基于改进粒子群优化的模糊PID温度控制策略研究
Research on Optimized Fuzzy PID Temperature Control Strategy Based on Improved Particle Swarm Optimization
AI总结：
针对工业温度控制的传统控制器不足问题，采用LMPSO算法优化模糊PID策略，仿真与鲁棒性测试表明其调节时间显著缩短、稳定性优异，适用于高精度工业场景。
AI中文摘要：
精确温度控制在工业自动化中至关重要，决定了从化学反应器到熔炉等工艺的产品质量。然而，高阶惯性、时滞和参数漂移导致传统PID控制器和手动模糊控制器无法满足需求。为克服这些障碍，本研究提出一种鲁棒框架：采用新型莱维飞行改进粒子群优化（LMPSO）算法优化的模糊PID策略。针对模糊调优中的“维数灾难”问题，LMPSO集成莱维飞行变异以打破早熟收敛，并引入精英记忆池确保进化效率。针对一阶加死区时间（FOPDT）模型的仿真显示该算法的效能：它将调节时间缩短至105.5秒，比标准PSO快约46.7%，比同类改进PSO变体快42.5%，同时达到最优ITAE值。鲁棒性测试证实其在严重模型失配下具有更优稳定性，证明其适用于高精度工业应用。
英文摘要：
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.
