PERFORMANCE IMPROVEMENT OF ROBOT CONTINUOUS-PATH OPERATION THROUGH ITERATIVE LEARNING USING NEURAL NETWORKS

In this article, an approach to improving the performance of robot con tinuous-path operation is proposed. This approach utilizes a multilaye r feedforward neural network to compensate for model uncertainty assoc iated with the robotic operation. Closed-loop stability and performanc e are analyzed. it is shown that the closed-loop system is stable in t he sense that all signals are bounded; it is further proved that the p erformance of the closed-loop system is improved in the sense that cer tain error measure of the closed-loop system decreases as the network learning process is iterated. These analytical results are confirmed b y computer simulation. The effectiveness of the proposed approach is d emonstrated through a laboratory experiment.