Models and methods for hybrid system identification: a systematic survey*

Moradvandi, Ali; Lindeboom, Ralph E.F.; Abraham, Edo; De Schutter, Bart

doi:10.1016/j.ifacol.2023.10.1553

Cited by 1 publication

(1 citation statement)

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“…The resulting identification problem is a mixed integer optimization program since one must infer from the training data set both continuous variables (the parameters of the ARX systems associated to the modes) and discrete variables (the switching signal that determines the sample‐mode assignment at each time instant). Various methodologies have been proposed in the literature for the identification of switched systems, see, for example, References 1–3. We here focus on the batch version of the identification problem.…”

Section: Introductionmentioning

confidence: 99%

A distributed randomized method for the identification of switched ARX systems

Yu,

Bianchi,

Piroddi

2024

Adaptive Control & Signal

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SummaryThe identification of switched systems amounts to a mixed integer nonlinear optimization problem, where the continuous variables are associated to the model parameterizations of the different modes, and the discrete ones are related to the switching signal (each data sample is assigned to a mode, and switching occurs when the mode assignment changes over time). In the batch form of the identification problem, the combinatorial complexity increases exponentially with the size of the training set, which makes the precise identification of the switching signal the most challenging task in the identification problem. To tackle this complexity we propose a distributed optimization approach, based on the solution of multiple instances of a much simpler problem, where switching can occur only at specific time instants (different for each subproblem), and an information sharing mechanism that preserves likely switching times to improve the local solutions. We employ an adapted version of a previously developed randomized algorithm to solve the individual subproblems. Another important feature of the proposed method is an a posteriori heuristic correction method, that is applied to further refine the switching locations based on the estimated local models before the information sharing phase. The performance of the proposed algorithm is analyzed and compared with other methods on synthetic datasets.

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Section: Introductionmentioning

confidence: 99%