Finite-time stabilization control of memristor-based neural networks

Cai, Zhiping; Huang, Lihong; Zhu, Mingxun; Wang, Dongshu

doi:10.1016/j.nahs.2015.12.001

Cited by 51 publications

(23 citation statements)

References 27 publications

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“…Typically, the sliding mode control method includes determining the sliding surface as the system state functions and using finite-time stability theory in [19] to prove that the trajectories of closed-loop system arrive this surface infinite time. In order to solve the disadvantages in the typical sliding mode control, a neural network is added.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Relying on Lyapunov stability theory in [18][19][20][21][22][23][24][25][26][27][28], it is demonstrated that the proposed control method ensures the outputs of all agents can trace the reference trajectory under the condition that the communication topology between the agents is described by a directed graph (Graph theory). In this research, for the first time, the NN in collaboration with PID sliding mode control [29,30] is implemented to the uncertain nonlinear multi-agent systems for solving the robust adaptive consensus problem.…”

Section: Introductionmentioning

confidence: 99%

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Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Trong

Duc

2017

Energies

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Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation.In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Trong

Duc

2017

Energies

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“…In particular many studies have focused on such typical collective behaviors as stability and synchrony of CDNs. Till now, many fruitful research avenues have been pursued in and the references therein.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is necessary to design suitable and effective controllers for CDNs to make them realize stabilization. Hitherto, many useful control schemes have been proposed to stabilize some kind of networks . For example, based on a parameter‐dependent Lyapunov function, the decentralized controller design method was proposed for linear and sector‐nonlinear CDNs in .…”

Section: Introductionmentioning

confidence: 99%

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Stabilization of Time‐Varying and Disturbed Complex Dynamical Networks with Different‐Dimensional Nodes and Uncertain Nonlinearities

Zhang

Lei

Wang

et al. 2017

Asian Journal of Control

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This paper investigates the stabilization problem for time-varying and disturbed complex dynamical networks (CDNs) with different-dimensional nodes and uncertain nonlinearities. To be consistent with the properties of real-world networks, both the disturbances of our networks and the nonlinear structures of the nodes permit are completely unknown but bounded. Furthermore, the norm bounds of the uncertain nonlinearities and disturbances (NBUND) are applied to design the stabilization controllers. When the NBUND are known in advance, some decentralized state feedback controllers are proposed to stabilize our networks. And when they are unknown, adaptive decentralized stabilization schemes are brought forward for our network models. The effectiveness and feasibility of our theoretical results are verified by two simulation examples.

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Stabilization of delayed Cohen‐Grossberg BAM neural networks

Rajivganthi

Rihan

Shanmugam

2017

Math Methods in App Sciences

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MOS Classification: 34H15; 93C40; 93D15This paper deals with finite-time stabilization results of delayed Cohen-Grossberg BAM neural networks under suitable control schemes. We propose a state-feedback controller together with an adaptive-feedback controller to stabilize the system of delayed Cohen-Grossberg BAM neural networks. Stabilization conditions are derived by using Lyapunov function and some algebraic conditions. We also estimate the upper bound of settling time functional for the stabilization, which depends on the controller schemes and system parameters.Two illustrative examples and numerical simulations are given to validate the success of the derived theoretical results.

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Finite-time stabilization control of memristor-based neural networks

Cited by 51 publications

References 27 publications

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Stabilization of Time‐Varying and Disturbed Complex Dynamical Networks with Different‐Dimensional Nodes and Uncertain Nonlinearities

Stabilization of delayed Cohen‐Grossberg BAM neural networks

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