Hybrid Reference Governor-Based Adaptive Robust Control of a Linear Motor Driven System

Abstract

The linear motor driven system has been widely used in the manufacturing industry, where high motion tracking accuracy is required, including fast dynamic response and high steady-state tracking accuracy. However, the improvement of the motion control performances is limited by parametric uncertainties and uncertain nonlinearities, such as nonlinear friction, varying load mass, etc. In addition, the system constraints owing to input saturation and speed/space limitations also challenge the improvement of the motion control performances. In this paper, a hybrid reference governor-based adaptive robust control (HRGARC) algorithm is proposed for the constrained motion control of the linear motor driven system. The proposed approach is composed of two reference governor (RG)-based adaptive robust controllers (RGARC) and a switching strategy. For each RGARC, the RG is utilized to deal with input/state constraints, and the adaptive robust control (ARC) algorithm is used to cope with parametric uncertainties and uncertain nonlinearities. These two RGARCs are specifically designed to achieve fast dynamic response and high steady-state tracking accuracy, respectively. Furthermore, a switching strategy is designed to coordinate these two RGARCs according to the system states and the input reference. Therefore, the high transient and steady-state motion control performances of the linear motor driven system can be achieved by the proposed HRGARC in the presence of parametric uncertainties, uncertain nonlinearities, and input/state constraints. Comparative experiments conducted on the linear motor driven system validate the effectiveness of the proposed HRGARC algorithm.