Identification of Hybrid Systems: A Tutorial
By Simone Paoletti, Aleksandar Lj. Juloski, Giancarlo Ferrari-Trecate, René Vidal. European Journal of Control, 2007
442 citations recorded by the source corpus.
This tutorial paper is concerned with the identification of hybrid models, i.e. dynamical models whose behavior is determined by interacting continuous and discrete dynamics. Methods specifically aimed at the identification of models with a hybrid structure are of very recent date. After discussing the main issues and difficulties connected with hybrid system identification, and giving an overview of the related literature, this paper focuses on four different approaches for the identification of switched affine and piecewise affine models, namely an algebraic procedure, a Bayesian procedure, a clustering-based procedure, and a bounded-error procedure. The main features of the selected procedures are presented, and possible interactions to still enhance their effectiveness are suggested.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/89b1465f1ede8d74bed48ed5ad9fa0aebdc93255
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In Control and Systems Engineering
- Fredrik Lindsten
- Matthew J. Johnson
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Citations in the corpus
- Deep learning helicopter dynamics models (2015, background): “…v̇ ω̇ = F(s,u,θ) = C12v 1 2 ω̂q CT12g−ω× v+ fv(s,u,θ) fω(s,u,θ) (1) Here g is the known acceleration of gravity, and f = ( fv, fω) is the unknown linear and angular acceleration that the helicopter undergoes, measured in the helicopter frame, as a function of state, control, and model parameters.”
- Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems (2017, result): “This work is similar in spirit to the piecewise affine (PWA) framework in control systems [Sontag, 1981, Juloski et al., 2005, Paoletti et al., 2007].”
- Bayesian Nonparametric Inference of Switching Dynamic Linear Models (2010, unknown): “ar X iv :1 00 3.”
- Identification of jump Markov linear models using particle filters (2014, methodology): “For recent overviews of existing system identification methods for jump Markov linear models, see [7], [8].”
- Gesture Modeling by Hanklet-Based Hidden Markov Model (2014, methodology): “Our approach is related to both linear parameter varying model identification [36] and switched system identification [37].”