A neuro-adaptive control of nonlinear slow processes

Abstract

A Neuro-Adaptive Internal Model-based Control (NAIMC), using the Fast Clustered Radial Basis Function Network (FCRBFN) equipped by the Stochastic Gradient Descent (SGD) learning algorithm is proposed to control the nonlinear plant with slow dynamics. As a first step in this design approach, the classical feedback controller is applied to improve the overall dynamic characteristics of the obtained local closed loop. Such local loop is further on considered as an equivalent plant to which the NAIMC can be applied. The improved characteristics of the equivalent plant can be usually obtained by some kind of the PD control law and we used this approach at the NAIMC design of the nonlinear slow process. To achieve a zero-steady state error in cases of the piecewise constant changes of the reference and disturbance at output of the plant, we applied the method of Gain Scheduling (GS) for adjusting the gain of the Q controller in the NAIMC based structure. We illustrate the performance of the proposed NAIMC design using simulation results for the control of a double tank system.