摘要
现实环境下的控制系统大多为存在时间延迟的连续时间控制系统,为了模拟控制系统对输入的响应,就必须对其参数进行估算。本文首先对时延连续系统方程作拉普拉斯变换,得到了系统的显式方程;然后,通过蝴蝶优化算法和Levy飞行算法对2020年提出的最新的麻雀搜索算法进行改进,使其避免陷入局部极小值点;最后,将智能优化算法应用于控制系统的辨识中,基于本文改进的麻雀搜索算法对时延连续系统的参数进行了估算,并通过数值实验验证了本文算法在辨识精度和收敛速度上的优越性。
Most control systems in the real environment are continuous time control systems with time delays,and in order to simulate the response of control systems to inputs,it is necessary to estimate their parameters.In this paper,Laplace transform is performed on the equation of the time-delay continuous system to obtain the explicit equation of the system.Then,the butterfly optimization algorithm and Levy flight algorithm are used to improve the sparrow search algorithm proposed in 2020 to avoid falling into local minimum points.Finally,the intelligent optimization algorithm is applied to the identification of the control system,that is,the parameters of the time-delay continuous systems are estimated based on the sparrow search algorithm improved in this paper,and the superiority of the proposed algorithm in recognition accuracy and convergence speed is verified by numerical experiments.
作者
张能
刘珑龙
Zhang Neng;Liu Longlong(School of Mathematical Science,Ocean University of China,Qingdao 266100,China)
出处
《中国海洋大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第8期166-172,共7页
Periodical of Ocean University of China
基金
国家自然科学基金项目(12171444)资助。
关键词
参数辨识
时延连续系统
麻雀搜索算法
蝴蝶优化算法
Levy飞行算法
拉普拉斯变换
parameter identification
time-delay continuous system
sparrow search algorithm
monarch butterfly optimization algorithm
Levy flight algorithm
Laplace transform
