Immune Particle Swarm Optimization for PID Tuning

In this document, we present the application of Immune Particle Swarm Optimization (IPSO) for PID parameter tuning. IPSO is an intelligent optimization algorithm based on evolutionary computation, commonly used to solve complex optimization problems. For PID tuning implementation, we employ a linear inertia coefficient and adaptive crossover-mutation methods to improve the algorithm's search performance and convergence speed. The linear inertia coefficient balances velocity and position updates in the particle swarm, preventing premature convergence to local optima. The adaptive crossover-mutation method enhances population diversity, thereby strengthening global search capabilities. By applying IPSO to PID tuning, we can optimize controller parameters (proportional, integral, and derivative gains) to achieve superior control performance and system stability. The algorithm typically involves initializing particle positions representing PID parameters, evaluating fitness using control performance indices like ISE or IAE, and iteratively updating particles using immune-inspired operators until convergence criteria are met.