| 引用本文: | 廖常超,周兰,潘昌忠,陈静.基于等价输入干扰补偿的改进型重复控制系统参数优化设计[J].控制理论与应用,2022,39(4):653~662.[点击复制] |
| LIAO Chang-chao,ZHOU Lan,PAN Chang-zhong,CHEN Jing.Parameter optimization design of modified repetitive control system based on equivalent input disturbance compensation[J].Control Theory and Technology,2022,39(4):653~662.[点击复制] |
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| 基于等价输入干扰补偿的改进型重复控制系统参数优化设计 |
| Parameter optimization design of modified repetitive control system based on equivalent input disturbance compensation |
| 摘要点击 1650 全文点击 502 投稿时间：2021-03-18 修订日期：2021-06-13 |
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| DOI编号 10.7641/CTA.2021.10228 |
| 2022,39(4):653-662 |
| 中文关键词 重复控制 等价输入干扰 扰动估计和补偿 粒子群优化算法 参数优化 |
| 英文关键词 repetitive control equivalent input disturbance disturbance estimation and compensation particle swarm optimization algorithm parameter optimization |
| 基金项目 国家自然科学基金项目(61673167), 湖南省自然科学基金项目(2019JJ50146), 湖南省研究生科研创新项目(CX20200997)资助. |
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| 中文摘要 |
| 针对一类同时具有周期性参考输入和非周期扰动的伺服系统, 提出基于等价输入干扰补偿的改进型重复
控制系统参数优化设计方法, 实现对非周期扰动的有效抑制和周期性参考输入的高精度跟踪控制. 首先, 利用全维
状态观测器的估计误差构造等价输入干扰估计器, 通过将等价输入干扰估计值反馈到控制输入端, 建立基于等价输
入干扰补偿的复合重复控制规律. 然后, 基于小增益定理推导出系统的稳定性条件, 引入一个对系统抗扰性能、跟
踪性能和收敛速度进行整体评价的性能目标函数, 建立系统参数优化模型, 采用粒子群优化算法, 实现对系统重复
控制器参数、等价输入干扰估计器参数和状态反馈控制器参数的同时优化. 最后, 通过数值仿真分析对比说明所提
方法的有效性和优越性. |
| 英文摘要 |
| For a class of servo systems with both periodic reference input and aperiodic disturbance, a modified repetitive
control system parameter optimization design method based on equivalent input disturbance compensation is proposed
to achieve effective suppression of aperiodic disturbance and high-precision tracking control of periodic reference input.
Firstly, the equivalent input disturbance estimator is constructed by using the estimation error of the full order state observer.
By feeding the estimated value of the equivalent input disturbance back to the control input, the compound repetitive
control law based on the equivalent input disturbance compensation is established. Then, based on the small gain theorem,
the stability conditions of the system are derived, and a performance objective function for overall evaluation of the system’s
anti-disturbance performance, tracking performance and convergence speed is introduced, and the system parameter
optimization model is established. The particle swarm optimization algorithm is used to achieve the system Simultaneous
optimization of repetitive controller parameters, equivalent input disturbance estimator parameters, and state feedback controller
parameters. Finally, the validity and superiority of the proposed method are verified by numerical simulation and
comparative analysis. |
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