Neural Network Based Direct Model Reference Adaptive Control Technique For Improving Tracking Performance in Nonlinear Systems.
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Date
2019-07
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Addis Ababa University
Abstract
This thesis investigates the application of a neural network based model reference adaptive
intelligent controller for controlling of the nonlinear systems. 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 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 back propagation neural network.
In the conventional model reference adaptive controller (MRAC) scheme, the controller is
designed to realize the plant output converges to reference model output based on the plant,
which is linear. This scheme is effective for controlling the linear plant with unknown
parameters. However, using MRAC to control the nonlinear system in real time is difficult.
The Neural Network is used to compensate the nonlinearity of the plant that is not taken into
consideration in the conventional MRAC. The proposed neural network 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 the plant output.
Adaptive law using Lyapunov stability criteria for updating the controller parameters online
has been formulated.
The effectiveness of the proposed control scheme is verified by developing the simulation
results for simple pendulum and Vander poll oscillation as a benchmark study in
MATLAB/SIMULINK software. It is observed from the simulation results that the proposed
neural network based Direct MRAC has 3.13sec rise time, 5.15sec settling time for 0.1rad
disturbance and 3.12sec rise time, 5.21sec settling time for 0.2rad disturbance. Whereas, the
conventional direct model reference adaptive control has 5.42sec rise time, 15.5sec settling time
for 0.1rad disturbance and 5.01sec rise time, 15.52sec settling time for 0.2rad disturbance.
It is shown that the proposed neural network based Direct MRAC has small rising time, steadystate
error
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Keywords
Model reference adaptive control (MRAC), Artificial Neural Network (ANN), Multilayer Backpropagation Neural Network