Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

Abstract

This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive controller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simula-tion results for a simple pendulum. It is shown that the proposed neural network-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

“…Intelligent control techniques have been emerged as promising control techniques in tolerating the harmful effects of unmatched uncertainty and complexity of the system dynamics. Authors in reference [23] did a comparative analysis between neural network-based direct MRAC and conven-tional MRAC, and the author proved that neural networkbased direct MRAC has a smaller rising time, steady-state error, and settling time than the conventional MRAC approach. Authors in [24] also developed extended minimum resource allocating network (EMRAN) based MRAC for UAV control and compared controller performance with existing PID controller.…”