改进PSO算法及在PID参数整定中应用研究

针对粒子群优化算法(PSO)存在早熟和局部收敛的问题,提出了一种带变异算子的改进粒子群优化算法(IPSOM),该算法在搜索中以一定变异概率对选中的粒子进行变异,同时对飞离搜索区域的粒子用新产生的粒子取代,以克服粒子群优化算法易陷入局部最优解的缺陷。用一典型的Rastigrin复杂函数对新算法进行测试,结果表明改进的算法较之粒子群优化算法(PSO)和常规遗传算法(SGA)不但提高了全局寻优能力,而且有效避免了早熟收敛问题。在此基础上将这种改进算法应用于高阶带时滞对象的PID控制器设计中进行仿真研究,结果表明了所提出算法的有效性和所设计控制器的优越性。

Improved Particle Swarm Optimization and its Application Research in Tuning of PID Parameters

Aiming at the problem that the particle swarm optimization (PSO) is difficult to deal with premature and local convergence, an improved particle swarm optimization with mutation (IPSOM) was proposed. This new algorithm introduces mutation operator into the particle swarm optimization and replaces those particles flying out the solution space with new particles during the searching process to overcome the shortcoming of the particle swarm optimization. Through testing with a typical Rastigrin complex mathematics function, the experimental results show that the improved method not only has better ability to converge to the global optimum than the PSO and the simple genetic algorithm (SGA), but also can avoid the premature convergence effectively. Based on the above, this improved algorithm was applied to design the PID controller of a high-order system with time delay. The results show that the approach is effective and the designed controller has excellent performance.