Fuzzy-Adaptive Decentralized Output-Feedback Control for Large-Scale Nonlinear Systems With Dynamical Uncertainties

Tong, Shaocheng; Liu, Changliang; Li, Yongming

doi:10.1109/tfuzz.2010.2050326

Cited by 441 publications

(202 citation statements)

References 35 publications

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“…Great attention has been paid to this area and many results have been achieved, for example, Tong et al (2010), Zhou et al (2012), Mehraeen et al (2011) and Liu et al (2014). It is possible that correct limitations on the interconnections in decentralised adaptive control strategies may be overcome by the use of intelligent control concepts.…”

Section: Decentralised Adaptive Controlmentioning

confidence: 99%

“…A decentralised PID controller is proposed using backstepping techniques in Zhang et al (2007) where the considered system model is expressed in the frequency domain. Recently, some authors have combined backstepping techniques with intelligent control such as fuzzy control and neural network to deal with interconnected systems (Mehraeen et al, 2011;Tong et al, 2011;Zhou et al, 2012;Tong et al, 2010). As one of the advantages of backstepping approach is that it has the flexibility to achieve regulation and tracking properties, the relevant decentralised strategies have also received much attention.…”

Section: Backstepping Approach-based Decentralised Controlmentioning

confidence: 99%

See 1 more Smart Citation

Decentralised control for complex systems - an invited survey

Yan

Zhang

Spurgeon

et al. 2014

IJMIC

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Section: Decentralised Adaptive Controlmentioning

confidence: 99%

Section: Backstepping Approach-based Decentralised Controlmentioning

confidence: 99%

Decentralised control for complex systems - an invited survey

Yan

Zhang

Spurgeon

et al. 2014

IJMIC

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“…To control a large-scale system, the decentralized control technique is more suitable than the traditional centralized control one. Thus, many decentralized control techniques have been proposed [3][4][5][6][7]; the decentralized fuzzy control technique [12][13][14][15] using a Takagi-Sugeno (T-S) fuzzy model has been recognized as one of the predominant decentralized control methods.…”

Section: Introductionmentioning

confidence: 99%

Sampled-Data Observer-Based Decentralized Fuzzy Control for Nonlinear Large-Scale Systems

Koo¹,

Park²,

Joo³

2016

Journal of Electrical Engineering and Technology

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-In this paper, a sampled-data observer-based decentralized fuzzy control technique is proposed for a class of nonlinear large-scale systems, which can be represented to a Takagi-Sugeno fuzzy system. The premise variable is assumed to be measurable for the design of the observer-based fuzzy controller, and the closed-loop system is obtained. Based on an exact discretized model of the closed-loop system, the stability condition is derived for the closed-loop system. Also, the stability condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed techniques.

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“…Stability analysis of discrete fuzzy large-scale systems was discussed by Wang et al (2010). Fuzzy adaptive output feedback control for large-scale nonlinear systems with dynamical uncertainties was studied by Tong et al (2010). However, these results are derived based on the Common Lyapunov-Krasovskii Functional (CLKF).…”

Section: Introductionmentioning

confidence: 99%

Delay-dependent generalized H2 control for discrete T-S fuzzy large-scale stochastic systems with mixed delays

Xia

2011

International Journal of Applied Mathematics and Computer Science

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This paper is concerned with the problem of stochastic stability and generalized H2 control for discrete-time fuzzy largescale stochastic systems with time-varying and infinite-distributed delays. Large-scale interconnected systems consist of a number of discrete-time interconnected Takagi-Sugeno (T-S) subsystems. First, a novel Delay-Dependent Piecewise Lyapunov-Krasovskii Functional (DDPLKF) is proposed, in which both the upper and the lower bound of delays are considered. Then, two improved delay-dependent stability conditions are established based on this DDPLKF in terms of Linear Matrix Inequalities (LMIs). The merit of the proposed conditions lies in its reduced conservatism, which is achieved by circumventing the utilization of some bounding inequalities for cross products of two vectors and by considering the interactions among the fuzzy subsystems in each subregion. A decentralized generalized H2 state feedback fuzzy controller is designed for each subsystem. It is shown that the mean-square stability for discrete T-S fuzzy large-scale stochastic systems can be established if a DDPLKF can be constructed and a decentralized controller can be obtained by solving a set of LMIs. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed method.

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Fuzzy-Adaptive Decentralized Output-Feedback Control for Large-Scale Nonlinear Systems With Dynamical Uncertainties

Cited by 441 publications

References 35 publications

Decentralised control for complex systems - an invited survey

Decentralised control for complex systems - an invited survey

Sampled-Data Observer-Based Decentralized Fuzzy Control for Nonlinear Large-Scale Systems

Delay-dependent generalized H2 control for discrete T-S fuzzy large-scale stochastic systems with mixed delays

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