Model-Data Driven Learning Adaptive Robust Control of Precision Mechatronic Motion Systems With Comparative Experiments

Abstract

In order to meet the rigorous motion accuracy requirement and efficiently utilize the repetitive-task characteristics in modern precision industry, this paper concentrates on the comprehensive research of model-based data-driven learning adaptive robust control (LARC) strategy for precision mechatronic motion systems. The proposed LARC can achieve not only excellent transient/steady-state tracking performance but also adaptation ability and disturbance robustness. Specifically, the LARC strategy contains robust feedback term, adaptive model compensation term, and iterative learning term. Herein, the former two terms are designed based on the system dynamic model under parametric uncertainty and uncertain nonlinearity, and the data-driven iterative learning term is synthesized to generate optimal input to adjust the optimal reference. The whole controller design procedure and stability is presented, while the reason for the practically achievable performance of LARC is analyzed. Comparative experiments, among proportional—integral—differential, adaptive robust control, iterative learning control, and the proposed LARC, are conducted on a developed linear motor stage. The experimental results consistently validate that the proposed LARC scheme simultaneously achieves excellent transient/steady-state tracking performance, parametric adaptation ability, and disturbance robustness. The LARC strategy essentially provides an effective control technology with good potential in industrial applications.