Neuro-adaptive hybrid controller for robot-manipulator tracking control

Abstract

The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters. The target error vector for weight adaptation of the neural networks is derived using the Lyapunov-function approach. The global stability of the closed-loop feedback system is guaranteed, provided the structure of the robot-manipulator dynamics model is exact. Generalisation of the controller over the desired trajectory space has been established using an on-line weight-learning scheme. Model learning, using a priori knowledge of a robot arm model, has been shown to improve tracking accuracy. The proposed control scheme has been implemented using both MLN and RBF networks. Faster convergence, better generalisation and superior tracking accuracy have been achieved in the case of the RBF network.