Real-time Hybrid System Identification with Online Deterministic Annealing

TL;DR

This paper presents a real-time hybrid system identification method for switching systems that adaptively estimates modes and parameters using a two-timescale approach, improving efficiency and enabling sequential data processing.

Contribution

The paper introduces a novel online deterministic annealing-based identification algorithm for switching systems, capable of estimating the number of modes and parameters in real-time.

Findings

Algorithm effectively estimates modes and parameters in real-time.

Progressive estimation enhances computational efficiency.

Simulation validates the method's efficacy.

Abstract

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.