A Stochastic Fuzzy System Approach to Networked Control Systems with Data Dropout

Zhang, Xiaomei; Zheng, Yufan; Tang, Hongji; Li, Guoping

doi:10.1109/chicc.2006.280943

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Recently, the fuzzy control method has been applied to stability analysis and control design of networked systems due to its success in functional approximation. For instance, the author in [21] developed a stochastic fuzzy system approach to solving the stabilization problem of NCS with data dropouts. Fuzzy robust control and fault estimation for nonlinear networked dynamic systems were discussed in [22] and [16], respectively.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Decentralized Control for a Class of Networked Systems with Time Delay and Missing Measurements

Zhou¹,

Liu²,

Feng³

2014

Asian Journal of Control

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This paper is concerned with fuzzy decentralized control of a class of large-scale networked dynamic systems with network-induced time delays, missing measurements and external disturbances. Nonlinear systems are modeled in Takagi-Sugeno (T-S) form. Fuzzy decentralized state feedback controllers are developed. A sufficient condition on stochastic stability with prescribed H ∞ performance for the closed-loop control system is obtained by using the Lyapunov function method. The constructive design of the controller is expressed in terms of linear matrix inequalities. Finally, numerical simulations are provided to illustrate the results of this paper.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Decentralized Control for a Class of Networked Systems with Time Delay and Missing Measurements

Zhou¹,

Liu²,

Feng³

2014

Asian Journal of Control

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“…Wang [12] studied the robust ∞ control for the networked systems with random packet losses. Zhang [13] developed a fuzzy control for the stochastic approach to networked control system with packet losses. However, there are few studies for a stochastic stabilization with the decentralized fuzzy control technique for the largescale fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Decentralized fuzzy controller for large-scale fuzzy systems with packet losses: Guaranteed cost control approach

Koo

Park

Joo

2012

2012 IEEE International Conference on Fuzzy Systems

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This paper presents a decentralized fuzzy controller design for the stochastic stabilization of the large-scale fuzzy systems with packet losses. Sufficient conditions are derived for the stochastic stabilization of the closed-loop system. Using the cost function, the fuzzy guaranteed cost control is defined and minimization of a given cost function is formulated in terms of linear matrix inequalities. Finally, a numerical example is provided to verify the effectiveness of the proposed technique. U.S. Government work not protected by U.S. copyright WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 -Brisbane, Australia FUZZ IEEE

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“…A Bernoulli stochastic variable is used to describe the packet dropouts in the design of fuzzy decentralized state feedback controllers. To this end, the stochastic stabilization method is better and more feasible than the deterministic one [8], [24]. So, we finally present a sufficient condition to guarantee the stochastic stability for the NCS by using Lyapunov function method.…”

mentioning

confidence: 98%

Fuzzy decentralized control for a class of networked control systems with time delays and missing measurements

Zhou

Liu

Feng

2013

2013 10th IEEE International Conference on Control and Automation (ICCA)

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This paper is concerned with the stochastic stability of a class of discrete-time networked control systems (NCS) with uncertain network-induced time delays and missing measurements by using a fuzzy decentralized control approach. We model nonlinear systems in Takagi-Sugetno (T-S) form and then develop fuzzy decentralized state feedback controllers with missing measurements. A sufficient condition on stochastic stability in the mean-square sense for the NCS is obtained by using Lyapunov function method. The constructive design of the controllers is expressed as the linear matrix inequality (LMI). Finally, a numerical simulation is provided to illustrate the result of this paper.

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A Stochastic Fuzzy System Approach to Networked Control Systems with Data Dropout

Cited by 6 publications

References 13 publications

Fuzzy Decentralized Control for a Class of Networked Systems with Time Delay and Missing Measurements

Fuzzy Decentralized Control for a Class of Networked Systems with Time Delay and Missing Measurements

Decentralized fuzzy controller for large-scale fuzzy systems with packet losses: Guaranteed cost control approach

Fuzzy decentralized control for a class of networked control systems with time delays and missing measurements

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