Design of Model Reference Adaptive Intelligent Controller Using Neural Network for Nonlinear Systems

In this paper a new approach to a neural network-based model reference adaptive intelligent controller is proposed. In this scheme, the intelligent supervisory loop is incorporated into the conventional model reference adaptive controller framework by utilizing an online growing multilayer back propagation neural network structure in parallel with it. The idea is to control the plant by conventional model reference adaptive controller with a suitable single reference model, and at the same time respond to plant by online tuning of a multilayer back propagation neural controller. In the conventional model reference adaptive control (MRAC) scheme, the controller is designed to realize plant output converges to reference model output based on the plant which is linear. This scheme is for controlling linear plant effectively with unknown parameters. However, using MRAC to control the nonlinear system at real time is difficult. In this paper, it is proposed to incorporate a Neural Network (NN) in MRAC to overcome the problem. The control input is given by the sum of the output of conventional MRAC and the output of NN. The NN is used to compensate the nonlinearity of the plant that is not taken into consideration in the conventional MRAC. The proposed NN-based model reference adaptive controller can significantly improve the system behavior and force the system to follow the reference model and minimize the error between the model and plant output. The effectiveness of the proposal control scheme is demonstrated by simulations.