Real-time Hybrid System Identification with Online Deterministic Annealing
<|reference_start|>Real-time Hybrid System Identification with Online Deterministic Annealing: We introduce a real-time identification method for discrete-time state-dependent switching systems in both the input--output and state-space domains. In particular, we design a system of adaptive algorithms running in two timescales; a stochastic approximation algorithm implements an online deterministic annealing scheme at a slow timescale and estimates the mode-switching signal, and an recursive identification algorithm runs at a faster timescale and updates the parameters of the local models based on the estimate of the switching signal. We first focus on piece-wise affine systems and discuss identifiability conditions and convergence properties based on the theory of two-timescale stochastic approximation. In contrast to standard identification algorithms for switched systems, the proposed approach gradually estimates the number of modes and is appropriate for real-time system identification using sequential data acquisition. The progressive nature of the algorithm improves computational efficiency and provides real-time control over the performance-complexity trade-off. Finally, we address specific challenges that arise in the application of the proposed methodology in identification of more general switching systems. Simulation results validate the efficacy of the proposed methodology.<|reference_end|>
@article{mavridis2024real-time,
    title={Real-time Hybrid System Identification with Online Deterministic
  Annealing},
    author={Christos Mavridis, Karl Henrik Johansson},
    journal={arXiv preprint arXiv:2408.01730},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01730},
    primaryClass={eess.SY cs.LG cs.SY}
}