Exponential Synchronization and $L_2$ -Gain Analysis of Delayed Chaotic Neural Networks Via Intermittent Control With Actuator Saturation

Sang, Hong; Zhao, Jun

doi:10.1109/tnnls.2019.2896162

Cited by 51 publications

(23 citation statements)

References 53 publications

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“…Remark 3. Many papers provide a control or synchronization method for the chaotic system, and the section above also proposed some control methods, but some common methods, such as a nonlinear method [18], an intermittent control method [19], and a state feedback control method [20], do not indicate whether the system converges at the origin exponentially and no method is given to adjust the convergence rate. However, the method in this paper gives conclusions.…”

Section: Remark 2 the Lyapunov Function Can Be Used To Prove Theoremmentioning

confidence: 99%

The Application of Accurate Exponential Solution of a Differential Equation in Optimizing Stability Control of One Class of Chaotic System

Jia

Guo

2020

Mathematics

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For many nonlinear systems in our life, the chaos phenomenon generated under certain conditions in special cases will split the system and result in a crash-down of the system. This paper discusses the stable control of one class of chaotic systems and a control method based on the accurate exponential solution of a differential equation is used. Compared with other methods, the advantages are: this method determines that the system can exponentially converge at the origin and the convergence rate can be easily regulated. The chaotic system with unknown parameters is also deduced and validated by using this method. In practical application, it is found that the ship’s electric system also has the same model, so it has certain practical significance.

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Section: Remark 2 the Lyapunov Function Can Be Used To Prove Theoremmentioning

confidence: 99%

The Application of Accurate Exponential Solution of a Differential Equation in Optimizing Stability Control of One Class of Chaotic System

Jia

Guo

2020

Mathematics

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“…When A d = C qd = 0 or = 0, the nonlinear model (7) becomes the nonlinear system without time delay. It is worth noting that linear systems and many famous chaotic systems with or without time delay, which contain chaotic neural networks, 32 Chua's circuits, 33 and unified chaotic systems, 34 can be converted to model (7).…”

Section: Figurementioning

confidence: 99%

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Tong

Chen

et al. 2019

Optim Control Appl Methods

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Summary The asynchronous control for the unified model of chaotic systems with Markovian jumping parameters is considered via the Takagi‐Sugeno (T‐S) fuzzy approach in this paper. According to jumping modes for chaotic systems and modes for the asynchronous controller, a double‐mode process is generated. By introducing the hidden Markov model (HMM), the asynchronization phenomenon is represented between the controller and chaotic systems. By linear matrix inequalities (LMIs), some conditions are received to guarantee the stochastic stability and the strict dissipativity for chaotic systems. Based on the stochastic stability theory and the dissipativity theory, the asynchronous controller is discussed. The proposed method can be applied to the mode‐independent controller and the synchronous controller. Finally, the effectiveness of the proposed controller is illustrated by two examples.

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“…The literature 1 points out that IC can achieve the synchronization between a drive system and a response system by limiting the amount of information to

0.02 %^{2}

in some situations. Due to its high efficiency, IC has wide applications on stabilization or synchronization issues of linear systems, 2 stochastic systems, 3 nonlinear systems, 4,5 time‐delay systems, 6‐8 chaotic systems, 9‐12 neural networks, 13‐15 and complex networks 16,17 . IC involved in these papers needs to update feedback information continuously on control time intervals.…”

Section: Introductionmentioning

confidence: 99%

Time‐triggered intermittent control of continuous systems

Wang

2021

Intl J Robust & Nonlinear

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For intermittent control, its control signal is updated in a continuous manner on control time intervals. To overcome the limitation, time‐triggered intermittent control (TIC) is presented for the first time in this article. In our work, based on a new time‐triggered mechanism, the mathematical description of TIC is put forward and a TIC system is modeled. Different from the traditional time‐triggered mechanism, the new time‐triggered mechanism just allows the partial sampled data to be transmitted. Due to different structures of the TIC system, a mixed Lyapunov functional is introduced to obtain a general stability criterion. As a result, a stabilization criterion of continuous systems with TIC is deduced and two methods to design TIC are proposed. Further, comparisons between TIC and sampled‐data control (SC) are made. Finally, an example is given for illustration.

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Exponential Synchronization and $L_2$ -Gain Analysis of Delayed Chaotic Neural Networks Via Intermittent Control With Actuator Saturation

Cited by 51 publications

References 53 publications

The Application of Accurate Exponential Solution of a Differential Equation in Optimizing Stability Control of One Class of Chaotic System

The Application of Accurate Exponential Solution of a Differential Equation in Optimizing Stability Control of One Class of Chaotic System

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Time‐triggered intermittent control of continuous systems

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