Parameter optimization of moving horizon estimation based on hybrid intelligent algorithm. Issue 2 (21st September 2022)

• Record Type: Journal Article
• Authors: Zhang, Bo; Jiang, Aipeng; Qi, Yanying; Jiang, Jiaji; Wang, Haokun; Kong, Jundong
• Is Part Of: Asian journal of control. Volume 25:Issue 2(2023)
• Page Start: 1542
• Page End: 1554
• Publication Date: 2022-09-21
• Subjects: moving horizon estimation -- normalization -- optimization algorithm -- quadratic programming -- window length; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2949
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: 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.