Command filter‐based event‐triggered adaptive neural network control for uncertain nonlinear time‐delay systems

Bao-min, LI; Xia, Jianwei; Sun, Wei; Park, Ju H.; Sun, Zhigang

doi:10.1002/rnc.5111

Cited by 24 publications

(11 citation statements)

References 41 publications

(78 reference statements)

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“…where {t 𝜄,𝜅 h} represents the sequence of successful transmission instants ((t 0,𝜅+1 h) = 𝜅T), which is generated by the ETM in (7),…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In this subsection, based on the 𝔏 2 − 𝔏 ∞ performance analysis result in Theorem 2, we present a co-design method of the quantized 𝔏 2 − 𝔏 ∞ filtering gain and weighting matrix H in ETM (7).…”

Section: 𝕷 2 − 𝕷 ∞ Filter Designmentioning

confidence: 99%

“…When T = 2, the other values are same as before. Then the relationship between T min off and the decay rate 𝜓 2T is given in F I G U R E 3 Release time intervals under the DoS attacks (7) on the allowable largest value h max and allowable smallest value T min off are shown Table 3. From Table 3, it can be seen that h max and T min off become smaller with the increase of 𝜎 (indicating the decreasing utilization of network resources).…”

Section: Numerical Examplementioning

confidence: 99%

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Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

Zhou

Chang

2022

Intl J Robust & Nonlinear

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This article deals with the reliable event‐triggered quantized frakturL2prefix−frakturL∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering issue for neural networks with exterior interference under denial‐of‐service attacks. In order to lighten the load of communication channels and save network resources, a resilient event‐triggered mechanism and a quantization scheme are employed, simultaneously. By applying a piecewise Lyapunov–Krasovskii functional method, sufficient conditions containing limitations of denial‐of‐service attacks are derived to guarantee that the filter error system is exponentially stable as well as possesses a prescribed frakturL2prefix−frakturL∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ disturbance attenuation performance. Then, a co‐design method of the desired quantized frakturL2prefix−frakturL∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering gain matrix and event‐triggering parameter can be obtained provided that the linear matrix inequalities have a feasible solution. Finally, the usefulness of the proposed design method is demonstrated by a numerical example.

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“…where {t 𝜄,𝜅 h} represents the sequence of successful transmission instants ((t 0,𝜅+1 h) = 𝜅T), which is generated by the ETM in (7),…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: 𝕷 2 − 𝕷 ∞ Filter Designmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

Zhou

Chang

2022

Intl J Robust & Nonlinear

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“…Specifically, in view of FLS and the adaptive backstepping design approach, an adaptive finite-time control issue for nonlinear systems with actuator faults has been studied in [8]. [14] studied the robust NN control issue for nonlinear time-delay systems by employing command filters based adaptive backstepping technique.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

Xia

Sun

et al. 2021

Preprint

Self Cite

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This paper addresses the event-triggered based adaptive asymptotic tracking control problem for switched nonlinear systems with unknown control directions based on neural network technique. A novel asymptotic tracking controller, in which Nussbaum functions are introduced to address the issue of unknown control directions, is designed by combining neural network control technology and event-triggered strategy. Different from the existing tracking control schemes, the proposed controller in this paper can guarantee that the tracking error ς 1 asymptotically converges to the origin and reduce the communication burden from the controller to the actuator. Finally, the effectiveness of the presented control design is proved by numerical examples.

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“…On the other hand, the LKFs have become a popular tool to solve time delays [8]. By combining NNs or FLSs with LKFs, several adaptive tracking control schemes were proposed for nonlinear strictfeedback systems with constant time delays in [9][10][11]. To solve time-varying delays, the LKFs and backstepping technique were utilized in [12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Finite-Time Tracking Control for Nonlinear Time-Varying Delay Systems With Full State Constraints and Input Saturation

2021

IEEE Access

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This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear time-varying delay systems subject to full state constraints and input saturation. The nonlinear state-dependent functions (NSDFs) are introduced to handle the asymmetric time-varying full state constraints without feasibility conditions. To cope with the unknown time-varying delays, the radial basis function neural networks (RBF NNs) and the finite covering lemma are utilized. Avoiding the use of the Lyapunov-Krasovskii functionals (LKFs), no restriction on the derivative of the time-varying delays is needed. Meanwhile, the effect of the input saturation is eliminated by the augmented function with an auxiliary control signal. The adaptive finite-time tracking controller with only one adaptive parameter is constructed by applying the command filter approach and backstepping technique. It is proved that the proposed controller can ensure that all signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed scheme is demonstrated by two simulation examples.

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Command filter‐based event‐triggered adaptive neural network control for uncertain nonlinear time‐delay systems

Cited by 24 publications

References 41 publications

Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

Event‐triggered quantized L2−L∞$$ {\mathfrak{L}}_2-{\mathfrak{L}}_{\infty } $$ filtering for neural networks under denial‐of‐service attacks

Neural Network-Based Event-triggered Adaptive Asymptotic Tracking Control for Switched Nonlinear Systems

Adaptive Finite-Time Tracking Control for Nonlinear Time-Varying Delay Systems With Full State Constraints and Input Saturation

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