Learning-Based Optimal Desired Compensation Adaptive Robust Control for a Flexure-Based Micro-Motion Manipulator

Flexure-based micro-motion mechanisms activated by piezoelectric actuators have a wide range of applications in modern precision industry, due to their inherent merits. However, system performance is negatively affected by model uncertainty, disturbance and uncertain nonlinearity, such as the cross-coupling effect and the hysteresis of the actuator. This paper presents an integrated learning-based optimal desired compensation adaptive robust control (LODCARC) methodology for a flexure-based parallel micro-motion manipulator. The proposed LODCARC optimizes the reference trajectory used in the desired compensation adaptive robust control (DCARC) by iterative learning control (ILC), which can greatly compensate for the effect of repetitive disturbance and uncertainty. The proposed control approach was tested on the flexure-based micro-motion manipulator, with the comparative results of high-speed tracking experiments verifying that the proposed LODCARC controller can achieve excellent tracking and contouring performances with parametric adaption and disturbance robustness. Furthermore, the iterative reference optimization can effectively accommodate the effects of unmodeled repetitive uncertainty from the micro-motion system. This study provides a practical and effective technique for the flexure-based micro-motion manipulator to achieve high-precision motion.