Model Discrimination of Switched Nonlinear Systems With Temporal Logic-Constrained Switching
Ruochen Niu, Syed M. Hassaan, Liren Yang, Zeyuan Jin, Sze Zheng Yong
Abstract
This letter considers the model discrimination problem for switched nonlinear systems, where the switching sequence is constrained by metric/signal temporal logic specifications. Specifically, we propose an optimization-based algorithm for analyzing the detectability of the models from noisy, finite data as well as a model discrimination algorithm for nonlinear parameter-varying systems to rule out models that are inconsistent with observations at run time, by checking the feasibility of corresponding mixed-integer linear programs. Moreover, we apply the algorithms to nonlinear systems subject to ( m, k)-firm data losses and explicitly provide the integer constraints corresponding to the ( m, k)-firm constraints for lossy/missing data. Finally, we demonstrate the effectiveness of our approaches using several illustrative examples on fault detection, swarm consensus and intent identification problems.