Abstract:

Different systems and processes such as proportional valves, temperature regulators, motors, etc. require certain controllers for optimum performance; compared to common controllers, the fractional PID controller provides the optimal solution for these systems because they have memory, quick answers and few errors. Various evolutionary algorithms can be used to optimize the parameters of the fractional PID controller, such as artificial bee colony irony (ABC), particle swarm optimization (PSO), genetic algorithm (GA), etc. In order to optimize our performance, we will implement these algorithms in different ways and we'll compare to the classic mode of operation of the controllers. These implementations and trials are based on code sequences in different languages such as python, c++, matlab, based on which results we can prove why these algorithms are an optimal solution. Different system components are modified within the PSO algorithm, optimizing some properties such as configuration or location.