Adaptive neural synchronization control of chaotic systems with unknown control directions under input saturation

Wei, Jianming; Zhang, Youan; Sun, Meimei; Bao-liang, Geng

doi:10.1016/j.ijleo.2016.12.045

Cited by 8 publications

(8 citation statements)

References 36 publications

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“…However, control methods in aforementioned literature can only guarantee that the tracking error converges to a small residual set, rather than a small residual set with the prescribed performance bounds. For an uncertain nonlinear system [4,[13][14][15], in order to solve the above problem, the prescribed performance control (PPC) strategy has been proposed. e idea of this method is converting the original system to an equivalent system and ensuring the boundedness of the states of the equivalent system.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Control for a Class of Nonlinear System with Prescribed Performance and Unknown Dead-Zone Inputs

Yang

2020

Journal of Mathematics

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Based on fuzzy logic systems and prescribed performance technology, this paper investigated the stable tracking problem of a class of nonlinear systems with unknown dead-zone inputs. Firstly, the original system is transformed into an equivalent system. Then, a Nussbaum-type function is added in the predefined performance controller to estimate the unknown control direction, and the boundedness of all signals in the closed-loop system is guaranteed. In addition, tracking errors will always remain within the prescribed performance boundaries. Finally, a comparative simulation example shows the effectiveness of the method proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Control for a Class of Nonlinear System with Prescribed Performance and Unknown Dead-Zone Inputs

Yang

2020

Journal of Mathematics

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“…Tracking control for the chaotic systems with input nonlinearities via variable structure design is studied [25], and synchronization of the chaotic systems with input nonlinearities is realized by an adaptive sliding mode controller [26]; however, the limitations of input are not considered. An adaptive neural synchronization control with Nussbaumtype function is developed for chaotic system, which has unknown control directions and input saturation [27]. An adaptive controller based on fuzzy neural is given for uncertain chaotic systems, in which the auxiliary system is used to deal with saturation [28].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control Design for Arneodo Chaotic System with Uncertain Parameters and Input Saturation

Cheng

Gao

et al. 2020

Advances in Mathematical Physics

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In this paper, tracking controller and synchronization controller of the Arneodo chaotic system with uncertain parameters and input saturation are considered. An adaptive tracking control law and an adaptive synchronization control law are proposed based on backstepping and Lyapunov stability theory. The adaptive laws of the unknown parameters are derived by using the Lyapunov stability theory. To handle the effect caused by the input saturation, an auxiliary system is used to compensate the tracking error and synchronization error. The proposed adaptive tracking control and synchronization schemes ensure the effects of tracking and synchronization. Several examples have been detailed to illuminate the design procedure.

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“…Multiple motors driving systems are susceptible to parameter variations, external disturbances and unmodeled dynamics due to their nonlinearity and strong coupling. Therefore, except for the aforementioned coupled control structures, effective synchronization control algorithms are also essential to guarantee synchronization control precision and system stability, such as neural control (Wei et al, 2017), adaptive feedforward feedback (Zhao et al, 2008), fuzzy logic coupling strategy (Moore and Chen, 1995; Shao et al, 2014), H ∞ control (Chen and Chen, 2012), QFT robust control (Cheng et al, 2014), SMC, optimal control (Yan and Yang, 2016), and so on. SMC is a nonlinear robust strategy that possesses several prominent features, such as insensitivity to system parameter variations and model uncertainties, strong robustness against external disturbance, fast convergence rate, good transient performance, high control precision, and simplicity of design and implementation (Saghafinia et al, 2015; Utkin, 1993; Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy sliding mode control for synchronization of multiple induction motors drive

Sun

Gong

Yang

et al. 2019

Transactions of the Institute of Measurement and Control

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In order to achieve high reliability and control precision of multiple induction motors drive systems, a fuzzy sliding mode control (FSMC)-based synchronization control strategy is proposed by combining the FSMC technique with the ring coupling synchronization structure. Also, a fuzzy controller is utilized as a switching control part of the FSMC to attenuate chattering phenomena despite large uncertainties that exist in practical applications. Moreover, the stability analysis of the proposed control strategy is derived by Lyapunov stability theorem. The proposed FSMC-based synchronization scheme possesses several attractive advantages of simple control framework, high control precision, strong robustness to uncertainties, and chattering elimination. Furthermore, the performance of the proposed control strategy is investigated on a four induction motors driving system. The numerical simulation results clearly demonstrate the effectiveness of the proposed FSMC based synchronization strategy for multiple induction motors drive at different operating conditions, and its superiority is indicated by comparison with proportional integral (PI) control and conventional SMC.

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Adaptive neural synchronization control of chaotic systems with unknown control directions under input saturation

Cited by 8 publications

References 36 publications

Fuzzy Adaptive Control for a Class of Nonlinear System with Prescribed Performance and Unknown Dead-Zone Inputs

Fuzzy Adaptive Control for a Class of Nonlinear System with Prescribed Performance and Unknown Dead-Zone Inputs

Adaptive Control Design for Arneodo Chaotic System with Uncertain Parameters and Input Saturation

Fuzzy sliding mode control for synchronization of multiple induction motors drive

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