Design of Observers for Hybrid Systems
HSCC '02 Proceedings of the 5th International Workshop on Hybrid Systems: Computation and Control
Automatica (Journal of IFAC)
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Observability of linear hybrid systems
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Brief paper: A maximum-likelihood Kalman filter for switching discrete-time linear systems
Automatica (Journal of IFAC)
The stability of nonlinear least squares problems and the Cramer-Rao bound
IEEE Transactions on Signal Processing
Simultaneous state and input estimation of hybrid systems with unknown inputs
Automatica (Journal of IFAC)
Moving-Horizon State Estimation for Nonlinear Systems Using Neural Networks
IEEE Transactions on Neural Networks
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This paper is concerned with moving horizon estimation for a class of constrained switching nonlinear systems, where the system mode is regarded as an unknown discrete state to be estimated together with the continuous state. In this work, we establish the observability framework of switching nonlinear systems by proposing a series of concepts about observability and analyzing the properties of such concepts. By fully applying the observability properties, we prove the stability of the proposed moving horizon estimators. Simulation results are reported to verify the derived results.