Exponential Synchronization of Chaotic Systems with Stochastic Perturbations via Quantized Feedback control

In this paper, the exponential synchronization problem is investigated for chaotic systems under the quantized feedback control. The chaotic systems are represented by the unified model with time delays and stochastic perturbations. The quantized feedback control strategy is studied for the unified model with state and control quantizers, which are connected by using two dynamic scalars. The one-step control approach is put forward to make sure the exponential synchronization of drive-response systems. The corresponding control gain and two scalars are provided by solving linear matrix inequalities. Finally, the obtained results are illustrated by a numerical example.

[1]  Rathinasamy Sakthivel,et al.  Finite Time Passive Reliable Filtering for Fuzzy Systems With Missing Measurements , 2018 .

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

[3]  Peng Shi,et al.  Sampled-Data Synchronization of Chaotic Lur'e Systems With Time Delays , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Peng Shi,et al.  Exponential Stability of Markovian Jumping Systems via Adaptive Sliding Mode Control , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Qiaoyu Chen,et al.  Delay and Its Time-Derivative-Dependent Model Reduction for Neutral-Type Control System , 2017, Circuits Syst. Signal Process..

[6]  Xiaodi Li,et al.  Lag synchronization of chaotic delayed neural networks via impulsive control , 2012, IMA Journal of Mathematical Control and Information.

[7]  Peng Shi,et al.  Exponential Synchronization of Neural Networks With Discrete and Distributed Delays Under Time-Varying Sampling , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Changming Ding,et al.  Synchronization of stochastic perturbed chaotic neural networks with mixed delays , 2010, J. Frankl. Inst..

[9]  Jing Zhang,et al.  Finite-Time Adaptive Fuzzy Control for Nonlinear Systems With Full State Constraints , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Pengcheng Wei,et al.  Weak synchronization of chaotic neural networks with parameter mismatch via periodically intermittent control , 2011 .

[11]  Yueying Wang,et al.  Reliable Fuzzy Tracking Control of Near-Space Hypersonic Vehicle Using Aperiodic Measurement Information , 2019, IEEE Transactions on Industrial Electronics.

[12]  Guanghong Yang,et al.  Fault-tolerant control via sliding-mode output feedback for uncertain linear systems with quantisation , 2013 .

[13]  Jinde Cao,et al.  Network-Based Quantized Control for Fuzzy Singularly Perturbed Semi-Markov Jump Systems and its Application , 2019, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  Qiankun Song,et al.  Stabilization and synchronization of chaotic systems with mixed time-varying delays via intermittent control with non-fixed both control period and control width , 2015, Neurocomputing.

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

[16]  Hongjing Liang,et al.  Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization , 2020, IEEE Transactions on Cybernetics.

[17]  Hongjing Liang,et al.  Adaptive Event-Triggered Output Feedback Fuzzy Control for Nonlinear Networked Systems With Packet Dropouts and Actuator Failure , 2019, IEEE Transactions on Fuzzy Systems.

[18]  Jun Zhou,et al.  Asymptotical synchronization for delayed stochastic neural networks with uncertainty via adaptive control , 2016 .

[19]  Guang-Hong Yang,et al.  New Results on Output Feedback $H_{\infty} $ Control for Linear Discrete-Time Systems , 2014, IEEE Transactions on Automatic Control.

[20]  Guang-Hong Yang,et al.  Quantized Dynamic Output Feedback H∞ Control for Discrete-time Systems with Quantizer Ranges Consideration , 2008 .

[21]  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.

[22]  Yaochu Jin,et al.  Multi-sensor optimal H∞ fusion filters for delayed nonlinear intelligent systems based on a unified model , 2011, Neural Networks.

[23]  Yuhua Xu,et al.  Adaptive State Estimation of Stochastic Delayed Neural Networks with Fractional Brownian Motion , 2018, Neural Processing Letters.

[24]  Jing Zhang,et al.  Command Filter-Based Adaptive Fuzzy Control for Nonlinear Systems With Unknown Control Directions , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[25]  Meiqin Liu,et al.  Exponential synchronization of two totally different chaotic systems based on a unified model , 2014, Neural Computing and Applications.

[26]  Karl Henrik Johansson,et al.  Quantized Control Under Round-Robin Communication Protocol , 2016, IEEE Transactions on Industrial Electronics.

[27]  Weihua Gui,et al.  Decentralised H ∞ quantisers design for uncertain interconnected networked systems , 2010 .

[28]  Jinde Cao,et al.  Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme , 2019, IEEE Transactions on Neural Networks and Learning Systems.

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

[30]  Guanghong Yang,et al.  Quantized output feedback stabilization of uncertain systems with input nonlinearities via sliding mode control , 2014 .

[31]  Xiaodi Li,et al.  Synchronization of chaotic delayed neural networks with impulsive and stochastic perturbations , 2011 .

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

[33]  杨光红,et al.  离散时间系统量化动态输出反馈的 H ∞ 控制 , 2009 .

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

[35]  Yugang Niu,et al.  Control strategy with adaptive quantizer's parameters under digital communication channels , 2014, Autom..

[36]  Yong Ren,et al.  Quantized Finite-Time Non-fragile Filtering for Singular Markovian Jump Systems with Intermittent Measurements , 2019, Circuits Syst. Signal Process..

[37]  Xin-Ping Guan,et al.  Synchronization of Chaotic Lur’e Systems With Time Delays Using Sampled-Data Control , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Jun Yang,et al.  Exponential synchronization for stochastic neural networks with multi-delayed and Markovian switching via adaptive feedback control , 2015, Commun. Nonlinear Sci. Numer. Simul..

[39]  Weihua Sheng,et al.  Exponential H∞ Synchronization and State Estimation for Chaotic Systems Via a Unified Model , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Yunliang Wei,et al.  Quantized Output Feedback Control of Uncertain Discrete-Time Systems with Input Saturation , 2014, Circuits Syst. Signal Process..

[41]  Jianliang Wang,et al.  Quantized insensitive consensus of Lipschitz nonlinear multi-agent systems using the incidence matrix , 2015, J. Frankl. Inst..

[42]  Rathinasamy Sakthivel,et al.  Non-fragile filtering for singular Markovian jump systems with missing measurements , 2018, Signal Process..

[43]  Hongjing Liang,et al.  Event-Triggered Fault Detection and Isolation of Discrete-Time Systems Based on Geometric Technique , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[44]  Daniel Liberzon,et al.  Adaptive control of passifiable linear systems with quantized measurements and bounded disturbances , 2016, Syst. Control. Lett..

[45]  Jianliang Wang,et al.  Quantized $H_{\infty}$ Consensus of Multiagent Systems With Quantization Mismatch Under Switching Weighted Topologies , 2017, IEEE Transactions on Control of Network Systems.

[46]  Shengyuan Xu,et al.  Slow State Variables Feedback Stabilization for Semi-Markov Jump Systems With Singular Perturbations , 2018, IEEE Transactions on Automatic Control.