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
The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achiev...The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regiona...In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regional controllability is more adapted to systems described by dynamic systems.Regional controllability results in a strategic area were established for vibrating plates by the Hilbertian Uniqueness Method.展开更多
在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优...在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优化机制,使控制器显示耦合至上一控制周期的车辆状态空间,有效避免预测时域变化造成的多步优化问题解耦效应,抑制因控制输入突变对轨迹跟踪控制任务的影响.结合两种优化方法,有效改善固定预测时域策略在高曲率轨迹跟踪中因累计误差造成的跟踪精度下降问题.最后,基于MATLAB/Simulink-CarSim联合仿真平台对算法进行了验证.经计算,高速单移线工况下,该方法在侧向偏差均值/峰值、纵向偏差均值/峰值、航向偏差均值/峰值指标中,相较于固定预测时域NMPC同比降低36.17%/15.25%、11.55%/38.58%、6.13%/25.27%;高速双移线工况下,同比降低30.28%/29.77%、25.07%/3.85%、11.02%/2.68%.此外,在高速低附着工况中,该方法仍能保证良好的控制精度及侧向稳定性,其峰值侧向偏差为0.2017 m、峰值纵向偏差为0.9744 km h^(-1)、峰值航向偏差为1.1936°、峰值质心侧偏角为1.9074°.展开更多
目前已有一些针对路径跟踪控制中信号时滞问题的研究工作,但这些工作大多针对某种特定的控制方法,而在路径跟踪控制方法中,非线性模型预测控制(Nonlinear model predictive control,NMPC)具有能够显式处理系统约束、便于实现多目标优化...目前已有一些针对路径跟踪控制中信号时滞问题的研究工作,但这些工作大多针对某种特定的控制方法,而在路径跟踪控制方法中,非线性模型预测控制(Nonlinear model predictive control,NMPC)具有能够显式处理系统约束、便于实现多目标优化、能够有效利用被控对象前方参考路径信息等优势,但是针对NMPC路径跟踪控制系统中时滞问题的研究较不成熟,制约了这种控制方法的实际应用.为解决上述问题,开展了以下研究工作.首先构建了能够较好地孤立出时滞影响的类车机器人路径跟踪控制系统.接着分析了信号时滞对NMPC路径跟踪控制系统的影响机理,即时滞会导致控制器产生的控制信号不能适应类车机器人在执行控制信号时所处的位置.然后提出了基于增长NMPC预测时域的时滞影响消减方法,即在迭代周期不变的情况下,在无时滞系统较优预测步数的基础上增加二倍时滞周期比以上的整数.最后通过计算机仿真和实验验证了提出方法的有效性.仿真和实验结果表明,信号时滞对NMPC路径跟踪控制系统存在影响,未考虑时滞的NMPC控制算法能够在无时滞系统中实现高精确性路径跟踪,而在有时滞系统中控制失效.通过增长预测时域可以有效消减信号时滞的影响,在信号时滞约为0.2 s的仿真与实验系统中,基于该方法的NMPC控制器可以保证路径跟踪控制的位移误差幅值不超过0.1258 m,航向误差幅值不超过0.0583 rad.展开更多
Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model ...Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.展开更多
The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and...The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.展开更多
Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource explorat...Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration.Recent advances in the field of communication technologies,perception capability,computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs.In order to deploy the constrained AMVs in the complex dynamic maritime environment,it is crucial to enhance the guidance and control capabilities through effective and practical planning,and control algorithms.Model predictive control(MPC)has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance.This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC.Finally,future research trends and directions in this substantial research area of AMVs are highlighted.展开更多
The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonli...The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework.The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible;and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions.Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system.Recursive feasibility and closed-loop stability of this NMPC are established.The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator(LQR) method.展开更多
In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the v...In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated： the first states that it is sometimes better to use less efficient （per iteration） optimizer if the lack of efficiency is overmcompensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.展开更多
Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal ...Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.展开更多
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Supported by National Natural Science Foundation of China(Grant Nos.52225212,52272418,U22A20100)National Key Research and Development Program of China(Grant No.2022YFB2503302).
文摘The development of chassis active safety control technology has improved vehicle stability under extreme conditions.However,its cross-system and multi-functional characteristics make the controller difficult to achieve cooperative goals.In addition,the chassis system,which has high complexity,numerous subsystems,and strong coupling,will also lead to low computing efficiency and poor control effect of the controller.Therefore,this paper proposes a scenario-driven hybrid distributed model predictive control algorithm with variable control topology.This algorithm divides multiple stability regions based on the vehicle’s β−γ phase plane,forming a mapping relationship between the control structure and the vehicle’s state.A control input fusion mechanism within the transition domain is designed to mitigate the problems of system state oscillation and control input jitter caused by switching control structures.Then,a distributed state-space equation with state coupling and input coupling characteristics is constructed,and a weighted local agent cost function in quadratic programming is derived.Through cost coupling,local agents can coordinate global performance goals.Finally,through Simulink/CarSim joint simulation and hardware-in-the-loop(HIL)test,the proposed algorithm is validated to improve vehicle stability while ensuring trajectory tracking accuracy and has good applicability for multi-objective coordinated control.This paper combines the advantages of distributed MPC and decentralized MPC,achieving a balance between approximating the global optimal results and the solution’s efficiency.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
文摘In this paper,we define for the trace operator,the solution of certain models of vibrating plates standards with initial data in a strategic region spaces of weak regularities.Indeed,we know that the notion of regional controllability is more adapted to systems described by dynamic systems.Regional controllability results in a strategic area were established for vibrating plates by the Hilbertian Uniqueness Method.
文摘在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优化机制,使控制器显示耦合至上一控制周期的车辆状态空间,有效避免预测时域变化造成的多步优化问题解耦效应,抑制因控制输入突变对轨迹跟踪控制任务的影响.结合两种优化方法,有效改善固定预测时域策略在高曲率轨迹跟踪中因累计误差造成的跟踪精度下降问题.最后,基于MATLAB/Simulink-CarSim联合仿真平台对算法进行了验证.经计算,高速单移线工况下,该方法在侧向偏差均值/峰值、纵向偏差均值/峰值、航向偏差均值/峰值指标中,相较于固定预测时域NMPC同比降低36.17%/15.25%、11.55%/38.58%、6.13%/25.27%;高速双移线工况下,同比降低30.28%/29.77%、25.07%/3.85%、11.02%/2.68%.此外,在高速低附着工况中,该方法仍能保证良好的控制精度及侧向稳定性,其峰值侧向偏差为0.2017 m、峰值纵向偏差为0.9744 km h^(-1)、峰值航向偏差为1.1936°、峰值质心侧偏角为1.9074°.
基金co-supported by the National Natural Science Foundation of China(Nos.61225015,61105092,61422102,and 61703040)the Beijing Natural Science Foundation,China(No.4161001)the China Postdoctoral Science Foundation(No.2017M620640)。
文摘Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.
基金co-supported by the National Natural Science Foundation of China(Nos.61803009,61903084)Fundamental Research Funds for the Central Universities of China(No.YWF-20-BJ-J-542)Aeronautical Science Foundation of China(No.20175851032)。
文摘The paper proposes a new swarm intelligence-based distributed Model Predictive Control(MPC)approach for coordination control of multiple Unmanned Aerial Vehicles(UAVs).First,a distributed MPC framework is designed and each member only shares the information with neighbors.The Chaotic Grey Wolf Optimization(CGWO)method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem(FHOCP).Then,the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint.Further,an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach,which considers the predicted state errors and the convergence of cost function.Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.
基金supported by the Natural Sciences and Engineering Research Council of Canada(NSERC)。
文摘Autonomous marine vehicles(AMVs)have received considerable attention in the past few decades,mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration.Recent advances in the field of communication technologies,perception capability,computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs.In order to deploy the constrained AMVs in the complex dynamic maritime environment,it is crucial to enhance the guidance and control capabilities through effective and practical planning,and control algorithms.Model predictive control(MPC)has been exceptionally successful in different fields due to its ability to systematically handle constraints while optimizing control performance.This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC.Finally,future research trends and directions in this substantial research area of AMVs are highlighted.
基金supported by the National Natural Science Foundation of China(613741 11)Zhejiang Provincial Natural Science Foundation of China(LR17F030004)
文摘The paper presents a new dual-mode nonlinear model predictive control(NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control.The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework.The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible;and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions.Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system.Recursive feasibility and closed-loop stability of this NMPC are established.The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator(LQR) method.
文摘In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated： the first states that it is sometimes better to use less efficient （per iteration） optimizer if the lack of efficiency is overmcompensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.
基金supported by the National Natural Science Foundation of China(51875061)China Scholarship Council(202206050107)。
文摘Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.
