Observer-based switched-linear system identification

Abstract: In this paper, we present a methodology to identify discrete-time state-space switched linear systems (SLSs) from input-output measurements. Continuous-state is not assumed to be measured. The key step is a deadbeat observer based transformation to a switched auto-regressive with exogenous input (SARX) model. This transformation reduces the state-space identification problem to a SARX model estimation problem. Overfitting issues are tackled. The switch and parameter identifiability and the persistence of excitation conditions on the inputs are discussed in detail. The discrete-states are identified in the observer domain by solving a non-convex sparse optimization problem. A clustering algorithm reveals the discrete-states under mild assumptions on the system structure and the dwell times. The switching sequence is estimated from the input-output data by the multi-variable output error state space (MOESP) algorithm and a variant modified from it. A convex relaxation of the sparse optimization problem yields the block basis pursuit denoising (BBPDN) algorithm. Theoretical findings are supported by means of a detailed numerical example. In this example, the proposed methodology is also compared to another identification scheme in hybrid systems literature.