Dynamic parameter tuning of PID via Particle Swarm Optimization: a novel approach to enhance temperature control accuracy in industrial nonlinear scenarios

Temperature control stands as a pivotal element in industrial production, directly influencing both operational efficiency and product quality. While the foundational PID control algorithm continues to be widely employed for temperature regulation, its traditional implementations are plagued by notable challenges: cumbersome parameter tuning processes, susceptibility to integral saturation, and inadequate handling of nonlinear systems. These drawbacks often lead to subpar control performance, thereby hampering overall production efficiency. To address these inherent limitations, this study proposes an enhanced PID control algorithm optimized via Particle Swarm Optimization (PSO). As a swarm-intelligence-driven optimization technique, PSO is integrated with the conventional PID framework to construct a second-order inertial control system, with the core objective of dynamically optimizing the proportional (kp), integral (ki), and derivative (kd) parameters of PID. To verify the efficacy of the proposed PSO-PID algorithm, comparative simulations were conducted in MATLAB under three typical industrial temperature input scenarios: step, ramp, and sinusoidal signals. The results consistently demonstrated that PSO-PID outperforms traditional PID in terms of convergence performance across all test cases. Specifically, significant improvements were observed in key performance metrics, including a substantial reduction in root mean square error (RMSE), maximum error, and steady-state error. These findings fully validate the effectiveness of the PSO-PID algorithm in temperature control systems, offering a practical and reliable optimization solution to enhance the precision of temperature regulation and boost overall production efficiency in industrial settings.