DIRECT ADAPTIVE NEURAL-NETWORK CONTROL OF ROBOTS

Neural network modelling of robots is introduced using the GL matrices and operator (Ge et al. 1994), and a new adaptive neural network cont roller for robots is presented. The controller is based on direct adap tive techniques, and there is no need for matrix inversion. Unlike man y neural network controllers in the literature, inverse dynamical mode l evaluation is not required and no time-consuming training process is necessary, except for initializing the neural networks based on appro ximate parameters of the initial posture at time t = 0. It is shown th at if gaussian radial basis function networks are used, uniformly stab le adaptation is assured and asymptotic tracking is achieved.