论文信息 - Recursive triangulation description of the feasible parameter set for bounded-noise models

Recursive triangulation description of the feasible parameter set for bounded-noise models

The problem of estimating feasible parameter sets for linear discrete-time systems with unknown-but-bounded noise is investigated. Considering that the feasible parameter set of a linear discrete-time system with bounded noise is a convex polytope and that a convex polytope can be triangulated into a limited number of simplexes with good properties, the authors propose an exact triangulation description method for the feasible parameter set. A convex polytope triangulation algorithm is introduced and a recursive algorithm of estimating the feasible parameter set in correspondence with the system description is developed. Simulation examples show the effectiveness of the proposed method.

Xianfang Sun | Xianfang Sun | L. He | L. He

[1] Eduardo F. Camacho,et al. Guaranteed state estimation by zonotopes , 2005, Autom..

[2] Luigi Chisci,et al. Recursive state bounding by parallelotopes , 1996, Autom..

[3] Eric Walter,et al. Set inversion via interval analysis for nonlinear bounded-error estimation , 1993, Autom..

[4] Yih-Fang Huang,et al. A recursive estimation algorithm using selective updating for spectral analysis and adaptive signal processing , 1986, IEEE Trans. Acoust. Speech Signal Process..

[5] E. Walter,et al. Exact recursive polyhedral description of the feasible parameter set for bounded-error models , 1989 .

[6] Y. F. Huang,et al. On the value of information in system identification - Bounded noise case , 1982, Autom..

[7] Jacques Cohen,et al. Two Algorithms for Determining Volumes of Convex Polyhedra , 1979, JACM.

[8] F. Schweppe. Recursive state estimation: Unknown but bounded errors and system inputs , 1967 .

[9] John R. Deller,et al. Convergence analysis of the quasi‐OBE algorithm and related performance issues , 2007 .

[10] E. Walter,et al. Further results on recursive polyhedral description of parameter uncertainty in the bounded-error context , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[11] Eric Walter,et al. Interval analysis for guaranteed non-linear parameter and state estimation , 2005 .