PSO Particle Swarm Optimization Algorithm for PID Parameter Identification

In this documentation, we implemented the PSO (Particle Swarm Optimization) algorithm to identify PID controller parameters: proportional gain (Kp), integral gain (Ki), and derivative gain (Kd). The algorithm operates by initializing a population of potential solutions (particles) that iteratively update their positions and velocities based on personal and global best solutions. The implementation typically involves defining a fitness function (often using integrated absolute error or squared error) to evaluate parameter performance. Results indicate that while the identification accuracy of this approach is not exceptionally high, it demonstrates reasonable reliability for preliminary parameter tuning. The method's effectiveness depends on proper configuration of PSO parameters including swarm size, inertia weight, and acceleration coefficients. Future research directions include exploring hybrid optimization algorithms (such as PSO-GA combinations) and implementing adaptive parameter adjustment mechanisms to improve identification precision. Additionally, experimental validation with real-world control systems should be conducted to verify the practical applicability of identified parameters. This systematic approach will facilitate better understanding and optimization of control system performance across various application scenarios.