关键词: PID, 蜜獾算法, 参数整定, 群智能算法, 不稳定对象, Simulink仿真, 智能算法, 引力搜索算法

Abstract:

As a swarm intelligence algorithm simulating the predator-prey behavior of honey badger, honey badger algorithm has many problems, such as easy to fall into local optimal solutions, and the number of iterations required. In view of the shortcomings of honey badger algorithm, a cloud honey badger algorithm (CHBA) combining gravity search algorithm and normal cloud technology is proposed. The density factor of the original honey badger algorithm that controls the individual search range of the honey badger is replaced by the acceleration in the gravitational search algorithm to improve the rationality of the individual search range of the honey badger and accelerate the search iteration speed. The normal cloud algorithm is used to generate a new batch of honey badgers with the expectation of the best position of the honey badger between generations, so as to improve the population diversity and avoid falling into local optimization. At the same time, the generation range of the new honey badger is adaptively adjusted to avoid local optimization. Twenty three benchmark functions are selected to test the proposed algorithm. From the optimization results of single peak, multi peak and fixed dimension multi peak functions, the step response PID(Proportion Integration Differentiation) parameters of first-order delay system, non minimum phase system and first-order minimum delay system are optimized and compared, and the results show that CHBA algorithm has better performance in search efficiency and iteration accuracy.

Key words: PID, honey badger algorithm, parameter setting, swarm intelligence algorithm, unstable object, Simulink simulation, intelligent algorithm, gravitational search algorithm