Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

作者：Zhang, Ruikun; Hou, Zhongsheng^*; Ji, Honghai; Yin, Chenkun

来源：International Journal of Systems Science, 2016, 47(5): 1084-1094.

DOI：10.1080/00207721.2014.911422

• adaptive iterative learning control (AILC)
• non-linearly parameterised systems
• composite energy CEF)
• input saturations

In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L-[0, T](2) convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

• 出版日期2016-4-3
• 单位北京交通大学