A new fixed-time stabilization approach for neural networks with time-varying delays

Aouiti, Chaouki; Miaadi, Foued

doi:10.1007/s00521-019-04586-y

Cited by 16 publications

(4 citation statements)

References 61 publications

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“…By using a novel fixed-time stability theorem of dynamical systems and Lyapunov method, two different control protocols are designed to guarantee the fixed-time stabilization of FNTINNs with time-varying delay. Also, the proposed theoretical results of this article present a more accurate upper settling-time estimation compared to known results [23,29,30]. The highlights and the main contributions of this paper are embodied in the following aspects:…”

Section: Introductionmentioning

confidence: 82%

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Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

Aouiti

Hui

Jallouli

et al. 2021

Fuzzy Sets and Systems

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This paper addresses the problem of fixed-time stabilization for a class of fuzzy neutral-type inertial neural networks (FNTINNs) with time-varying delay. By using a novel fixed-time stability theorem for dynamical systems, two different feedback control laws are designed to ensure the fixed-time stabilization of FNTINNs with time-varying delay. The proposed theoretical results can lead to a better upper settling-time estimation compared to existing results. Finally, three simulation examples are provided to illustrate the validity of the proposed theoretical results.

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Section: Introductionmentioning

confidence: 82%

“…Remark 5.3. The fuzzy neural networks are investigated in [7,29,47]. These models are first order differential equations without neutral terms and distributed time-varying delays.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

Aouiti

Hui

Jallouli

et al. 2021

Fuzzy Sets and Systems

Self Cite

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“…The following Lemma 2 is provided in advance for the convergence analysis of the proposed NAZNN model. Lemma 2: [48,49] Considering a non-linear dynamic system as follows.…”

Section: Convergence and Robustness Analysis Of The Naznn Model 231 C...mentioning

confidence: 99%

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Lan

Liu²

2023

Front. Phys.

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As a powerful method for time-varying problems solving, the zeroing neural network (ZNN) is widely applied in many practical applications that can be modeled as time-varying linear matrix equations (TVLME). Generally, existing ZNN models solve these TVLME problems in the ideal no noise situation without inequality constraints, but the TVLME with noises and inequality constraints are rarely considered. Therefore, a non-linear activation function is designed, and based on the non-linear activation function, a non-linearly activated ZNN (NAZNN) model is proposed for solving constrained TVLME (CTVLME) problems. The convergence and robustness of the proposed NAZNN model are verified theoretically, and simulation results further demonstrate the effectiveness and superiority of the NAZNN model in dealing with CTVLME and the constrained robot manipulator trajectory tracking problems. In addition, the wheeled robot trajectory tracking fault problems with physical constraints are also analyzed theoretically, and the proposed NAZNN model is also applied to the manipulator trajectory tracking fault problem, and the experimental results prove that the NAZNN model also deal with the manipulator trajectory tracking fault problem effectively.

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“…The origin of the dynamic system (8) will be globally finite-time stable if the system is globally finite-time stable and the settling time function T is globally bounded, i.e., there exists a constant t f ∈ℝ + satisfying t f ≥ T(x 0 ) for all x 0 ∈ℝ n . Lemma 1 [57,64]. If there exists a radially continuous unbounded function V: ℝ n → ℝ + ∪{0} such that V(ζ) = 0 for ζ∈Ω and any solution ζ(t) satisfies…”

Section: Rznn Model Analysismentioning

confidence: 99%

A robust zeroing neural network for solving dynamic nonlinear equations and its application to kinematic control of mobile manipulator

Jin

2020

Complex Intell. Syst.

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Nonlinear phenomena are often encountered in various practical systems, and most of the nonlinear problems in science and engineering can be simply described by nonlinear equation, effectively solving nonlinear equation (NE) has aroused great interests of the academic and industrial communities. In this paper, a robust zeroing neural network (RZNN) activated by a new power versatile activation function (PVAF) is proposed and analyzed for finding the solutions of dynamic nonlinear equations (DNE) within fixed time in noise polluted environment. As compared with the previous ZNN model activated by other commonly used activation functions (AF), the main improvement of the presented RZNN model is the fixed-time convergence even in the presence of noises. In addition, the convergence time of the proposed RZNN model is irrelevant to its initial states, and it can be computed directly. Both the rigorous mathematical analysis and numerical simulation results are provided for the verification of the effectiveness and robustness of the proposed RZNN model. Moreover, a successful robotic manipulator path tracking example in noise polluted environment further demonstrates the practical application prospects of the proposed RZNN models.

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A new fixed-time stabilization approach for neural networks with time-varying delays

Cited by 16 publications

References 61 publications

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

A robust zeroing neural network for solving dynamic nonlinear equations and its application to kinematic control of mobile manipulator

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