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 con-troller 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 simulation 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.
Highlights
In the adaptive control, controlling of the nonlinear system with present-day sophistication and complexities has often been an important research area due to the difficulty in modelling, nonlinearities, and uncertainties
Below response for the nonlinear system the tracking error is high at the beginning so to reduce the tracking error Artificial neural network is preferable
Artificial Neural Network (NN) is used to improve the performance of nonlinear system by training of adaptation law to improve the plant due to rapidly estimating of the nonlinear uncertainty and unknown parameters to ideal values
Summary
In the adaptive control, controlling of the nonlinear system with present-day sophistication and complexities has often been an important research area due to the difficulty in modelling, nonlinearities, and uncertainties. In the MRAC scheme, the controller is designed to realize the plant output converges to reference model output based on assumption that plant can be linearized [3], [4], and [5]. Direct MRAC is best controller for controlling linear plants with unknown parameters. The adaptive controller is designed to realize a plant output tracks to reference model output based on assumption that the plant can be linearized. In [11], the output of neural networks adaptively adjusts the gain of the sliding mode controller so that the effects of system uncertainties eliminated and the output tracking error between the plant output and the desired reference signal can be asymptotically converging to zero. The sliding mode control action can lead to high frequency oscillations called chattering which may excite un-modeled dynamics, energy loss, and system instability and sometimes it may lead to plant damage
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