Robust adaptive fuzzy neural control of robot manipulators

Abstract

This paper presents a robust adaptive fuzzy neural controller suitable for trajectory control of robot manipulators. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure; 2) online learning; 3) fast learning speed; 4) fast convergence of tracking error; 5) adaptive control; and 6) robust control, i.e. asymptotic stability of the control system is established using Lyapunov theorem. Computer simulation studies were carried out and comparison of simulation results with some existing controllers demonstrate the flexibility, adaptability and good tracking performance of the proposed controller.