状态空间抽样数据模型的约束最大似然估计

2018 22nd International Conference on System Theory, Control and Computing (ICSTCC) Pub Date : 2018-10-01 DOI:10.1109/ICSTCC.2018.8540710

F. Ávila, J. Yuz, Alejandro Donaire, Juan C. Agüero

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引用次数: 0

摘要

本文应用期望最大化算法来估计包含极点位置约束的状态空间采样数据模型。使用线性二次矩阵不等式作为约束，以获得保留连续时间系统特性(如稳定性或阻尼特性)的模型。仿真研究结果表明了该算法的有效性。

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Constrained maximum likelihood estimation for state space sampled-data models

The Expectation-Maximization algorithm is applied in this paper to estimate state-space sampled-data models including constraints on the location of the poles. Linear quadratic matrix inequalities are used as constraints to obtain a model that preserves properties of the continuous time system, such as stability or damping characteristics. The results of the algorithm are shown in a simulation study.

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来源期刊

2018 22nd International Conference on System Theory, Control and Computing (ICSTCC)

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