The state space model is a common and important model structure for automation and control.In this pa-per,the robust identification of nonlinear state-space model corrupted by outliers is investigated.The outliers imposed on both the state transition process and the output measurement process are considered and a more comprehensive and robust identification algorithm is proposed.To ensure the robustness of the proposed algorithm,two independent heavy-tailed Stu-dent's t-distributions are used to describe the state noise and the output noise,respectively.Then the particle smoothing method is applied to estimate the posterior distribution of the unknown states.Finally,the expectation maximization algo-rithm is used to realize the parameter estimation problem.The mathematical decomposition of the Student's t-distribution is employed in the identification process which brings two main advantages:(1)facilitating the derivation and implementation of the proposed algorithm;(2)providing a more clearer explanation of the robustness of the algorithm.The usefulness of the proposed algorithm is demonstrated via the numerical and mechanical examples.
关键词
非线性状态空间系统/鲁棒辨识/学生氏t分布/粒子平滑/期望最大化算法
Key words
nonlinear state-space model/robust system identification/Student's t-distribution/particle smoother/ex-pectation maximization algorithm
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