<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, a conventional control system may not behave appropriately online; this is because of many factors like a variation in the dynamics of the process itself, unexpected changes in the environment, or even undefined parameters of the system model. To overcome this problem, we have designed and implemented an adaptive controller. This paper discusses the design of a controller for a ball and beam system with Genetic Model Reference Adaptive Control (GMRAC) for an adaptive mechanism with the MIT rule. Parameter adjustment (selection) should occur using optimization methods to obtain an optimal performance, so the genetic algorithm (GA) will be used as an optimization method to obtain the optimum values for these parameters. The Linear Quadratic Regulator (LQR) controller will be used as it is one of the most popular controllers. The performance of the proposed controller with the ball and beam system will be carried out with MATLAB Simulink in order to evaluate its effectiveness. The results show satisfactory performance where the position of the ball tracks the desired model reference.</p>
</abstract>

The conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, a conventional control system may not behave appropriately online; this is because of many factors like a variation in the dynamics of the process itself, unexpected changes in the environment, or even undefined parameters of the system model. To overcome this problem, we have designed and implemented an adaptive controller. This paper discusses the design of a controller for a ball and beam system with Genetic Model Reference Adaptive Control (GMRAC) for an adaptive mechanism with the MIT rule. Parameter adjustment (selection) should occur using optimization methods to obtain an optimal performance, so the genetic algorithm (GA) will be used as an optimization method to obtain the optimum values for these parameters. The Linear Quadratic Regulator (LQR) controller will be used as it is one of the most popular controllers. The performance of the proposed controller with the ball and beam system will be carried out with MATLAB Simulink in order to evaluate its effectiveness. The results show satisfactory performance where the position of the ball tracks the desired model reference.

Design of a Linear Quadratic Regulator Based on Genetic Model Reference Adaptive Control

Systems and Control Engineering Department

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, a conventional control system...

Design of a Linear Quadratic Regulator Based on Genetic Model Reference Adaptive Control

Data publikacji: 06 wrz 2023

Zakres stron: 75 - 81

Otrzymano: 06 sty 2022

Przyjęty: 07 maj 2022

DOI: https://doi.org/10.14313/jamris/3-2022/26

Słowa kluczowe
model reference adaptive control, gradient approach, Linear Quadratic Regulator, genetic algorithm

© 2022 Abdullah I. Abdullah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Design of a Linear Quadratic Regulator Based on Genetic Model Reference Adaptive Control

Data publikacji: 06 wrz 2023

Zakres stron: 75 - 81

Otrzymano: 06 sty 2022

Przyjęty: 07 maj 2022

DOI: https://doi.org/10.14313/jamris/3-2022/26

Słowa kluczowemodel reference adaptive control, gradient approach, Linear Quadratic Regulator, genetic algorithm

© 2022 Abdullah I. Abdullah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
model reference adaptive control, gradient approach, Linear Quadratic Regulator, genetic algorithm