Optimization Design Method for PID Parameters in Control Systems Based on Particle Swarm Algorithm and Improvements to PID Control

In this paper, we discuss the optimization design method for PID parameters in control systems based on particle swarm algorithm and improvements to PID control. First, we introduce the fundamental principles of particle swarm optimization (PSO) and its application domains, including key algorithmic components like particle initialization, fitness evaluation using objective functions (e.g., ITAE, ISE), and velocity updates with cognitive/social parameters. Next, we elaborate on utilizing PSO to optimize parameter settings (Kp, Ki, Kd) in PID control systems to enhance system performance and stability, demonstrating code implementation aspects such as boundary handling and convergence criteria. Furthermore, we explore modifications to PID control algorithms—including derivative filtering, set-point weighting, and anti-windup techniques—to address system limitations like overshoot and integral saturation. Finally, we present practical application cases (e.g., motor speed control, temperature regulation) to validate PSO's effectiveness and feasibility in control systems. Through this paper, readers will gain deeper insights into PSO applications in PID control systems and be equipped to apply these methods for performance improvements.