Enhancing the estimation of plant Jacobian for adaptive neural inverse control

To implement the specialized learning of the inverse dynamic neuro-controller for controlling nonlinear plants with noise, it is strongly desirable that an on-line trained neural plant emluator may provide a reasonably good estimation of the plant Jacobian under noise environments. This paper presents an approach for enhancing the estimation of the plant Jacobian which is on-line used in direct adaptive neural inverse control schemes. The estimated teaching signals are obtained by using the input-output data available at each time step, and then they are used to train the neural plant emluator by a new cost function introduced in this work for training the plant emluator, Convergence theorem for the adaptive back-propagation algorithm and stability of the closed-loop control system are established by using the Lyapunov theory. Simulations are conducted for demonstrating the effectiveness of the proposed strategy. (C) 2000 Published by Elsevier Science B.V. All rights reserved.