A New Motion Tracking Controller with Feedforward Compensation for Robot Manipulators Based on Sectorial Fuzzy Control and Adaptive Neural Networks

Abstract

A novel trajectory tracking control approach for robot manipulators that uses adaptive neural network feedforward compensation plus a sectorial fuzzy controller is presented. We conduct simulation and real-time experiments comparing it with two previously published control schemes: a Proportional–Derivative (PD) plus feedforward compensation controller model, and a sectorial fuzzy control plus feedforward compensation model. The proposed controller shows a faster transient response and better steady-state angular error performance than its counterparts, and it maintains its tolerance to parameter deviation, a main characteristic of fuzzy controllers; furthermore, it excludes the need for knowledge of the robot manipulator model to achieve excellent results. A formal stability analysis of the proposed controller in a closed loop with the robot manipulator guarantees that position and velocity errors converge to zero and all signals are uniformly bounded.