Moving Horizon State Estimation for Networked Control Systems With Multiple Packet Dropouts

• Published in: IEEE transactions on automatic control Vol. 57; no. 9; pp. 2360 - 2366
• Main Authors: Xue, Binqiang, Li, Shaoyuan, Zhu, Quanmin
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Channels; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control systems; Control theory. Systems; Dropouts; Estimation error; Estimators; Exact sciences and technology; Kalman filters; Mathematical models; Modelling and identification; Moving horizon estimation (MHE); Moving horizon state estimation; multiple packet dropouts; networked control systems (NCSs); Noise; Packets (communication); Real-time systems; Simulation; Software; State estimation; Tuning; Uncertainty; Error estimation; Kalman filter; Lossy channel; networked control systems (NCSs); Modeling; multiple packet dropouts; Uncertain system; Moving horizon estimation (MHE); Model matching; Efficiency; Observer; System identification; State estimation; Actuator; Estimation error; Probabilistic approach; Computer simulation; Measurement sensor; Distributed system; Real time; Independent variable; Moving horizon; Distributed control; Feasibility; Transmission loss
• DOI: 10.1109/TAC.2012.2204835
• ISSN: 0018-9286

This technical note studies some of the challenging issues on moving horizon state estimation for networked control systems in the presence of multiple packet dropouts in both sensor-to-controller and controller-to-actuator channels, which both situations are modeled by two mutually independent stochastic variables satisfying the Bernoulli binary distribution. Compared with standard Kalman filter, this study proposes a novel moving horizon estimator to deal with the uncertainties induced from the multiple packet dropouts, which has a larger degree of freedom to obtain better behavior by tuning the weight parameters. A sufficient condition for the convergence of the norm of the average estimation error is also presented to guarantee the performance of the estimator. Finally, a real-time simulation experiment is presented to demonstrate the feasibility and efficiency of the proposed method.