NEURO-ADAPTIVE HYBRID CONTROLLER FOR ROBOT-MANIPULATOR TRACKING CONTROL

The paper is concerned with the design of a hybrid controller structur e, consisting of the adaptive control law and a neural-network-based ] earning scheme for adaptation of time-varying controller parameters. T he 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 t he controller over the desired trajectory space has been established u sing an online weight-learning scheme. Model learning, using a priori knowledge of a robot arm model, has been shown to improve tracking acc uracy. The proposed control scheme has been implemented using both MLN and RBF networks. Faster convergence, better generalisation and super ior tracking accuracy have been achieved in the case of the RBF networ k.