Design of fuzzy-neural-network tracking control with only position feedback for robot manipulator including actuator dynamics

Abstract

This study focuses on the development of a fuzzy-neural-network velocity sensorless control (FNNVSC) scheme including a nonlinear observer and a fuzzy-neural-network (FNN) controller for an n-link robot manipulator to achieve high-precision position tracking. This nonlinear observer is used to estimate joint velocities of the robot manipulator. Then, a four-layer FNN is utilized for the major control role without auxiliary compensated control, and the adaptive tuning laws of network parameters are derived in the sense of Lyapunov stability theorem to ensure the stable control performance. Numerical simulations of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed FNNVSC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), Takagi-Sugeno-Kang (TSK)-type fuzzy-neural-network control (T-FNNC) and robust-neural-fuzzy-network control (RNFNC) systems.