迭代学习控制
控制理论(社会学)
PID控制器
迭代法
非线性系统
参数统计
计算机科学
线性化
控制器(灌溉)
趋同(经济学)
线性系统
数学
算法
控制工程
人工智能
控制(管理)
工程类
生物
统计
量子力学
经济增长
物理
数学分析
经济
温度控制
农学
作者
Ronghu Chi,Huaying Li,Na Lin,Biao Huang
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:54 (3): 1650-1660
被引量:4
标识
DOI:10.1109/tcyb.2022.3232136
摘要
In this work, a data-driven indirect iterative learning control (DD-iILC) is presented for a repetitive nonlinear system by taking a proportional-integral-derivative (PID) feedback control in the inner loop. A linear parametric iterative tuning algorithm for the set-point is developed from an ideal nonlinear learning function that exists in theory by utilizing an iterative dynamic linearization (IDL) technique. Then, an adaptive iterative updating strategy of the parameter in the linear parametric set-point iterative tuning law is presented by optimizing an objective function for the controlled system. Since the system considered is nonlinear and nonaffine with no available model information, the IDL technique is also used along with a strategy similar to the parameter adaptive iterative learning law. Finally, the entire DD-iILC scheme is completed by incorporating the local PID controller. The convergence is proved by applying contraction mapping and mathematical induction. The theoretical results are verified by simulations on a numerical example and a permanent magnet linear motor example.
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