Recursive Filtering for Time-Varying Systems With Random Access Protocol

This paper is concerned with the recursive filtering problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive filtering approach.

[1]  Bo Yu,et al.  Robust mixed H2/H∞ control of networked control systems with random time delays in both forward and backward communication links , 2011, Autom..

[2]  Fuwen Yang,et al.  Set-membership filtering for systems with sensor saturation , 2009, Autom..

[3]  H. Fang,et al.  Recursive state estimation for discrete‐time nonlinear systems with event‐triggered data transmission, norm‐bounded uncertainties and multiple missing measurements , 2016 .

[4]  Andrey V. Savkin,et al.  The problem of state estimation via asynchronous communication channels with irregular transmission times , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[5]  Karl Henrik Johansson,et al.  Networked Control With Stochastic Scheduling , 2015, IEEE Transactions on Automatic Control.

[6]  Yeng Chai Soh,et al.  Adaptive Kalman Filtering in Networked Systems With Random Sensor Delays, Multiple Packet Dropouts and Missing Measurements , 2010, IEEE Transactions on Signal Processing.

[7]  Nathan van de Wouw,et al.  Stability Analysis of Networked Control Systems Using a Switched Linear Systems Approach , 2011, IEEE Trans. Autom. Control..

[8]  Shuai Liu,et al.  Probability-guaranteed set-membership filtering for systems with incomplete measurements , 2015, Autom..

[9]  Ling Shi,et al.  Sensor data scheduling for optimal state estimation with communication energy constraint , 2011, Autom..

[10]  Minyue Fu,et al.  Kalman Filtering With Intermittent Observations: On the Boundedness of the Expected Error Covariance , 2014, IEEE Transactions on Automatic Control.

[11]  Daniel E. Quevedo,et al.  Stability analysis of networked control systems subject to packet-dropouts and finite-level quantization , 2011, Syst. Control. Lett..

[12]  Ge Guo,et al.  Control with a random access protocol and packet dropouts , 2016, Int. J. Syst. Sci..

[13]  Lei Zou,et al.  Set-membership filtering for time-varying systems with mixed time-delays under Round-Robin and Weighted Try-Once-Discard protocols , 2016, Autom..

[14]  Ling Shi,et al.  Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality , 2013, IEEE Transactions on Automatic Control.

[15]  Emilia Fridman,et al.  A Round-Robin Type Protocol for Distributed Estimation with H∞ Consensus , 2014, Syst. Control. Lett..

[16]  Hui Tian,et al.  Analysis and synthesis for networked control systems with uncertain rate of packet losses , 2012, J. Frankl. Inst..

[17]  Lei Zou,et al.  On ${\mathcal H}_{\infty }$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks , 2017, IEEE Transactions on Automatic Control.

[18]  Gang Feng,et al.  Optimal linear estimation for networked systems with communication constraints , 2011, Autom..

[19]  Fuwen Yang,et al.  Set-Membership Filtering for Discrete-Time Systems With Nonlinear Equality Constraints , 2009, IEEE Transactions on Automatic Control.

[20]  J. Deyst,et al.  Conditions for asymptotic stability of the discrete minimum-variance linear estimator , 1968 .

[21]  Zidong Wang,et al.  A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements , 2013, IEEE Transactions on Control Systems Technology.

[22]  Zidong Wang,et al.  H∞ state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays , 2015 .

[23]  M. Athans,et al.  Further results on the uncertainty threshold principle , 1977 .

[24]  Alberto Bemporad,et al.  Stability analysis of stochastic Networked Control Systems , 2010, ACC 2010.

[25]  Jun Hu,et al.  Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements , 2012, Autom..

[26]  Dong Yue,et al.  Event-based fault detection for networked systems with communication delay and nonlinear perturbation , 2013, J. Frankl. Inst..

[27]  Fuad E. Alsaadi,et al.  Event-based recursive filtering for time-delayed stochastic nonlinear systems with missing measurements , 2017, Signal Process..

[28]  M. Athans,et al.  The uncertainty threshold principle: Fundamental limitations of optimal decision making under dynamic uncertainty , 1976 .

[29]  Lei Zou,et al.  State Estimation for Discrete-Time Dynamical Networks With Time-Varying Delays and Stochastic Disturbances Under the Round-Robin Protocol , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Ling Shi,et al.  On Set-Valued Kalman Filtering and Its Application to Event-Based State Estimation , 2015, IEEE Transactions on Automatic Control.

[31]  Tristan Needham,et al.  A Visual Explanation of Jensen's Inequality , 1993 .

[32]  Biao Huang,et al.  Identification of nonlinear parameter varying systems with missing output data , 2012 .

[33]  Dragan Nesic,et al.  Input–Output Stability of Networked Control Systems With Stochastic Protocols and Channels , 2008, IEEE Transactions on Automatic Control.

[34]  Raquel Caballero-Águila,et al.  Optimal state estimation for networked systems with random parameter matrices, correlated noises and delayed measurements , 2015, Int. J. Gen. Syst..