执行器约束下基于数据驱动的参数化前馈控制器设计
Design of parameterized feedforward controller based on data-driven under actuator constraints
摘要点击 355  全文点击 90  投稿时间:2021-09-13  修订日期:2022-01-25
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/CTA.2022.10863
  2022,39(9):1733-1744
中文关键词  执行器约束  数据驱动  前馈参数化  迭代寻优  最优跟踪
英文关键词  actuator constraints  data-driven  feedforward parameterization: iterative optimization  optimal tracking
基金项目  浙江省自然科学基金项目(LY18E050016), 浙江省科技厅重点研发计划项目(2020C01SA902172)资助.
作者单位E-mail
杨亮亮 浙江理工大学 机械与自动控制学院 yangliangliang@zstu.edu.cn 
张晖 浙江理工大学 机械与自动控制学院  
张华 浙江理工大学 机械与自动控制学院  
鲁文其 浙江理工大学 机械与自动控制学院  
中文摘要
      针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.
英文摘要
      Aiming at the trajectory tracking problem of non-repetitive point-to-point motion under actuator constraints, a data-driven parameterized input shaping filter and feedforward controller optimization design algorithm under the constraints of actuators is proposed. Firstly, the input shaping filter and the feedforward controller are parameterized, then the constraints of the control signal variation and the control signal energy are added to the objective function, and the data-driven iterative optimization algorithm is used to obtain the optimal parameters. Under the parameters, the optimal trajectory tracking performance of the motion control system under the constraints of the actuator can be achieved. And because of the feedforward parameterized design method, the proposed algorithm can still maintain good trajectory tracking performance when the point-to-point trajectory changes. Simulation and experimental results show that the proposed algorithm can achieve the optimal point-to-point trajectory tracking performance under the constraints of the actuator, and it has certain robustness to non-repetitive point-to-point trajectory tracking.