Fuzzy-neural-network control for robot manipulator via sliding-mode design

Abstract

This study presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher-order dynamic model of an n-link robot manipulator is introduced briefly. Then, a conventional SMC scheme is developed for the joint position tracking of the robot manipulator. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.