Research on Optimized Fuzzy PID Temperature Control Strategy Based on Improved Particle Swarm Optimization
Precise temperature control is critical in industrial automation, governing product quality in processes from chemical reactors to furnaces. However, high-order inertia, time delays, and parameter drift render traditional PID and manual fuzzy controllers inadequate. To surmount these hurdles, this study presents a robust framework: a Fuzzy PID strategy optimized by a novel Levy-flight Improved Particle Swarm Optimization (LMPSO) algorithm. Addressing the "curse of dimensionality" in fuzzy tuning, LMPSO integrates Levy flight mutation to shatter premature convergence and an Elite Memory Pool to secure evolutionary efficiency. Simulations on a First-Order Plus Dead Time (FOPDT) model reveal the algorithm's potency: it slashes settling time to 105.5 s -- approximately 46.7% faster than standard PSO and 42.5% faster than competitive improved PSO variants -- while achieving an optimal ITAE value. Robustness tests confirm superior stability under severe model mismatches, proving its viability for high-precision industrial applications.