Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model

Wu, Zheng‐Guang; Shi, Peng; Su, Hongye; Chu, Jian

doi:10.1109/tfuzz.2013.2249520

Cited by 264 publications

(120 citation statements)

References 28 publications

Supporting

Mentioning

119

Contrasting

Unclassified

Order By: Relevance

“…where c =λ + βλ, κ = c − k and v = λ (3) . After expressing the dynamics of the Lorenz system with the new state variables z 1 , z 2 , z 3 one has the following linear canonical form…”

Section: Differential Flatness Of Chaotic Dynamical Systemsmentioning

confidence: 99%

“…The approach of differential flatness theory-based adaptivefuzzy control of chaotic systems is compared to control of such systems with the use of Takagi-Sugeno fuzzy modelling and the solution of Linear Matrix Inequalities (LMIs) [3]. It is shown that the differential flatness theory-based adaptivefuzzy control is more efficient because it is a control method based on exact global linearization of the chaotic oscillators' dynamics and thus avoids numerical approximators and truncation of specific terms in the linearized equivalent of the system.…”

Section: Nonlinear Feedback Control Of Chaotic Systems Based On Takagmentioning

confidence: 99%

“…In [1], the design of a feedback controller for Chua's chaotic circuit is analyzed, based on a combination of H ∞ control theory, adaptive fuzzy control and Lyapunov's stability method. In [2,3] TakagiSugeno fuzzy modelling is proposed for chaotic systems. A feedback controller is designed based on Lyapunov's stability analysis and the solution of LMIs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Rigatos

Siano

2016

Intell Ind Syst

View full text Add to dashboard Cite

The paper proposes a solution to the problem of control of nonlinear chaotic dynamical systems, which is based on differential flatness theory and on adaptive fuzzy control. An adaptive fuzzy controller is designed for chaotic dynamical systems, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H ∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the chaotic system's model into a linear form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neurofuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H ∞ tracking performance. The efficiency of the adaptive fuzzy control method is checked through simulation experiments, using as case study the Lorenz chaotic oscillator.

show abstract

Section: Differential Flatness Of Chaotic Dynamical Systemsmentioning

confidence: 99%

Section: Nonlinear Feedback Control Of Chaotic Systems Based On Takagmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Rigatos

Siano

2016

Intell Ind Syst

View full text Add to dashboard Cite

show abstract

“…Among various kinds of fuzzy methods, the Takagi-Sugeno-Kang (TSK) fuzzy model is widely accepted as a tool for design and analysis of fuzzy control systems [14]. Takagi, Sugeno and Kang proposed the popular TSK fuzzy model as a result of their effort to develop a systematic method for generation of fuzzy rule base out of a given input-output training data set.…”

Section: Design Of Tsk-type Fuzzy Logic Controllermentioning

confidence: 99%

A Comparative Study of Two Types of Fuzzy Logic Controllers for Shunt Active Power Filters

Gadanayak

Majumder

2015

Swarm, Evolutionary, and Memetic Computing

View full text Add to dashboard Cite

Abstract. In this work, two types of fuzzy logic controllers have been designed for a shunt active power filter and their steady state as well as transient performance has been compared to a conventional PI-controller. The performance of the various controllers has been judged on the basis of their ability to reduce Total Harmonic Distortion and their ability to reduce DC-link capacitor voltage settling time during load change. The second factor is important since it signifies controller's usefulness in rapid load changing industrial environment. Also, DC-link capacitor voltage rise at the time of decrease in load is taken as another factor for comparison as it might affect the voltage ratings of the power electronic devices used in the filter.

show abstract

“…The insertion of the network can offer many advantages including reduced equipment weight, lower deployment costs and easy maintenance. Such benefits have given a tremendous amount of motivations to extensive applications of NCSs [10], [21], [29], [32], [39]. It should be remarkable that the communication bandwidth of the shared communication network, as a scarce resource in NCSs, is usually limited.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Event-Triggered $\mathcal{H}_{\infty }$ Control for T–S Fuzzy Markov Jump Systems

Shen

Yan

et al. 2018

IEEE Trans. Fuzzy Syst.

405

120

View full text Add to dashboard Cite

Abstract-This paper investigates the finite-time eventtriggered H∞ control problem for Takagi-Sugeno Markov jump fuzzy systems. Because of the sampling behaviors and the effect of the network environment, the premise variables considered in this paper are subject to asynchronous constraints. The aim of this work is to synthesize a controller via an event-triggered communication scheme such that not only the resulting closedloop system is finite-time bounded and satisfies a prescribed H∞ performance level, but also the communication burden is reduced. Firstly, a sufficient condition is established for the finitetime bounded H∞ performance analysis of the closed-loop fuzzy system with fully considering the asynchronous premises. Then, based on the derived condition, the desired controller design method is presented. Finally, two illustrative examples are given to explain the practicability and availability of the designed controller.

show abstract

Sampled-Data Fuzzy Control of Chaotic Systems Based on a T–S Fuzzy Model

Cited by 264 publications

References 28 publications

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

Flatness-Based Adaptive Neurofuzzy Control of Chaotic Dynamical Systems

A Comparative Study of Two Types of Fuzzy Logic Controllers for Shunt Active Power Filters

Finite-Time Event-Triggered $\mathcal{H}_{\infty }$ Control for T–S Fuzzy Markov Jump Systems

Contact Info

Product

Resources

About