Recursive identification of switched ARX systems
Automatica (Journal of IFAC)
Expert Systems with Applications: An International Journal
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Engineering Applications of Artificial Intelligence
Brief paper: A continuous optimization framework for hybrid system identification
Automatica (Journal of IFAC)
Identification of switched linear systems via sparse optimization
Automatica (Journal of IFAC)
A real-integer-discrete-coded particle swarm optimization for design problems
Applied Soft Computing
Parameters identification of nonlinear state space model of synchronous generator
Engineering Applications of Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Brief Equivalence of hybrid dynamical models
Automatica (Journal of IFAC)
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In this paper, a methodology for identifying switching sequences and switching instants of switched linear systems (SLS) is derived. The identification problem of a SLS is a challenging and non-trivial problem. In fact, it involves interaction between binary, discrete and real-valued variables. A SLS switches many times over a finite time horizon and thus estimating the sequence of activated modes and the switches locations is a crucial problem for both control and Fault Detection and Isolation (FDI). The proposed methodology is based on the Discrete Particle Swarm Optimization (DPSO) technique. The identification problem is formulated as an optimization problem involving noisy data (system inputs and outputs). Both a set of binary variables corresponding to each sub-model before and after each switch, and the corresponding switching instants are iteratively adjusted by the DPSO algorithm. Thus, the DPSO algorithm has to classify which sub-system has generated which data. The efficiency of the proposed approach is illustrated through a numerical example and a physical one. The numerical example is a Switched Auto-Regressive eXogenous (SARX) system and the physical one is a buck-boost DC/DC converter.