IEEE Trans Neural Netw Learn Syst. 2015 Nov;26(11):2939-48. doi: 10.1109/TNNLS.2015.2461022. Epub 2015 Aug 12.

Enhanced Data-Driven Optimal Terminal ILC Using Current Iteration Control Knowledge.

IEEE transactions on neural networks and learning systems

Ronghu Chi, Zhongsheng Hou, Shangtai Jin, Danwei Wang, Chiang-Ju Chien

PMID: 26277006 DOI: 10.1109/TNNLS.2015.2461022

Abstract

In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain, which is a function about the system partial derivative with respect to the time-varying control input. In addition, a parameter updating law is designed to estimate the unknown partial derivatives iteratively. The input signals of the proposed E-DDOTILC are time-varying and updated utilizing not only the terminal tracking error of the previous run but also the input signals of the previous time instants in the current iteration. The proposed approach is a data-driven control strategy and only the I/O data are required for the controller design and analysis. The monotonic convergence and effectiveness of the proposed approach is further verified by both the rigorous mathematical analysis and the simulation results.

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't