A stochastic method for neural-adaptive control of multi-modal nonlinear systems

Abstract

The multiple model adaptive control approach is extended to a class of nonlinear stochastic systems

whose underlying functions are unknown and which

can change arbitrarily in time. Gaussian radial basis function neural networks are used to learn the

nonlinear functions characterising the different plant

modes on-line, without resorting to a separate learning phase. Function estimation, mode change detection and control signal generation are based on

probabilistic techniques utilising concepts of Kalman

filtering, the multiple model algorithm and dual control.