A novel multiple-controller incorporating a radial basis function neural network based generalized learning model

A new adaptive multiple-controller is proposed incorporating a radial basis function (RBF) neural network based generalized learning model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a non-linear RBF neural-network learning sub-model. The proposed non-linear multiple-controller methodology provides the designer with a choice, through simple switching, of using: either, a conventional proportional-integral-derivative (PID) controller, a PID structure based pole (only) placement controller, or a newly developed PID structure based (simultaneous) zero and pole placement controller. Closed-loop stability analysis of the multiple-controller framework is discussed and sample simulation results using a realistic non-linear single-input single-output (SISO) plant model are used to demonstrate the effectiveness of the multiple-controller with respect to tracking desired set-point changes and dealing with sudden introduction of disturbances.