Real-time Hybrid System Identification with Online Deterministic Annealing
This work addresses real-time system identification for switching systems, offering improved computational efficiency and control over performance-complexity trade-offs, but it appears incremental as it builds on existing stochastic approximation and recursive identification methods.
The authors tackled the problem of real-time identification for discrete-time state-dependent switching systems by introducing a two-timescale adaptive algorithm that gradually estimates the number of modes and updates parameters, with simulation results validating its efficacy.
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.