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.

中文翻译：

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状态空间形式的多变量系统基于最大似然的自适应差分进化识别算法

参数估计在系统控制领域中起着重要作用。本文涉及状态空间形式的多变量系统的参数估计方法。为了解决多变量系统中大量参数导致的识别复杂性，我们将原始的多变量系统分解为一些参数较少的子系统，并研究了识别算法以估计每个子系统的参数。通过将最大似然准则函数作为差分进化算法的适应度函数，我们提出了一种基于最大似然的差分进化（ML-DE）算法进行参数估计。为了提高参数估计的准确性，我们将自适应变异因子和自适应交叉因子引入ML‐DE算法，并提出了基于最大似然的自适应差分进化算法。仿真研究表明了所提算法的有效性。