Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form. (7th September 2020)

• Record Type: Journal Article
• Authors: Cui, Ting; Xu, Ling; Ding, Feng; Alsaedi, Ahmed; Hayat, Tasawar
• Is Part Of: International journal of adaptive control and signal processing. Volume 34:Number 11(2020)
• Page Start: 1658
• Page End: 1676
• Publication Date: 2020-09-07
• Subjects: differential evolution -- maximum likelihood -- multivariable system -- parameter estimation -- recursive identification; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3169
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state‐space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood‐based differential evolution (ML‐DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML‐DE algorithm and propose a maximum likelihood‐based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.