Fixed-Time Anti-Synchronization of Unified Chaotic Systems via Adaptive Backstepping Approach

Ai, Yingdong; Wang, Huanqing

doi:10.1109/tcsii.2022.3179377

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…With the development of neuroscience, future work will focus on non-bifurcation modulation in memristive neurons or neural networks, and then use the modulation of neuronal firing to understand the working mechanism of the brain and the means of preventing some physiological diseases such as epilepsy. More possible applications based on amplitude control and offset boosting will be explored in the future, which could bring practical value to the electronic applications such as secure communications [37], electrolysis of metals [38], and reservoir computing [39].…”

Section: Discussionmentioning

confidence: 99%

Offset boosting in a memristive hyperchaotic system

Zhang,

Li,

Lei

et al. 2023

Phys. Scr.

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In this article, an autonomous memristive hyperchaotic system with multi-dimensional offset boosting is constructed and analyzed. Besides this, the oscillation can be rescaled by an independent controller in the memristor. Two independent constants are obtained for offset boosting with one or two variables, which provide two modes of offset boosting, including single control and synchronous reverse control. In addition, the offset of the variables is also modified by the system bifurcation parameters or combined with amplitude control. The multistability can also be identified according to the offset boosting. Finally, circuit implementation based on PCB is proposed to confirm the numerical simulations.

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Section: Discussionmentioning

confidence: 99%

Offset boosting in a memristive hyperchaotic system

Zhang,

Li,

Lei

et al. 2023

Phys. Scr.

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“…Since numerous industrial systems are modeled as multi-input multi-output (MIMO) nonlinear systems, fruitful control strategies [1][2][3] have been investigated to solve control problems for such systems. Among the existing control methods, adaptive backstepping control schemes, 1,[4][5][6][7][8] have been widely used to tackle the issue of multi-variable, tightly coupled, and nonlinearity in MIMO nonlinear systems during the past several decades. Furthermore, to compensate for highly uncertain and unknown nonlinear items in MIMO systems, the combination of adaptive backstepping control and intelligent control methods such as those involving fuzzy logic systems (FLSs) or neural networks (NNs) has been investigated (see References 2,9-12 and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Song

Sun

Ahn

et al. 2023

Intl J Robust & Nonlinear

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This study developed a new strategy for adaptive fixed-time output feedback control of multi-input multi-output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock-in-place were simultaneously considered. Furthermore, a composite observer was used in MIMO systems to estimate unmeasurable states and external disturbances simultaneously, and radial basis function neural networks were employed to identify unknown internal nonlinearities. Additionally, the command-filtered backstepping approach was applied to avoid tedious analytic calculations of the backstepping framework, and a compensation item was constructed to attenuate the filter error. A fault-tolerant controller was developed to ensure that the overall states of the controlled system were practically fixed-time bounded while the tracking error was regulated to the equilibrium region having a fixed-time convergence ratio. Illustrative studies showed the validity and practicability of the proposed theory.

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“…It is noted that the backstepping design is effective and potent and can be used in a variety of real-world systems, including the ones listed below: Through the use of backstepping design, the fixed-time antisynchronization control problem [11] for two unified chaotic systems with uncertain parameters was taken into account. The issue of controller design for DC microgrids that supply constant power loads was studied.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear recursive gain asymptotic tracking controller design for hydraulic turbine regulating systems

Kanchanaharuthai

2023

Asian Journal of Control

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This paper focuses on the nonlinear asymptotic tracking control for a hydraulic turbine regulating system (HTRS). The proposed control approach combines a recursive gain control scheme with an adaptive technique to achieve asymptotic tracking. An important characteristic feature of the proposed controller is the incorporation of a smooth function with a time‐varying function that is integrable and positive. This function provides the developed controller with sufficient power to achieve asymptotic tracking, whereas the recursive gain control alone can ensure that tracking errors converge exponentially to zero within balls of any arbitrarily small radii. The simulation results demonstrate the efficacy of the presented control technique, which outperforms backstepping, command‐filtered backstepping, dynamic surface, synergetic control designs; deals with the “explosion of complexity” problem; and simultaneously achieves asymptotic tracking. Furthermore, even though the HTRS parameters vary, the proposed technique is capable of providing consistent control performance. As a result, the presented controller is unaffected by changes in the system's parameters.

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Fixed-Time Anti-Synchronization of Unified Chaotic Systems via Adaptive Backstepping Approach

Cited by 16 publications

References 29 publications

Offset boosting in a memristive hyperchaotic system

Offset boosting in a memristive hyperchaotic system

Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

Nonlinear recursive gain asymptotic tracking controller design for hydraulic turbine regulating systems

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