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


Keywords: Model Reference Adaptive Control, Gradient Approach, Linear Quadratic Regulator, Genetic Algorithm


As the conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, this control system may not behave appropriately online; this is because of many factors like some variation in the dynamics of the process itself or unexpected changes in the environment, or even undefined parameters of the system model. To overcome the problem above, an adaptive controller is designed and implemented. This paper discusses the design of a controller for a ball and beam system with Genetic Model Reference Adaptive Control (GMRAC) for adaptive mechanism with the MIT rule. Parameter adjustment (selection) should be done using optimization methods in order to obtain an optimal performance, so genetic algorithm. (GA) will be used as an optimization method to obtain the optimum values for these parameters. As the controller, a 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.



How to Cite

Abdullah, A. ., Mahmood, A., & Thanoon , M. . (2023). Design of Linear Quadratic Regulator Based on Genetic Model Reference Adaptive Control. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(3), 75-81.