Envelope-constrained H∞ filtering for nonlinear systems with quantization effects: The finite horizon case

Abstract This paper is concerned with the envelope-constrained H ∞ filtering problem for a class of discrete nonlinear stochastic systems subject to quantization effects over a finite horizon. The system under investigation involves both deterministic and stochastic nonlinearities. The stochastic nonlinearity described by statistical means is quite general that includes several well-studied nonlinearities as its special cases. The output measurements are quantized by a logarithmic quantizer. Two performance indices, namely, the finite-horizon H ∞ specification and the envelope constraint criterion, are proposed to quantify the transient dynamics of the filtering errors over the specified time interval. The aim of the proposed problem is to construct a filter such that both the prespecified H ∞ requirement and the envelope constraint are guaranteed simultaneously over a finite horizon. By resorting to the recursive matrix inequality approach, sufficient conditions are established for the existence of the desired filters. A numerical example is finally proposed to demonstrate the effectiveness of the developed filtering scheme.

[1]  Lihua Xie,et al.  Envelope-constrained IIR filter design: An LMIH∞ optimization approach , 2000 .

[2]  Boris Polyak,et al.  Multi-Input Multi-Output Ellipsoidal State Bounding , 2001 .

[3]  Qing-Long Han,et al.  Variance-Constrained Distributed Filtering for Time-Varying Systems With Multiplicative Noises and Deception Attacks Over Sensor Networks , 2017, IEEE Sensors Journal.

[4]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[5]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[6]  Zidong Wang,et al.  Envelope-constrained H∞ filtering with fading measurements and randomly occurring nonlinearities: The finite horizon case , 2015, Autom..

[7]  Zidong Wang,et al.  Robust Hinfinity finite-horizon filtering with randomly occurred nonlinearities and quantization effects , 2010, Autom..

[8]  Kok Lay Teo,et al.  An introduction to envelope constrained filter design , 2001 .

[9]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[10]  Hak-Keung Lam,et al.  Event-Triggered Mean-Square Consensus Control for Time-Varying Stochastic Multi-Agent System With Sensor Saturations , 2017, IEEE Transactions on Automatic Control.

[11]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[12]  Giuseppe Carlo Calafiore,et al.  Robust filtering for discrete-time systems with bounded noise and parametric uncertainty , 2001, IEEE Trans. Autom. Control..

[13]  T. Katayama,et al.  Discrete-time H ∞ algebraic Riccati equation and parametrization of all H ∞ filters , 1996 .

[14]  Huijun Gao,et al.  On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels , 2016, IEEE Transactions on Automatic Control.

[15]  Zidong Wang,et al.  Event-based state estimation for a class of complex networks with time-varying delays: A comparison principle approach , 2017 .

[16]  Garry A. Einicke,et al.  Robust extended Kalman filtering , 1999, IEEE Trans. Signal Process..

[17]  Uri Shaked,et al.  H∞ nonlinear filtering of discrete-time processes , 1995, IEEE Trans. Signal Process..

[18]  Chi-Yi Tsai,et al.  Visual Tracking Control of a Wheeled Mobile Robot With System Model and Velocity Quantization Robustness , 2009, IEEE Transactions on Control Systems Technology.

[19]  Moon Gi Kang,et al.  Super-resolution image reconstruction , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[20]  Qing-Long Han,et al.  Consensus control of stochastic multi-agent systems: a survey , 2017, Science China Information Sciences.

[21]  Simo Särkkä,et al.  On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Trans. Autom. Control..

[22]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[23]  Fuad E. Alsaadi,et al.  A new framework for output feedback controller design for a class of discrete-time stochastic nonlinear system with quantization and missing measurement , 2016, Int. J. Gen. Syst..

[24]  Qing-Long Han,et al.  Network-based output tracking control for T-S fuzzy systems using an event-triggered communication scheme , 2015, Fuzzy Sets Syst..

[25]  A. Amthor,et al.  Bayes filter for dynamic coordinate measurements – Accuracy improvment, data fusion and measurement uncertainty evaluation , 2013 .

[26]  Kok Lay Teo,et al.  Envelope-constrained IIR filter design via H/sub /spl infin// optimization methods , 1999 .

[27]  Renquan Lu,et al.  Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Qing-Long Han,et al.  An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems , 2017, IEEE Transactions on Industrial Informatics.

[29]  Xuemin Shen,et al.  Game theory approach to discrete H∞ filter design , 1997, IEEE Trans. Signal Process..