This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
In recent years,there has been a growing demand for more efficient and robust control strategies in cooperative multi-robot systems.This paper introduces the cascade explicit tube model predictive controller(CET-MPC),...In recent years,there has been a growing demand for more efficient and robust control strategies in cooperative multi-robot systems.This paper introduces the cascade explicit tube model predictive controller(CET-MPC),a control architecture designed specifically for distributed aerial robot systems.By integrating an explicit model predictive controller(MPC)with a tube MPC,our approach significantly reduces online computational demands while enhancing robustness against disturbances such as wind and measurement noise,as well as uncertainties in inertia parameters.Further,we incorporate a cascade controller to minimize steady-state errors and improve system performance dynamically.The results of this assessment provide valuable insights into the effectiveness and reliability of the CET-MPC approach under realistic operating conditions.The simulation results of flight scenarios for multi-agent quadrotors demonstrate the controller’s stability and accurate tracking of the desired path.By addressing the complexities of quadrotors’six degrees of freedom,this controller serves as a versatile solution applicable to a wide range of multi-robot systems with varying degrees of freedom,demonstrating its adaptability and scalability beyond the quadrotor domain.展开更多
Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands...Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set a...This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties.Assisted with enhanced robust tubes,the chance constraints are then formulated into a deterministic form.To alleviate the online computational burden,a novel event-triggered stochastic model predictive control is developed,where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance.Two triggering parametersσandγare used to adjust the frequency of solving the optimization problem.The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined.Finally,numerical studies on the control of a heating,ventilation,and air conditioning(HVAC)system confirm the efficacy of the proposed control.展开更多
Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynam...Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynamics control.For this purpose,this paper studies the DYC through the Takagi-Sugeno(T-S)fuzzy-based model predictive control to deal with the nonlinear challenge.First,a T-S fuzzy-based vehicle dynamics model is established to describe the time-varying tire cornering stiffness and vehicle speeds,and thus the uncertain parameters can be represented by the norm-bounded uncertainties.Then,a robust model predictive control(MPC)is developed to guarantee vehicle handling stability.A feasible solution can be obtained through a set of linear matrix inequalities(LMIs).Finally,the tests are conducted by the Carsim/Simulink joint platform to verify the proposed method.The comparative results show that the proposed strategy can effectively guarantee the vehicle’s lateral stability while handling the nonlinear challenge.展开更多
The existing research on the path following of the autonomous electric vehicle(AEV)mainly focuses on the path planning and the kinematic control.However,the dynamic control with the state observation and the communica...The existing research on the path following of the autonomous electric vehicle(AEV)mainly focuses on the path planning and the kinematic control.However,the dynamic control with the state observation and the communication delay is usually ignored,so the path following performance of the AEV cannot be ensured.This article studies the observer-based path following control strategy for the AEV with the communication delay via a robust explicit model predictive control approach.Firstly,a projected interval unscented Kalman filter is proposed to observe the vehicle sideslip angle and yaw rate.The observer considers the state constraints during the observation process,and the robustness of the observer is also considered.Secondly,an explicit model predictive control is designed to reduce the computational complexity.Thirdly,considering the efficiency of the information transmission,the influence of the communication delay is considered when designing the observer-based path following control strategy.Finally,the numerical simulation and the hardware-in-the-loop test are conducted to examine the effectiveness and practicability of the proposed strategy.展开更多
To achieve the track following and collision avoidance of underactuated unmanned surface vehicle(USV),autonomous navigation model based on model predictive control is established by including the track offset,speed va...To achieve the track following and collision avoidance of underactuated unmanned surface vehicle(USV),autonomous navigation model based on model predictive control is established by including the track offset,speed variation and rule compliance as the evaluation functions and including the ship domain of dynamic/static navigation obstacles and the mechanical characteristics limitation as constraints.The effectiveness of the model for autonomous navigation of USV in the situation of multi-ship encounters and in the complex waters with both dynamic and static obstructions is verified by several groups of simulation work.The simulation results show that the proposed model can realize the autonomous navigation of the underactuated USV under the complex waters.展开更多
This paper aims to fuse two well-established and,at the same time,opposed control techniques,namely,model predictive control(MPC)and active disturbance rejection control(ADRC),to develop a dynamic motion controller fo...This paper aims to fuse two well-established and,at the same time,opposed control techniques,namely,model predictive control(MPC)and active disturbance rejection control(ADRC),to develop a dynamic motion controller for a laser beam steering system.The proposed technique uses the ADRC philosophy to lump disturbances and model uncertainties into a total disturbance.Then,the total disturbance is estimated via a discrete extended state disturbance observer(ESO),and it is used to(1)handle the system constraints in a quadratic optimization problem and(2)injected as a feedforward term to the plant to reject the total disturbance,together with the feedback term obtained by the MPC.The main advantage of the proposed approach is that the MPC is designed based on a straightforward integrator-chain model such that a simple convex optimization problem is performed.Several experiments show the real-time closed-loop performance regarding trajectory tracking and disturbance rejection.Owing to simplicity,the self-contained approach MPC+ESO becomes a Frugal MPC,which is computationally economical,adaptable,efficient,resilient,and suitable for applications where on-board computational resources are limited.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanis...Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.展开更多
Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified M...Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.展开更多
A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraint...A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.展开更多
In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guar...In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.展开更多
Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature ...Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions.Therefore,a path tracking robust control strategy based on force-driven H_(∞)and MPC is proposed.To fully exploit the nonlinear dynamics characteristics of tires,a force-driven state space model of a path tracking system based on a linear time-varying tire model is established;the H_(∞)and MPC methods are used to design a robust controller.Considering disturbance and system state constraints,the robust control constraint model based on LMI is established.Finally,the proposed controller is validated through joint simulations using CarSim and MATLAB.The results show that the maximum lateral deviation is reduced by 17.07%,and the maximum course angle deviation is reduced by 13.04%under large curvature disturbance conditions.The maximum lateral deviation is reduced by 27.85%,and the maximum course angle deviation is reduced by 31.17%under conditions of uncertain road adhesion coefficients.Based on the controller’s performance,the proposed controller effectively mitigates modeling errors,parameter uncertainties,and curvature disturbances.展开更多
This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strateg...This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strategy with constraints is proposed for automatic landing control of the aircraft.First,the long short-term memory(LSTM)neural network is used to calculate the adaptive reference trajectories of the aircraft.Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator.Second,an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory.Moreover,some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC.Finally,the simulation results of F/A-18A aircraft show that compared with the conventional MPC,the presented MPC strategy improves the computational efficiency by nearly 56%and satisfies the control performance requirements of carrier landing.展开更多
This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed ra...This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.展开更多
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
文摘In recent years,there has been a growing demand for more efficient and robust control strategies in cooperative multi-robot systems.This paper introduces the cascade explicit tube model predictive controller(CET-MPC),a control architecture designed specifically for distributed aerial robot systems.By integrating an explicit model predictive controller(MPC)with a tube MPC,our approach significantly reduces online computational demands while enhancing robustness against disturbances such as wind and measurement noise,as well as uncertainties in inertia parameters.Further,we incorporate a cascade controller to minimize steady-state errors and improve system performance dynamically.The results of this assessment provide valuable insights into the effectiveness and reliability of the CET-MPC approach under realistic operating conditions.The simulation results of flight scenarios for multi-agent quadrotors demonstrate the controller’s stability and accurate tracking of the desired path.By addressing the complexities of quadrotors’six degrees of freedom,this controller serves as a versatile solution applicable to a wide range of multi-robot systems with varying degrees of freedom,demonstrating its adaptability and scalability beyond the quadrotor domain.
文摘Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
基金supported by the National Nature Science Foundation of China(62073194)the Natural Science Foundation of Shandong Province of China(ZR2023MF028)the Taishan Scholars Program of Shandong Province(tsqn202312008)
文摘This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI)systems under additive stochastic disturbances.It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties.Assisted with enhanced robust tubes,the chance constraints are then formulated into a deterministic form.To alleviate the online computational burden,a novel event-triggered stochastic model predictive control is developed,where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance.Two triggering parametersσandγare used to adjust the frequency of solving the optimization problem.The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined.Finally,numerical studies on the control of a heating,ventilation,and air conditioning(HVAC)system confirm the efficacy of the proposed control.
基金Supported by National Natural Science Foundation of China(Grant Nos.52402497,52025121 and 52002066)Young Scientists Project and General Project of Applied Basic Research in Yunnan Province(Grant Nos.202501AT070296,202401AU070196)+1 种基金The Key Laboratory of Modern Agricultural Engineering of Ordinary Colleges and Universities of Education Department of Autonomous Region(Grant No.TDNG2023108)Jiangsu Provincial Achievements Transformation Project(Grant No.BA2018023).
文摘Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynamics control.For this purpose,this paper studies the DYC through the Takagi-Sugeno(T-S)fuzzy-based model predictive control to deal with the nonlinear challenge.First,a T-S fuzzy-based vehicle dynamics model is established to describe the time-varying tire cornering stiffness and vehicle speeds,and thus the uncertain parameters can be represented by the norm-bounded uncertainties.Then,a robust model predictive control(MPC)is developed to guarantee vehicle handling stability.A feasible solution can be obtained through a set of linear matrix inequalities(LMIs).Finally,the tests are conducted by the Carsim/Simulink joint platform to verify the proposed method.The comparative results show that the proposed strategy can effectively guarantee the vehicle’s lateral stability while handling the nonlinear challenge.
基金Supported by the National Key Research and Development Program of China(Grant No.2023YFE0204700)the National Natural Science Foundation of China(Grant Nos.52472402 and 52302469)+7 种基金the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023A1515012327 and 2024A1515010449)the research grant of the University of Macao(Grant No.MYRG GRG2023-00235-FST-UMDF)Shandong Provincial Natural Science Foundation(Grant No.ZR2023ME133)the Fundamental Research Funds for the Central Universities(Grant No.N2403012)the Science and Technology Development Fund of Macao SAR(Grant No.0091/2023/AMJ)the China Postdoctoral Science Foundation(Grant Nos.2023M740538 and AM2024003)the Zhuhai Science and Technology Innovation Bureau(Grant No.2220004003107)the Yunfu Science and Technology Project(Grant No.2024090202).
文摘The existing research on the path following of the autonomous electric vehicle(AEV)mainly focuses on the path planning and the kinematic control.However,the dynamic control with the state observation and the communication delay is usually ignored,so the path following performance of the AEV cannot be ensured.This article studies the observer-based path following control strategy for the AEV with the communication delay via a robust explicit model predictive control approach.Firstly,a projected interval unscented Kalman filter is proposed to observe the vehicle sideslip angle and yaw rate.The observer considers the state constraints during the observation process,and the robustness of the observer is also considered.Secondly,an explicit model predictive control is designed to reduce the computational complexity.Thirdly,considering the efficiency of the information transmission,the influence of the communication delay is considered when designing the observer-based path following control strategy.Finally,the numerical simulation and the hardware-in-the-loop test are conducted to examine the effectiveness and practicability of the proposed strategy.
基金the National Natural Science Foundation of China(No.51879119)the Key Projects of National Key Research and Development Program(No.2021YFB390150)+1 种基金the Natural Science Project of Fujian Province(Nos.2022J01323,2021J01822 and 2020J01660)the Fuzhou-Xiamen-Quanzhou Independent Innovation Region Cooperated Special Foundation(No.3502ZCQXT2021007)。
文摘To achieve the track following and collision avoidance of underactuated unmanned surface vehicle(USV),autonomous navigation model based on model predictive control is established by including the track offset,speed variation and rule compliance as the evaluation functions and including the ship domain of dynamic/static navigation obstacles and the mechanical characteristics limitation as constraints.The effectiveness of the model for autonomous navigation of USV in the situation of multi-ship encounters and in the complex waters with both dynamic and static obstructions is verified by several groups of simulation work.The simulation results show that the proposed model can realize the autonomous navigation of the underactuated USV under the complex waters.
基金support through his Master scholarshipThe Vicerrectoría de Investigación y Estudios de Posgrado(VIEP-BUAP)partially funded this work under grant number 00593-PV/2025.
文摘This paper aims to fuse two well-established and,at the same time,opposed control techniques,namely,model predictive control(MPC)and active disturbance rejection control(ADRC),to develop a dynamic motion controller for a laser beam steering system.The proposed technique uses the ADRC philosophy to lump disturbances and model uncertainties into a total disturbance.Then,the total disturbance is estimated via a discrete extended state disturbance observer(ESO),and it is used to(1)handle the system constraints in a quadratic optimization problem and(2)injected as a feedforward term to the plant to reject the total disturbance,together with the feedback term obtained by the MPC.The main advantage of the proposed approach is that the MPC is designed based on a straightforward integrator-chain model such that a simple convex optimization problem is performed.Several experiments show the real-time closed-loop performance regarding trajectory tracking and disturbance rejection.Owing to simplicity,the self-contained approach MPC+ESO becomes a Frugal MPC,which is computationally economical,adaptable,efficient,resilient,and suitable for applications where on-board computational resources are limited.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
基金supported by the key project of the National Nature Science Foundation of China(51736002).
文摘Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.
基金supported by the National Natural Science Foundation of China under Grant 5227705。
文摘Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.
基金Project supported by the National Natural Science Foundation of China(Nos.62273245 and 62173033)the Sichuan Science and Technology Program of China(No.2024NSFSC1486)the Opening Project of Robotic Satellite Key Laboratory of Sichuan Province of China。
文摘A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.
基金supported by the National Natural Science Foundation of China (62073015,62173036,62122014)。
文摘In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.
基金Supported by Qinghai University Youth Research Fund,China(Grant No.2023-QGY-15)。
文摘Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions.Therefore,a path tracking robust control strategy based on force-driven H_(∞)and MPC is proposed.To fully exploit the nonlinear dynamics characteristics of tires,a force-driven state space model of a path tracking system based on a linear time-varying tire model is established;the H_(∞)and MPC methods are used to design a robust controller.Considering disturbance and system state constraints,the robust control constraint model based on LMI is established.Finally,the proposed controller is validated through joint simulations using CarSim and MATLAB.The results show that the maximum lateral deviation is reduced by 17.07%,and the maximum course angle deviation is reduced by 13.04%under large curvature disturbance conditions.The maximum lateral deviation is reduced by 27.85%,and the maximum course angle deviation is reduced by 31.17%under conditions of uncertain road adhesion coefficients.Based on the controller’s performance,the proposed controller effectively mitigates modeling errors,parameter uncertainties,and curvature disturbances.
基金National Defense Science and Technology Innovation Project(No.2022-4b5s-wwht-0041)。
文摘This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints,deck motion,measurement noises,and unknown disturbances.The iterative model predictive control(MPC)strategy with constraints is proposed for automatic landing control of the aircraft.First,the long short-term memory(LSTM)neural network is used to calculate the adaptive reference trajectories of the aircraft.Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator.Second,an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory.Moreover,some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC.Finally,the simulation results of F/A-18A aircraft show that compared with the conventional MPC,the presented MPC strategy improves the computational efficiency by nearly 56%and satisfies the control performance requirements of carrier landing.
基金National Science and Technology Council,Taiwan,for financially supporting this research(Grant No.NSTC 113-2221-E-018-011)Ministry of Education’s Teaching Practice Research Program,Taiwan(PSK1120797 and PSK1134099).
文摘This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.
