Adaptive on-line Genetic Algorithm Based PD Iterative Control for PUMA560

Abstract

In order to improve the accuracy of the repetitive motion trajectory tracking control for industrial robots of less rapid demanding, an online adaptive PD iterative learning control algorithm is proposed. In order to improve the accuracy, PD parameters are optimized online at each sampling time with the advantage of genetic algorithm for global optimization. In order to avoid overshoot, penalty function is used and overshoot is regard as one of the best indicators. Finally, this algorithm is tried in PUMA560. Simulation analysis shows that the proposed algorithm is better than unmodified in accuracy.