Optimal robotic controller based on signals and data information with kinematic redundancy

Abstract

A kinematic redundancy robot is considered as a class of unknown nonlinear discrete-time systems. The compact form dynamic linearization is firstly utilized to establish the equivalent model of the robotic system. Thereafter, the adaptive controller is derived by fuzzy rules emulated network while the learning law is designed to minimize both the tracking error and the control effort energy with the stability analysis. The experimental system is constructed to validate the performance of closed-loop systems.