Abstract: In order to solve the premature convergence problem of particle swarm optimization，a novel fuzzy adaptive Particle Swarm Optimization based on T-S model（T-SPSO） is presented.The proposed method shapes the T-S rules according to the current best performance evaluation and inertia weight of swarm，which dynamically update the value of inertia weight and significantly speed up the convergence.The improved algorithm significantly improves the performance of parameters applied in parameter setting of PID controller.The simulation results illustrate the effectiveness of this proposed method and superiority of the controller.

Key words: Particle Swarm Optimization（PSO）, PID control, parameter optimization, Particle Swarm Optimization based on T-S model（T-SPSO）, premature

摘要： 针对微粒群优化算法存在的早熟问题，提出了一种基于T-S模型的模糊自适应PSO算法（T-SPSO算法）。算法依据种群当前最优性能指标和惯性权重值所制定T-S规则，动态自适应惯性权重取值，改善了PSO算法的收敛性。将该算法应用于PID控制器的参数整定，可得到更优的控制器参数。仿真结果验证了所提出算法的有效性和所设计控制器的优越性。

关键词: 微粒群优化算法, PID控制, 参数优化, 基于T-S模型的模糊自适应PSO算法, 早熟