Set-membership state and parameter estimation for discrete time-varying systems based on the constrained zonotope

In this paper, the set-membership state and parameter estimation problems are considered for linear time-varying systems. The noises in the system are unknown but bounded and the variation of parameters is considered as a bounded expansion. The entire estimator is an interactive estimation of parameters and states. In parameter estimation, the exact set of parameter estimates may be non-convex because of the uncertain states. A polytopic linear parameter varying (LPV) enclosure of the system regression expression is constructed as a convex relaxation. The result of parameter estimation is a set to include the true parameter of the model. Meanwhile, in the state estimation, a similar LPV enclosure of the state-space model is also used to bound the state matrix uncertainties. This LPV enclosure in the state estimation is narrowed by the parameter estimation, and then the set of state estimates is computed to include the true state. The constrained zonotope (CZ) is used to describe sets of interest and the necessary set operations are expanded to LPV mapping. Because of the highly adjustable accuracy of CZ, the proposed algorithm can make a better trade-off between computational accuracy and efficiency. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

Automated summary

This paper discusses the set-membership state and parameter estimation problems for linear time-varying systems. Parameter and state estimation are done using polytopic linear parameter varying (LPV) enclosures, while the constrained zonotope (CZ) is used to describe sets of interest. The proposed algorithm strikes a better balance between computational accuracy and efficiency. A numerical example is provided to illustrate its effectiveness.