Robust extrapolation for systems with unknown input and interval uncertainty in system and observations | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2023. № 62. DOI: 10.17223/19988605/62/9

Robust extrapolation for systems with unknown input and interval uncertainty in system and observations

The problem of robust extrapolation for discrete linear system with unknown input and uncertain interval parameters in system and model of observations is considered. The probabilistic approach is used, which is based on replacing uncertain parameters of interval type by independent random variables with uniform distribution in recursive Kalman schemes. The LSM algorithms and nonparametric smoothing procedures are applied for estimating unknown input. The proposed algorithms can be used in control systems with incomplete information. Simulation results are presented and discussed. Contribution of the authors: the authors contributed equally to this article. The authors declare no conflicts of interests.

Download file

Counter downloads: 8

Keywords

robust extrapolation, interval parameters, unknown input, nonparametric smoothing

Authors

Name	Organization	E-mail
Smagin Valery I.	Tomsk State University	vsm@mail.tsu.ru
Kim Konstantin S.	Tomsk State University	kks93@rambler.ru

Всего: 2

References

Abolhasani, M. & Rahmani, M. (2017) Robust Kalman filtering for discrete-time systems with stochastic uncertain time-varying parameters. Electronics Letters. 53(3). pp. 146-148.

Ichalal, D., Marx, B., Maquin, D. & Ragot, J. (2018) State estimation of system with bounded uncertain parameters: interval multi model approach.International Journal of Adaptive Control and Signal Processing. 32(3). pp. 480-493.

Rocha, K.D.T. & Terra, M.H. (2021) Robust Kalman filter for systems subject to parametric uncertainties. Systems and Control Letters. 157. Art. 105034.

Abolhasani, M. & Rahmani, M. (2018) Robust Kalman filtering for discrete-time time-varying systems with stochastic and normbounded uncertainties. Journal of Dynamic Systems, Measurement, and Control. 140(3). Art. No. 030901.

Kim, S., Deshpande, V.M. & Bhattacharya, R. (2021) Robust Kalman filtering with probabilistic uncertainty in system parameters. IEEE Control Systems Letters. 5(1). pp. 295-300.

Janczak, D. & Grishin, Yu. (2006) State estimation of linear dynamic system with unknown input and uncertain observation using dynamic programming. Control and Cybernetics. 4. pp. 851-862.

Witczak, M. (2014) Fault diagnosis and fault-tolerant control strategies for non-linear systems. Chapter 2. Unknown input observers and filters. Lecture Notes in Electrical Engineering. Springer International Publishing. Switzerland. pp. 19-56.

Smagin, V.I. & Smagin, S.V. (2011) Filtering for linear not stationary discrete system with unknown disturbances. Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie vychislitelnaya technika i informatika - Tomsk State University Journal of Control and Computer Science. 16(3). pp. 43-50.

Smagin, V.I. (2014) State estimation for linear discrete systems with unknown input using compensations.Russian Physics Journal. 57(5). pp. 682-690.

Smagin, V.I. & Koshkin, G.M. (2015) Kalman filtering and control algorithms for systems with unknown disturbances and parameters using nonparametric technique. Proceedings 20th International Conference on Methods and Models in Automation and Robotics (MMAR 2015). Miedzyzdroje. Poland. pp. 247-251.

Koshkin, G.M. & Smagin, V.I. (2016) Kalman filtering and forecasting algorithms with use of nonparametric functional estimators. Springer Proceedings in Mathematics & Statistics. 2nd Conference of the International-Society-for-Nonparametric-Statistics (ISNPS). Vol. 175. pp. 75-84.

Smagin V., Koshkin G. & Udod V. (2015) State estimation for linear discrete-time systems with unknown input using nonparametric technique. Proceedings of the International Conference on Artificial Intelligence and Control Automation (AICA 2015). Atlantis Press, Bangkok, Thailand. pp. 675-677.

Barmish, B.R. & Polyak, B.T. (1996) A new approach to open robustness problems based on probabilistic predication formulae. Vol. H. Proceedings 13th WorldIFAC Congr. San Francisco. USA. pp. 1-6.

Kim, K.S. & Smagin, V.I. (2022) Robust extrapolation in discrete systems with interval parameters using algorithms for estimating unknown input. Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie vychislitelnaya technika i informatika - Tomsk State University Journal of Control and Computer Science. 59. pp. 47-54.

Athans, M. (1968) The matrix minimum principle. Informat. and Contr. 11. pp. 592-606.