Controller optimizations with current metaheuristic algorithms

Optimization is one of the most frequently used steps/processes in engineering. In any engineering problem, the selection of the best among the possible alternatives (solutions) under certain conditions is extremely important in many aspects (efficiency, cost, etc.). From a mathematical point of view, this process is to find the value that makes the determined objective function the optimum (most appropriate) in the desired definition range. There are different methods (deterministic, random, etc.) for this. In recent years, metaheuristic algorithms have been widely used in the solution of optimization problems. In this direction, many metaheuristic algorithms have been developed and continue to be developed. These algorithms, which have types such as evolutionary, nature-inspired (swarm-based, physics/chemistry-based, biological-based, human-based, etc.) give extremely successful results, especially in solving large and multidimensional optimization problems. Controllers and their designs are very important in the field of Electrical and Electronics Engineering. Classical methods (Ziegler-Nichols, Cohen-Coon, etc.) are available for setting the relevant coefficients in basic PID controllers. In thesis, the adjustment of the coefficients in the PID type controllers was carried out with six current metaheuristic algorithms. Using these metaheuristic algorithms, P, PI and PID controller designs for different systems were made under different criteria (sum of absolute error, sum of error squares, sum of time-weighted absolute error, sum of time-weighted error squares and special error function) and their performances were evaluated comparatively.