Position Control of DC Motor Using Genetic Algorithm-Based PID Controller

This research focuses on "Position Control of DC Motor Using Genetic Algorithm-Based PID Controller". The core objective involves employing genetic algorithm (GA) to optimize PID controller parameters for precise DC motor positioning. Genetic algorithm is a metaheuristic optimization technique inspired by natural selection processes, which effectively tunes the proportional (Kp), integral (Ki), and derivative (Kd) gains of the PID controller. Key implementation aspects include: - Population initialization with random PID parameters - Fitness evaluation using performance criteria like ISE (Integral Square Error) or IAE (Integral Absolute Error) - Selection, crossover, and mutation operations for parameter evolution - Termination criteria based on convergence thresholds or generation counts The GA-enhanced PID controller achieves superior control accuracy and stability by systematically exploring parameter combinations that minimize position error. This approach significantly improves motor positioning performance in applications requiring high precision, such as robotics and automation systems. The algorithm can be implemented in MATLAB/Simulink using functions like ga() for optimization and pidtune() for controller validation, with real-time testing through Arduino or DSP platforms.