Meta-Heuristic LQI Bio-regulator Benchmark for a Permanent Magnet DC Motor on ARM Platform

There are controllers such as the Linear Quadratic Controller plus Integral part (LQI) that have presented favorable results in critical and complex processes, however, one of the disadvantages of this controller is the parameterization of the Q and R matrices, since they are obtained based on the cost of the controller and a trial and error method. Therefore, the present study aims to optimize these parameters through meta-heuristic algorithms such as: Genetic Algorithms (GA), Bacterial Foraging Optimization (BFO) and Ant Colony Optimization (ACO). In the MATLAB/SIMULINK software, the control loop programming is performed, using the control blocks of the Waijung library and with the STM32F407 card with Advanced Risk Machine (ARM) processor, the capture, reading and processing of data from the plant containing the DC motor for control is obtained. To validate the efficiency of the controller, the Integral of the Absolute Value of the Time Weighted Error (ITAE) is used and together with the Wilcoxon statistical method, it compares the optimization methods or techniques performed in the LQI controller. Interesting and favorable results were obtained for the stability and viability of each bio-controller at the moment of applying them in the speed control of the DC motor.