Performance Of Hybrid Learning Control With Input Shaping For Input Tracking And Vibration Suppression Of A Flexible Manipulator

The objective of the work reported in this paper is to investigate the performance of an intelligent hybrid iterative learning control scheme with input shaping for input tracking and end–point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using finite element method. Initially, a collocated proportional–derivative (PD) controller utilizing hub–angle and hub–velocity feedback is developed for control of rigid–body motion of the system. This is then extended to incorporate iterative learning control with genetic algorithm (GA) to optimize the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in time and frequency domains. The performance of hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied