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Artificial Firefly Meta-heuristic Used for the Optimization of a Fractional PID on an ARM Platform

Abstract:

Nowadays, DC motors are very useful in the industry, and their easy control is one of the reasons. Over time, different control techniques have been implemented to improve the performance of these machines. In the present study, a Fractional Order Proportional Integral Derivative controller (FOPID) is developed, which presents better results in the face of disturbances or changes in the set point than a conventional Proportional Integral and Derivative (PID) controller. Since it is a more complex controller, one of its problems is the tuning of its parameters. For this purpose, artificial intelligence techniques such as metaheuristics can be used, among which one that has not been explored in depth is the Artificial Firefly (AF) algorithm, which uses a Cost Function (CF) that adjusts the optimization based on a performance index, such as the Integral Time Absolute Error (ITAE). The hardware used for the control is an STM32F4 embedded board (with Advanced RISC Machine - ARM technology). A Control Plant Trainer (CPT) containing all the instrumentation for the actual tests is used as the plant. The performance of the FOPID - FA is compared with a conventionally tuned FOPID, using the Wilcoxon statistical method, yielding interesting results from the control point of view.