Reciprocal of Exponential Varying-Parameter RNN Solving Repetitive Tracking Control Problems With Tolerance of Random Initial Error Compounded With Noise Perturbation

Abstract

Positioning, posture of the robotic joints and end-effector could probably introduce random initial errors. Those errors could exponentially deteriorate with compounded of common noise perturbation to cause the final failure of repetitive tracking control. To better improve the tolerance of those complex errors, a novel reciprocal of exponential varying-parameter recurrent neural network (RE-VP-RNN) is proposed to consider superimposed noise interference including the initial position deviation and noise perturbation together. Theoretical analysis further proves the convergence of the proposed method. The effectiveness, accuracy, and robustness of the proposed RE-VP-RNN solver are verified by simulation and physical experiments on three representative redundant and hype-redundant manipulators. The proposed model could be widely used in robot control for high-precision machining scenarios such as medical, industry, and aviation.