curvature of the reference trajectory as the system input, and using the fuzzy control algorithm to dynamically adjust the prediction range of the DF-MPC trajectory tracking controller in real time, a variable predictive horizon double feedback model predictive control (VDF-MPC) trajectory tracking control method for autonomous vehicle was established. Through MATLAB/Simulink-CarSim joint simulation, the reliability of the established VDF-MPC trajectory tracking control method was verified. [ABSTRACT FROM AUTHOR]

A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS. [ABSTRACT FROM AUTHOR]

Efficient filling strategies for hydrogen fuel cell vehicles are critical for hydrogen utilization efficiency at hydrogen fuelling stations. A novel event-triggered model predictive control framework is proposed in this article for the filling process of a hydrogen fuelling station, which involves multiple compressors, cascade storage tanks, and multiple dispensers. The filling process is formulated as a Mixed-Integer NonLinear Programming (MINLP) problem with the objective of minimizing the vehicle filling times and maximizing the hydrogen utilization efficiency. A solution approach that combines the mixed-integer linear programming and genetic algorithm is designed for solving the resulting MINLP problem. In addition, an event-triggered mechanism is proposed to increase the computational efficiency and to update the control inputs only when needed. Different sets of computational experiments are carried out to demonstrate the effectiveness of the mathematical formulation and the solution approach. [ABSTRACT FROM AUTHOR]

A combined steering and braking collision avoidance control method based on model predictive control is proposed in the present paper. This method can overcome the limitations of single steering or braking collision avoidance and improve vehicle safety and tracking performance in extremely complex conditions. The utilization rate of tire force, path tracking performance, and driving stability are considered in the design of the model predictive control (MPC) optimization objective. In addition, the front wheel angle and four-wheel braking torque are taken as the optimization variables. Simulation results showed that the proposed method can greatly improve the path tracking performance and driving stability of vehicles. [ABSTRACT FROM AUTHOR]

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability. [ABSTRACT FROM AUTHOR]

This work studies the application of a sample-based model Monte Carlo Model Predictive Control (MCMPC) to address discontinuous events, such as collisions. The inclusion of contact dynamics in the prediction steps of MCMPC renders it advantageous for the operation of underwater snake robots in restricted environments during rescue missions. The Curvature Derivative Control (CDC) method is employed for the purpose of controlling the joints, with the exception of the head joint. The snake robot body demonstrates adaptability to various environments with the assistance of the CDC. Results obtained from the simulation demonstrate that the snake robot is capable of autonomously navigating from its beginning position outside the region to a pipe as the entrance in order to get inside. The robot successfully traverses through various internal structures within the pipe and ultimately reaches the intended destination, guided by a predetermined cost function of MCMPC. By using the capabilities of CUDA GPU parallel computing, the computational time is substantially lowered. [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]

Voltage instability in power systems arises due to the shortage of reactive power and may cause abnormally low bus voltages leading to a partial or complete blackout. In order to maintain the system voltages within a safe limit, voltage control techniques such as shunt capacitor banks, Static VAR Compensators (SVCs), load shedding, and transformer tap-changer blocking, are employed. In this paper, a novel receding-horizon Model Predictive Control (MPC)-based voltage controller is proposed, which, by optimally controlling generator reactive power and SVC output, maintains the voltage stability of a power system. For this, a sensitivity-based analysis is performed to design a state-space model of the power system. The frequency and voltage dependency of load and generation are considered in the system equations. The voltage control is done step-wise, and the optimal control action in each step is calculated by minimizing a cost function subject to a set of relevant constraints. Different Voltage Stability Indices (VSIs) are used as a measure of voltage stability and also used in the constraints for the optimization problem. The performance of the proposed controller is evaluated on IEEE 9-, 39- and 118-bus systems, considering different types of loads and contingencies. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the switching frequency is not fixed and the number of samples is large when using the traditional finite control set-model predictive control (FCS-MPC) method for a five-level inverter with coupled inductors, an improved model prediction method is proposed. This improved method combines the space vector pulse width modulation and the minimum current error optimization principle to design the modulation strategy, and combines the modulation function with the evaluation function to obtain a modulation function that satisfies the minimum current error. Compared with the traditional finite set model prediction method, the improved method can realize the balance of the coupled inductor current without measuring the coupled inductor current, can fix the switching frequency of power switches, and retains the rapidity of the traditional model predictive control. The effectiveness of the proposed method is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

The tracking of optimal current trajectories in an interior permanent magnet (IPM) synchronous machine is traditionally realized by a complex online tracking, inaccurate analytical equations, or tiresome offline calibration methods. This paper proposes a modified hybrid feed-forward/feedback flux-weakening algorithm for IPM synchronous machines. In this paper, the utilized power converter is a standard T-type multilevel inverter, and hence, the voltage and current harmonics contents of the conventional two-level inverter are improved. The control algorithm utilizes the optimal current profile for maximum torque per ampere (MTPA) operation and a voltage regulation (VR) feedback control for efficient flux-weakening operation. For infinite-speed IPM machine drives, the d-q current components are limited to follow the maximum torque per voltage (MTPV) trajectory. A low-complexity model predictive control (MPC) is employed to minimize the conflict that arises from using cascaded PI control loops for current and speed control. The performance of the drive is investigated based on the Prius 2004 IPM parameters. Extensive simulation scenarios were performed using the MATLAB/SIMULINK which validates the effectiveness of the proposed algorithm. Real-time simulations based on the dSPACE DS 1103 platform are conducted to confirm the system validity for real hardware implementation. The proposed IPM drive system proves simple structure, fast response, and low harmonic contents. [ABSTRACT FROM AUTHOR]

The classical model predictive control (MPC) usually aims to minimize the current error at the end of the control period. This method is equivalent to minimizing the current total harmonic distortion (THD) when the control period tends to be infinitely small. However, due to the limitations of hardware in practice, the optimization effect of current THD is not obvious. Aiming at the above problem, an improved double-vector model predictive control method is proposed for the single-phase active-neutral-point-clamped five-level (ANPC-5L) inverter. Two voltage vectors in each control cycle are used in this method to reduce the current THD by minimizing the area enclosed by the actual and reference current. In addition, a capacitor voltage control method is designed for the problem of multiple control objectives coupling in this structure. In this method, an idea of hierarchy is designed for voltage jump and switching transition limits, neutral point (NP) voltage, and flying capacitor (FC) voltage, achieving the effect of weightless factor and good dynamic and static performance. Finally, the effectiveness of the proposed method is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

In this article, a resilient model predictive control algorithm incorporating a dual‐mode control strategy is proposed for a class of cyber‐physical systems subject to state and input constraints, additive disturbances and denial‐of‐service (DoS) attacks. An adversary aims to disrupt the controller‐to‐actuator channel by deploying adversarial jamming signals. By exploring the prediction uncertainty between a nominal model and an actual model and utilizing a constraint tightening technique, a novel set of constraints is developed, which guarantees robust constraint satisfaction in open‐loop mode caused by DoS attacks. A robust positive invariant set is designed, which allows the control mode to switch from rolling optimization to a state‐feedback control law when the state enters its interior, thus saving computational resources. We explicitly determine a critical duration for DoS attacks and, through theoretical analysis, establish that when the duration of DoS attacks falls below this critical value, the proposed algorithm ensures guaranteed recursive feasibility and robust asymptotic stability. Finally, some comparisons are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a centralized disturbance suppression strategy for distributed drive electric vehicles which is based on model predictive direct motion control. This strategy is capable of addressing issues such as parameter uncertainties and external disturbances in vehicles. Firstly, the paper provides a brief introduction to model predictive direct motion control. Secondly, it analyzes the impact of vehicle parameter uncertainties and external disturbances on the mathematical model. Finally, a centralized disturbance suppression strategy based on a sliding mode observer is proposed. Simulation results demonstrate that this strategy exhibits excellent disturbance rejection capabilities. [ABSTRACT FROM AUTHOR]

In limit conditions, autonomous vehicles face the risk of lateral instability. The integrated control of steering and braking, an important measure for improving the stability of autonomous vehicles, has been extensively studied. A novel steering and braking integrated model predictive path tracking control (PTC) based on a dimension reduction model is proposed in this study. This method aims at the dilemma of the real-time limitation of the current integrated model predictive PTC based on nonlinear vehicle dynamics in practical applications and the unsatisfactory control effect of the integrated model predictive PTC based on the linearised vehicle dynamics in limit conditions. The core concept of this study is to reduce the input dimension of the integrated controller model by designing a model dimension reduction method, thereby reducing the decision variables of the optimisation problem and improving the real-time performance. The model dimension reduction method is designed based on the optimal utilisation of tire force to ensure the control performance of the proposed integrated control method in limit conditions. The integrated control method based on the dimension reduction model is compared with several existing integrated control methods in limit conditions to demonstrate its validity and superiority. The simulation tests with Simulink and CarSim indicate that the proposed method can reduce the calculation time by more than 40 $ \% $ % on the premise of ensuring path tracking accuracy and vehicle stability in limit conditions. Moreover, the hardware-in-the-loop tests prove the practicability of the presented method. [ABSTRACT FROM AUTHOR]

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

Summary: Ensuring nominal asymptotic stability of the nonlinear model predictive control (NMPC) controller is not trivial. Stabilizing ingredients such as terminal penalty term and Terminal Region (TR) are crucial in establishing the asymptotic stability. Approaches available in the literature provide limited degrees of freedom for the characterization of the TR for the discrete time quasi infinite horizon NMPC formulation. Current work presents alternate approaches namely arbitrary controller based approach and linear quadratic regulator (LQR) based approach, which provide larger degrees of freedom for enlarging the TR. Both the approaches are scalable to system of any dimension. Approach from the literature provides a scalar whereas proposed approaches provide two additive matrices as tuning parameters for shaping of the TR. Proposed approaches involve solving modified Lyapunov equations to compute terminal penalty term, followed by explicit characterization of the TR. Efficacy of the proposed approaches is demonstrated using benchmark two state system. TR obtained using the arbitrary controller based approach and LQR based approach are approximately 10.4723 and 9.5055 times larger by area measure when compared to the largest TR obtained using the approach from the literature. As a result, there is significant reduction in the prediction horizon length while retaining the feasibility of the controller. [ABSTRACT FROM AUTHOR]

The time-varying delay is a peculiar phenomenon that occurs in almost all systems. It can cause numerous problems and instability during system operation. In this paper, the time-varying delay is considered in both the states and input vectors, which is a significant distinction between the proposed method here and previous algorithms. Furthermore, the time-varying delay is unknown but bounded. To address this issue, the Razumikhin approach is applied to the proposed method, as it incorporates a Lyapunov function with the original non-augmented state space of the system models, in contrast to the Krasovskii formula. Moreover, the Razumikhin method performs better and avoids the inherent complexity of the Krasovskii method, particularly when dealing with large delays and disturbances. For achieving output stabilization, the model predictive control (MPC) is designed for the system. The considered system in this paper is an interval type-2 (IT2) fuzzy T-S model, which provides a more accurate estimation of the dynamic model of the system. The online optimization problems are solved using linear matrix inequalities (LMIs), which reduces the computational burden and online computational costs compared to offline and non-LMI approaches. Finally, an example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

As an efficient, clean and pollution-free power generation device, proton exchange membrane fuel cell (PEMFC) has been widely applied in transportation, distributed power generation and other fields. However, the performance of PEMFC cannot be separated from appropriate cathode gas supply, for example, it is prone to oxygen starvation under frequent variable load conditions. This is due to the mismatch between the air supply rate and the electrochemical reaction rate, making it difficult for the system to meet the air flow requirement instantaneously when subject to the load changes abruptly. Thus, oxygen starvation control has become a particularly challenging nonlinear control problem because of the great difficulty in achieving an accurate system identification model and an efficient controller. To this end, a long short-term memory (LSTM) neural network-based model predictive control (MPC) is developed to model and control the PEMFC air supply system, which combines the advantages of LSTM and MPC. Firstly, LSTM is utilized to train an online control-oriented model from the measured dataset. Secondly, a sparrow search algorithm (SSA) is utilized to update the hyper-parameters of LSTM model, which can obtain more accurate predictive model in MPC. Thirdly, the MPC with LSTM-SSA model is solved online using different high efficiency solvers. Fourthly, the proposed LSTM-SSA based on MPC is adopted to model and control a PEMFC air supply system. Finally, the stability proof of the proposed method is illustrated in the Appendix. The simulation results reveal that the data-driven learning method and MPC method have significant advantages in modeling and improving the system performance. • Machine learning-based MPC framework is proposed to regulate air supply system. • LSTM as the predictive model in MPC is developed to describe the dynamic system. • Sparrow search algorithm is used to update the hyper-parameters of the LSTM model. • The control effects of proposed controller based on different solvers are compared. [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]