Nonlinear adaptive control based on RBF networks and multi-model method

Feedforward neural networks have been extensively applied to modeling and control of nonlinear systems. It has been known that using only one NN model to approximate accurately a highly nonlinear plant within a large domain is very difficult, and the controller based on the model often fail when the operating point changes greatly. This paper proposes a nonlinear direct adaptive control strategy based on radial basis function (RBF) neural networks and multi-models. An online adaptive algorithm and several effective model switching methods are given. The adaptive control strategy based on a single NN model has been proved to be robust, reliable, efficient and simple. The strategy based on multi-model proposed in this work can trace an expected output accurately without oscillation within a large domain. The control strategy is also applied to a pH continuously stirred tank reactor and the simulation results demonstrate the advantages