Taylor Discrete Circadian Rhythms Neural Network for Resolving Bicriteria Optimization Problem of Redundant Robot Manipulators Perturbed by Periodic Noises

Abstract

To solve the motion planning problems of redundant manipulators disturbed by the periodic noise from device hardwares or their surroundings, a Taylor-type discrete-time circadian rhythms neural network (TD-CRNN) method is proposed, developed, and studied in this article. First, a representative bicriteria optimization scheme combining torque criterion and acceleration criterion is presented for the redundant manipulator. Second, inspired by a continuous-time circadian rhythms model, the corresponding TD-CRNN model is derived based on the Taylor discrete formulation. Third, the 0-stability, convergence, and consistency of the proposed TD-CRNN model are analyzed theoretically and proved strictly. Finally, to confirm the capacity of the resisting periodic noise in the tracking problem of manipulators, two groups of comparative simulations and experiments conducted by the proposed TD-CRNN model are performed before the conclusion is given.