摘要
传统模糊神经PID控制算法易出现网络参数调整不合适导致控制效果差的问题。本文提出一种改进蝗虫算法优化下的模糊神经网络PID控制算法。首先针对传统蝗虫算法粒子多样性不足的问题引入Levy随机飞行策略,其次引入非线性缩减因子和模拟退火算法来改善算法寻优能力以及跳出局部最优解的能力,然后将改进的蝗虫算法与模糊神经PID结合来优化神经网络超参数以及实现控制参数自整定,最后由仿真结果验证所提出的改进蝗虫算法优化模糊神经网络PID算法的优越性和可靠性。
The traditional fuzzy neural PID control algorithm is prone to the problem of poor control effect caused by improper adjustment of network parameters. This paper presents a fuzzy neural network PID control algorithm optimized by improved grasshopper algorithm. Aimed at the problem of insufficient traditional algorithms of grasshopper particle diversity firstly introduced Levy random flight strategy, secondly introduce nonlinear reduction factor and simulated annealing algorithm to improve the optimization ability and the ability to jump out of local optimal solution, then the improved grasshopper algorithm combined with fuzzy neural PID neural network is optimized by super parameter and control parameter self-tuning, Finally, the simulation results verify the superiority and reliability of the proposed improved grasshopper algorithm to optimize the fuzzy neural network PID algorithm.
作者
曹梦龙
马俊林
Cao Menglong;Ma Junlin(College of Automation and Electronic Enginnering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《电子测量技术》
北大核心
2022年第20期74-80,共7页
Electronic Measurement Technology
基金
山东省自然科学基金(ZR2020KE037)项目资助。
关键词
蝗虫优化算法
Levy飞行策略
非线性因子
模拟退火算法
grasshopper optimisation algorithm
Levy flight strategy
nonlinear factor
simulated annealing algorithm
