Genetic Algorithm-Based PID Controller Parameter Tuning Using MATLAB

In this research, we implemented genetic algorithm optimization for PID controller parameters using MATLAB programming. The implementation involves creating chromosome representations for PID gains (Kp, Ki, Kd), defining fitness functions based on performance criteria like ISE (Integral Square Error) or IAE (Integral Absolute Error), and implementing genetic operators including selection, crossover, and mutation. We applied this method to control a first-order plus time delay model and conducted step response simulations. Key MATLAB functions used include ga() from Global Optimization Toolbox for algorithm implementation and step() for system response analysis. Through these experiments, we gained better understanding of genetic algorithm applications in control systems and evaluated their performance and effectiveness. This research holds significant importance for optimizing controller design and improving system response characteristics.