Optimization of BP-PID control parameters based on improved mayfly algorithm

Zhongxin Zhang; Shusen Kuang; Jiang Song; Guoqi Ye; Li Shen

doi:10.1117/12.2652820

20 October 2022 Optimization of BP-PID control parameters based on improved mayfly algorithm

Zhongxin Zhang, Shusen Kuang, Jiang Song, Guoqi Ye, Li Shen

Proceedings Volume 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022); 123502R (2022) https://doi.org/10.1117/12.2652820
Event: 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 2022, Qingdao, China

Abstract

Since the traditional PID control algorithm has many problems in parameter selection, it cannot meet specific requirements in engineering practice. However, the PID control algorithm after BP neural network tuning can realize adaptive learning and further improve the control ability, but due to the randomness of its initial weight selection, It is easy to lead to inconsistent training results and affect system stability. To solve these problems, this paper proposes to use the improved mayfly algorithm (IMA) to optimize BP neural network. By taking advantage of the powerful advantages of the improved mayfly algorithm in global search, the optimal position is found as the initial weight of BP neural network. Compared with the traditional method, the overshoot of IMA-BP-PID is only 0.28% in the second order control system, and the overshoot is greatly reduced without steady-state error, which can be better applied to the actual control system.

Citation Download Citation

Zhongxin Zhang, Shusen Kuang, Jiang Song, Guoqi Ye, and Li Shen "Optimization of BP-PID control parameters based on improved mayfly algorithm", Proc. SPIE 12350, 6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022), 123502R (20 October 2022); https://doi.org/10.1117/12.2652820

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