Adaptive neural network control for Markov jumping systems against deception attacks

Wu, Junhui; Qin, Gang; Cheng, Jun; Cao, Jinde; Yan, Huaicheng; Katib, Iyad

doi:10.1016/j.neunet.2023.09.027

Cited by 9 publications

(1 citation statement)

References 42 publications

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“…Furthermore, due to the good nonlinear approximation ability, the NN technique and fuzzy logic systems (FLSs) are becoming more widely used, respectively [23][24][25]. Combined with the backstepping approach, many adaptive control strategies have been applied to Markov jump nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

Yao,

Yuan,

Chen

et al. 2024

Fractal Fract

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Based on an adaptive neural control scheme, this paper investigates the consensus problem of random Markov jump multi-agent systems with full state constraints. Each agent is described by the fractional-order random nonlinear uncertain system driven by random differential equations, where the random noise is the second-order stationary stochastic process. First, in order to deal with the unknown functions with Markov jump parameters, a radial basis function neural network (RBFNN) structure is introduced to achieve approximation. Second, for the purpose of keeping the agents’ states from violating the constraint boundary, the tan-type barrier Lyapunov function is employed. By using the stochastic stability theory and adopting the backstepping technique, a novel adaptive neural control design method is presented. Furthermore, to cope with the differential explosion problem in the design course, the extended state observer (ESO) is developed instead of neural network (NN) approximation or command filtering techniques. Finally, the exponentially noise-to-state stability in the mean square is analyzed rigorously by the Lyapunov method, which guarantees the consensus of the considered multi-agent systems and all the agents’ outputs are bounded in probability. Two simulation examples are provided to verify the effectiveness of the suggested control strategy.

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Section: Introductionmentioning

confidence: 99%

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

Yao,

Yuan,

Chen

et al. 2024

Fractal Fract

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Adaptive event‐triggered lateral control for autonomous vehicle system under stochastic‐sampling subject to dynamic quantization

Jiang,

Luo,

Cheng

et al. 2024

Intl J Robust & Nonlinear

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SummaryIn this paper, we delve into the intricate problem of lateral control in autonomous vehicles, utilizing adaptive event triggering, dynamic quantizers, and incorporating stochastic sampling. By integrating the Adaptive Event‐Triggering Scheme (AETS) and dynamic quantizer in dual channels—namely the sensor‐to‐controller and controller‐to‐observer channels—we aptly cater to the multifaceted road conditions faced by autonomous vehicles. Moreover, in light of Denial of Service (DoS) attacks, our controllers ensure system stability amidst stochastic sampling. While ensuring an effective reduction in the amount of network communication data, the efficiency of the output feedback controllers is also significantly improved, thus enabling the closed‐loop system to be strictly dissipative performance stabilized. To substantiate the efficacy of our proposed method, simulation experiments were rigorously conducted using the Carsim‐Simulink platform, highlighting the enhanced safety of autonomous vehicles in real‐world operations.

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Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

Zhao,

Shi,

Zhang

et al. 2024

Chaos, Solitons & Fractals

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Adaptive neural network control for Markov jumping systems against deception attacks

Cited by 9 publications

References 42 publications

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

Adaptive event‐triggered lateral control for autonomous vehicle system under stochastic‐sampling subject to dynamic quantization

Event-triggering adaptive neural network output feedback control for networked systems under false data injection attacks

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