Remarks on Adaptive Neural Network Controller Using Reference Model

Abstract

This paper proposes neural network controllers using a reference model and investigates their characteristics. Since the neural network compensates the incorrectness of the plant such as nonlinearity and uncertainty against the reference model, a conventional controller can be designed based on the known dynamics of the reference model. The neural network controller can be classified into three types in terms of the connection between the neural network controller and the conventional controller. In the D-type controller, the output from the conventional controller inputs to the neural network and the reference model in parallel. In the Y-type controller, the output from the conventional controller inputs to the neural network, the plant and the reference model in parallel. The neural network of both controllers learns the combination of the inverse characteristics of the plant and the forward characteristics of the reference model. In the I-type controller, the output from the conventional controller inputs to the reference model whose output inputs to the neural network. The neural network leams the inverse characteristics of the plant. Simulation results using nonlinear plants show the achievement of the neural controllers and confirm their effectiveness for controlling the nonlinear plants.