Data-Driven Iterative Learning Control of Nonlinear Systems by Adaptive Model Matching

作     者：Lee, Yu-Hsiu Rai, Sandeep Tsao, Tsu-Chin 

作者机构：Natl Taiwan Univ Dept Mech Engn Taipei 106319 Taiwan Cymer San Diego CA 92127 USA Univ Calif Los Angeles Dept Mech & Aerosp Engn Los Angeles CA 90095 USA 

出 版 物：《IEEE-ASME TRANSACTIONS ON MECHATRONICS》 (IEEE ASME Trans Mechatron)

年 卷 期：2022年第27卷第6期

页      面：5626-5636页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：U.S. NIH [R01EY030595] Taiwan MOST [110-2222-E-002-012] 

主　　题：Adaptive filtering data-driven control iterative learning control (ILC) nonlinear systems 

摘      要：Iterative learning control (ILC) has proven successful in the industry for enhancing tracking performance of repetitive tasks. The high accuracy and fast convergence of ILC algorithms hinge on: 1) the knowledge of the system model and 2) an effective learning algorithm. For general industrial systems with nonlinear dynamics, this raises technical challenges because acquiring a nonlinear dynamical model or several linearized models at different operating points may be difficult and costly. It is also non-trivial to determine the learning algorithm for the complex model obtained. To address these challenges, this article proposes a novel data-driven ILC algorithm for single-input-single-output nonlinear systems. Without explicit nonlinear models, our algorithm treats the nonlinear system as an unknown linear time-varying system linearized on a specified input-output (I/O) trajectory in each ILC iteration. A linearly parameterized time-varying adaptive filter is constructed in each ILC iteration so that, when cascading with the nonlinear plant, the I/O dynamics around the specified trajectory follow a linear time-invariant reference model. The ILC error trajectory is then filtered by the adaptive time-varying filter, which represents the inverse dynamics with a bandwidth specified by the reference model, to render fast convergence. The benefits of the proposed data-driven algorithm is demonstrated by comparing to a gradient and Hessian-based data-driven ILC algorithm on a prototypical two-degree-of-freedom nonlinear pendulum.