Stability and H∞ Control of Systems with Variable Quantization Density in Both Input and Output Channels

This paper is concerned with the problems of stability analysis and Hoo control for a class of systems with quantized signals in both control input channel and measurement output channel. The quantization density of each quantizer is considered to be variable, and the variations of the quantizers are governed by a Markov chain. With the aid of quantization-error-dependent Lyapunov function approach, a set of Hoo controllers depending on the mode of quantization density are carried out to achieve the stochastic stability and the prescribed Hoo performance of the closed-loop system. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control method and the benefits of the variability of quantization density.

[1]  Dragan Nesic,et al.  Input-to-State Stabilization of Linear Systems With Quantized State Measurements , 2007, IEEE Transactions on Automatic Control.

[2]  Peng Shi,et al.  Asynchronous I2-I∞ filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities , 2014, Autom..

[3]  P. Kiessler Stochastic Switching Systems: Analysis and Design , 2008 .

[4]  José Claudio Geromel,et al.  Continuous-time state-feedback H2-control of Markovian jump linear systems via convex analysis , 1999, Autom..

[5]  Wei Xing Zheng,et al.  Observer-Based Control for Piecewise-Affine Systems With Both Input and Output Quantization , 2017, IEEE Transactions on Automatic Control.

[6]  Peng Shi,et al.  A survey on Markovian jump systems: Modeling and design , 2015 .

[7]  Huijun Gao,et al.  A new approach to quantized feedback control systems , 2008, Autom..

[8]  Daniel Liberzon,et al.  Hybrid feedback stabilization of systems with quantized signals , 2003, Autom..

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

[10]  Zepeng Ning,et al.  State estimation for T-S fuzzy affine systems with variable quantization density , 2015, 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP).

[11]  Fuwen Yang,et al.  Observer-based H ∞ control for discrete-time stochastic systems with quantisation and random communication delays , 2013 .

[12]  Huijun Gao,et al.  Network-Induced Constraints in Networked Control Systems—A Survey , 2013, IEEE Transactions on Industrial Informatics.

[13]  Jianbin Qiu,et al.  Observer-Based Piecewise Affine Output Feedback Controller Synthesis of Continuous-Time T–S Fuzzy Affine Dynamic Systems Using Quantized Measurements , 2012, IEEE Transactions on Fuzzy Systems.

[14]  Xunyuan Yin,et al.  Asynchronous Filtering for Discrete-Time Fuzzy Affine Systems With Variable Quantization Density , 2017, IEEE Transactions on Cybernetics.

[15]  Wen Tan,et al.  Switched Quantization Level Control of Networked Control Systems with Packet Dropouts , 2014 .

[16]  Minyue Fu,et al.  Input and Output Quantized Feedback Linear Systems , 2010, IEEE Transactions on Automatic Control.

[17]  Xuerong Mao,et al.  Exponential stability of stochastic delay interval systems with Markovian switching , 2002, IEEE Trans. Autom. Control..

[18]  Carlos E. de Souza,et al.  Robust stability and stabilization of uncertain discrete-time Markovian jump linear systems , 2006, IEEE Transactions on Automatic Control.

[19]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..

[20]  Keith J. Burnham,et al.  On stabilization of bilinear uncertain time-delay stochastic systems with Markovian jumping parameters , 2002, IEEE Trans. Autom. Control..

[21]  Patrizio Colaneri,et al.  Markov Jump Linear Systems with switching transition rates: Mean square stability with dwell-time , 2010, Autom..