In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering th...In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.展开更多
Signal filtering and differential acquisition are classic yet challenging issues in control engineering.The discrete-time optimal control(DTOC)based on classic tracking differentiator(TD)can effectively extract differ...Signal filtering and differential acquisition are classic yet challenging issues in control engineering.The discrete-time optimal control(DTOC)based on classic tracking differentiator(TD)can effectively extract differentiation signals and filter signals,while eliminating the chattering problem that arises during the discretization of the continuous solution.However,under external disturbance,the convergence mode may change,leading to overshoot and noise amplification.In this paper,a dual-switching strategy is proposed,which can alternate between the base double-integral system and its dual system according to the quadrant of the system’s state.And a novel linearized control law is also introduced,deriving a novel dual-switch tracking differentiator.Further analysis of system convergence and time optimality is provided.Simulation results show that the application of this dual-switching strategy notably reduces overshoot in both tracking and differential signals while enhancing noise filtering performance.Moreover,experiments conducted on a permanent magnet synchronous motor(PMSM)platform,where the proposed TD acts as a filter in the speed feedback loop,demonstrate that the standard deviation between the reference speed and the target speed(at a constant speed of 378 r/min)decreased from 5.63 r/min to 4.93 r/min,compared to the moving average algorithm.展开更多
In order to accelerate the convergence speed of iterative learning control(ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear ...In order to accelerate the convergence speed of iterative learning control(ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is precorrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.展开更多
针对观测器估计精度偏低及高速列车系统的强耦合、受外界扰动、参数时变等问题,提出一种基于补偿函数观测器的分数阶非奇异快速终端滑模控制算法(Compensating Function Observer-Fractional Order Non-singular Fast Terminal Sliding ...针对观测器估计精度偏低及高速列车系统的强耦合、受外界扰动、参数时变等问题,提出一种基于补偿函数观测器的分数阶非奇异快速终端滑模控制算法(Compensating Function Observer-Fractional Order Non-singular Fast Terminal Sliding Mode Control,CFO-FONFTSMC),以提高高速列车速度控制的鲁棒性和控制精度.首先,建立高速列车纵向多质点动力学模型,设计高精度的补偿函数观测器对系统的总扰动进行实时估计并补偿;然后,设计一种带状态负指数控制律的分数阶非奇异快速终端滑模控制算法,用于对列车的运行曲线进行跟踪控制,并通过李雅普诺夫稳定性理论证明系统在有限时间内的收敛性;最后,以CRH3型高速列车参数和合肥站-蚌埠南站的实际线路为实例,分别跟踪理想运行曲线和节能优化运行曲线进行实验验证.仿真结果表明:所提算法跟踪理想运行速度曲线的平均误差为0.01377 km/h,跟踪带干扰的节能优化运行速度曲线的平均误差为0.0364 km/h,相较于基于扩张状态观测器的滑模和非奇异快速终端滑模控制方法,所提方法具有最小的跟踪误差和更高的跟踪精度,验证了其有效性和可行性,可为列车速度跟踪控制领域的研究提供参考.展开更多
基金Project(20180608005600843855-19)supported by the International Graduate Exchange Program of Beijing Institute of Technology,China。
文摘In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.
基金Project(QZKFKT2023-012)supported by the State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive,China。
文摘Signal filtering and differential acquisition are classic yet challenging issues in control engineering.The discrete-time optimal control(DTOC)based on classic tracking differentiator(TD)can effectively extract differentiation signals and filter signals,while eliminating the chattering problem that arises during the discretization of the continuous solution.However,under external disturbance,the convergence mode may change,leading to overshoot and noise amplification.In this paper,a dual-switching strategy is proposed,which can alternate between the base double-integral system and its dual system according to the quadrant of the system’s state.And a novel linearized control law is also introduced,deriving a novel dual-switch tracking differentiator.Further analysis of system convergence and time optimality is provided.Simulation results show that the application of this dual-switching strategy notably reduces overshoot in both tracking and differential signals while enhancing noise filtering performance.Moreover,experiments conducted on a permanent magnet synchronous motor(PMSM)platform,where the proposed TD acts as a filter in the speed feedback loop,demonstrate that the standard deviation between the reference speed and the target speed(at a constant speed of 378 r/min)decreased from 5.63 r/min to 4.93 r/min,compared to the moving average algorithm.
基金supported by the National Natural Science Foundation of China(51777170)the Aeronautical Science Foundation of China(20162853026)the Project Supported by Natural Science Basic Research Plan in Shannxi Province of China(2019JM-462,2020JM-151)。
文摘In order to accelerate the convergence speed of iterative learning control(ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is precorrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.
文摘针对观测器估计精度偏低及高速列车系统的强耦合、受外界扰动、参数时变等问题,提出一种基于补偿函数观测器的分数阶非奇异快速终端滑模控制算法(Compensating Function Observer-Fractional Order Non-singular Fast Terminal Sliding Mode Control,CFO-FONFTSMC),以提高高速列车速度控制的鲁棒性和控制精度.首先,建立高速列车纵向多质点动力学模型,设计高精度的补偿函数观测器对系统的总扰动进行实时估计并补偿;然后,设计一种带状态负指数控制律的分数阶非奇异快速终端滑模控制算法,用于对列车的运行曲线进行跟踪控制,并通过李雅普诺夫稳定性理论证明系统在有限时间内的收敛性;最后,以CRH3型高速列车参数和合肥站-蚌埠南站的实际线路为实例,分别跟踪理想运行曲线和节能优化运行曲线进行实验验证.仿真结果表明:所提算法跟踪理想运行速度曲线的平均误差为0.01377 km/h,跟踪带干扰的节能优化运行速度曲线的平均误差为0.0364 km/h,相较于基于扩张状态观测器的滑模和非奇异快速终端滑模控制方法,所提方法具有最小的跟踪误差和更高的跟踪精度,验证了其有效性和可行性,可为列车速度跟踪控制领域的研究提供参考.
