Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm

作     者：Zhang, Bo Jiang, Aipeng Qi, Yanying Jiang, Jiaji Wang, Haokun Kong, Jundong 

作者机构：Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China 

出 版 物：《ASIAN JOURNAL OF CONTROL》 (亚洲控制杂志)

年 卷 期：2023年第25卷第2期

页      面：1542-1554页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：Zhejiang Provincial Natural Science Foundation [LY20F030010] National Natural Science Foundation of China 

主　　题：moving horizon estimation normalization optimization algorithm quadratic programming window length 

摘      要：In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.