Hybrid Sliding-Mode Fuzzy Neural Network Tracking Control for a Wheeled Mobile Manipulator

Abstract

This paper develops a methodology for trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and external load changes. Based on backstepping technique, the proposed control law consists of two levels: kinematics and dynamic levels. First, the auxiliary kinematic velocity control laws for the mobile platform and the onboard arm are separately proposed. Second, a robust tracking control system based on hybrid sliding-mode fuzzy neural networks (HSMFNN) is presented to ensure the velocity tracking ability under dynamic uncertainties. To achieve the goal, a fuzzy neural network (FNN) controller is developed to act as an equivalent control law in the sliding-mode control, a robust controller is designed to incorporate the system dynamics into the sliding surface for guaranteeing the asymptotical stability, and the proportional controller is designed to improve the transient performance for randomly initializing FNN. All the adaptive learning algorithms of the proposed controller are derived from the Lyapunov stability theory so that the close-loop system tracking ability can be guaranteed no matter the uncertainties occur or not. Simulation results illustrate the feasibility as well as usefulness of the proposed control strategy in comparison with other strategies