Engineering experience-based swarm intelligence for generalized PID tuning

PID controllers are extensively employed in industrial process control. Nevertheless, traditional PID tuning methods often face significant limitations when applied to intricate dynamic systems, especially under varying operating conditions. These challenges can lead to the Empirical Infeasible Solution (EIS) problem. To mitigate this issue, this paper proposes a PID tuning method that integrates engineering experience with Particle Swarm Optimization (PSO). By defining a constrained parameter space and introducing directional constraints, the proposed method effectively avoids the occurrence of EIS. The Engineering Experience-based Levy-Memory-PSO (EE-LMPSO) algorithm is applied to optimize PID parameters through numerical simulations. Its advantages in optimization performance, reliability, and control accuracy are demonstrated through comparisons with traditional methods. Experimental results show that the proposed method improves tuning efficiency, enhances system stability, and achieves better control performance in complex dynamic environments.