| 引用本文: | 薄雨蒙,曹明生,高慧斌.结合前馈调参与迭代学习的数据驱动控制方法[J].控制理论与应用,2020,37(6):1367~1376.[点击复制] |
| BO Yu-meng,CAO Ming-sheng,GAO Hui-bin.A data-driven control method combining feedforward tuning and iterative learning control[J].Control Theory and Technology,2020,37(6):1367~1376.[点击复制] |
|
| 结合前馈调参与迭代学习的数据驱动控制方法 |
| A data-driven control method combining feedforward tuning and iterative learning control |
| 摘要点击 2537 全文点击 972 投稿时间：2019-06-27 修订日期：2019-12-31 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2019.90484 |
| 2020,37(6):1367-1376 |
| 中文关键词 迭代前馈调参 迭代学习控制 数据驱动控制 基函数 |
| 英文关键词 iterative feedforward tuning iterative learning control data-driven control basis function |
| 基金项目 |
|
| 中文摘要 |
| 在前馈控制中, 需要尽可能的去除前馈控制器对系统模型的需求, 同时保证高精度和鲁棒性. 本文提出了
一种数据驱动的将迭代前馈调参与迭代学习控制进行结合的方法, 通过引入基函数参数化的前馈控制器和输入整
形滤波器, 使用梯度下降法求解最优系统前馈控制器, 消除期望轨迹引入的扰动; 通过迭代学习控制, 消除系统重复
性扰动, 进一步提高控制精度. 算法具有不依赖系统模型, 高精度, 适用于变轨迹任务的优点. 文中给出了相应的仿
真, 并应用到一个直线电机系统, 通过实验验证了算法的有效性. |
| 英文摘要 |
| In feedforward control process, requirement of system model should be moved as much as possible, meanwhile,
achieve high control precision and extrapolation ability. In this paper, a data-driven algorithm combining iterative
parameter tuning and iterative learning control is proposed. Reference-induced disturbance is compensated by introducing
parameterized feedforward controller and input shaping filter with basis function, and using gradient-descent method to
calculate optimal system feedforward controller. Repetitive disturbance in system is eliminated by introducing iterative
learning control to achieve further improvement of control precision. The proposed algorithm has the advantage of modelfree,
high precision and high extrapolation ability. Simulation results are provided in this paper. Experiments of proposed
algorithm is carried on a linear motor system to validate effectiveness and extrapolation ability of algorithm. |
|
|
|
|
|