Optimum control is concerned with operating a dynamic system at minimum cost, which means that the settings of a controller governing a process are found by using a mathematical algorithm that minimizes a cost function with weighting factors supplied. The 'cost' function is frequently defined as the sum of the deviations of key measurements from their desired values. The Linear Quadratic Regulator (LQR) finds the controller settings that minimize the undesired deviations. The choice of the weighting factors is made through trial and error. In this paper, a genetic algorithm is utilised to determine the optimum weighting matrices for a LQR. The process used as the investigation tool is a binary distillation column which possesses multivariable and nonlinear features. Simulation results show the effectiveness of the proposed approach.
A state-of-the-art study of computerized control of chemical processes used in industry, this book is for chemical engineering and industrial chemistry students involved in learning the micro-macro design of chemical process systems.
This project is focused mainly on designing and implementing Linear Quadratic Regulator (LQR) controller and PID controller for a Dc servo motor. The PID and LQR controller are used to control the speed and position of the Dc servo motor, with a specified performance requirement. To ensure that the controller satisfies the requirement, simulation using MATLAB/Simulink software will be performed to obtain the optimum PID and LQR controller parameters. The obtained parameters are then used to implement LQR controller to the real Dc servo motor. The performance of the LQR controller will then be compared with that of PID controller.