关键词: 粒子群算法, 天牛须算法, PID参数整定, 改进粒子群算法, Schaffer函数, 不稳定对象, Simulink仿真

Abstract:

PSO is widely used in various optimization events, including PID parameter tuning. However, the traditional particle swarm optimization has a slow convergence speed in some cases and is easy to fall into local optimum values. Aiming at this problem, this study proposed a new efficient BAS integrated into the PSO algorithm. The BAS algorithm was applied to the global optimization process of the traditional PSO algorithm, so that it can jump out of the local optimum better. At the same time, because the BAS algorithm is a single individual algorithm, it is easy to fall into local optimum because of premature convergence. By combining with traditional PSO, the richness of BAS was significantly enriched. After 20 independent tests on Schaffer function, compared with the traditional PSO, BAS and the cited references studies, the proposed algorithm achieved better optimization results. Finally, the algorithm was applied to the optimization of the PID parameters of unstable objects. Compared with the PSO and the improved PSO, the indexes such as ts, tr, IAE and ISE had achieved better improvement.

Key words: particle swarm optimization, beetle antennae search algorithm, PID parameter tuning, improved particle swarm optimization, Schaffer function, unstable object, Simulink simulation