A novel multiple-controller incorporating a neural network based generalized learning model
نویسندگان
چکیده
A new adaptive multiple-controller is proposed incorporating a 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 Radial Basis Function (RBF) neural-network based learning sub-model . The proposed non-linear multiple-controller methodology provides the designer (through simple switching) with a choice of using either a conventional Proportional-Integral-Derivative (PID) controller, a PID based pole-placement controller, or a PID based zero-pole placement controller. Sample simulation results using a realistic non-linear Single-Input Single-Output plant model are used to demonstrate the effectiveness of the proposed non-linear controller, with respect to tracking desired set-point changes, and it also illustrates the efficiency of employing the RBF neural network in representing the non-linear dynamics compared with conventionally used Multi-Layered Perceptron (MLP) neural networks.
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