Design of Hybrid Learning Control for Flexible Manipulators: a Multi-objective Optimisation Approach

Abstract

This paper presents investigations at development of a design approach of a hybrid iterative learning control scheme for flexible robot manipulators using the multi-objective genetic algorithm (MOGA) approach. A single-link flexible manipulator system is considered in this work. This is a high order, nonlinear and single-input multi-output system with infinite number of modes each with associated damping ratios. Moreover, rise time, overshoot, settling time and end-point vibration are always in conflict in the flexible manipulator since the faster the motion, the larger the level of vibration. A collocated proportional-derivative (PD) controller utilising hub-angle and hub-velocity feedback is developed to control rigid-body motion of the system. This is then extended to incorporate iterative learning control with acceleration feedback to reduce the end-point acceleration of the system. The system performance largely depends on suitable selection of controller parameters. Single objective optimisation techniques can hardly provide good solution in such cases. Multi-objective GAs with fitness sharing technique is used to find optimal set of solutions for iterative learning control parameters, which trade off between these conflicting objectives. The performance of the hybrid learning control scheme is assessed in terms of time-domain specifications and level of vibration reduction at resonance modes.