Event-triggered fixed-time adaptive neural tracking control for stochastic non-triangular structure nonlinear systems

Yao, Yangang; Tan, Jiyuan; Wu, Jian; Zhang, Xu

doi:10.1007/s00521-021-06210-4

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…In this simulation, the center of the first RBF NN is set on [−3, 3], the nodes number is 2 and the width of Gaussian functions is 0. Moreover, we compare the proposed ETC scheme with TTC scheme, fixed threshold scheme, 25 relative threshold scheme 43 in terms of the triggered number, integrated absolute error (IAE) and integrated absolute error (ITAE). 56 These four schemes are simulated under the same design parameters.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The desired scheme is completed in the following three steps. First, the system (1) is rewritten to an equivalent system with affine structure by MVT; 43 Second, select the appropriate coordinate transformation; Third, get the design process by the backstepping technique.…”

Section: Controller Designmentioning

confidence: 99%

“…$$ When the triggered condition in (50) is satisfied, the time

{t}_i

is updated to

{t}_{i+1}

, and the actuator will be executed by

\overset{\breve }{u}\left({t}_{i+1}\right)=\varrho \left({t}_{i+1}\right)

. The ETC with the fixed threshold scheme 25 and relative threshold scheme 26,43 have been addressed in stochastic nonlinear systems. Different from them, the proposed ETC mechanism is more flexible and can switch between them via hyperbolic tangent function.…”

Section: Controller Designmentioning

confidence: 99%

See 2 more Smart Citations

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

Ren

Liu

2022

Adaptive Control & Signal

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Summary This article is concerned with the issue of event‐triggered adaptive fixed‐time control with sensor and actuator faults for stochastic nontriangular structure nonlinear system (SNSNS). The main difficulty in this article is the design of a novel and stable controller for SNSNS to compensate for the effect of sensor and actuator faults with event‐triggered control (ETC) in a unified framework. The mean value theorem (MVT) and ETC method are introduced to solve the questions of nonaffine structure and communication burden. Meanwhile, with the aid of adaptive neural network and fixed‐time theory, a novel controller is proposed by backstepping technology, which can ensure all the closed‐loop signals are bounded in probability within a fixed time as well avoid Zeno behavior. At last, the simulation is carried out to demonstrate the effectiveness of the proposed scheme.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Controller Designmentioning

confidence: 99%

“…$$ When the triggered condition in (50) is satisfied, the time

{t}_i

is updated to

{t}_{i+1}

, and the actuator will be executed by

\overset{\breve }{u}\left({t}_{i+1}\right)=\varrho \left({t}_{i+1}\right)

Section: Controller Designmentioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

Ren

Liu

2022

Adaptive Control & Signal

Self Cite

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show abstract

“…In order to solve this problem, many effective ETC methods have been proposed. [26][27][28][29][30][31][32][33][34] In Reference 26, the finite-time control problem of uncertain nonlinear systems was solved by event-triggered strategy. In Reference 34, the event-triggered fixed-time control problem for a class of stochastic nontriangular nonlinear systems is studied.…”

Section: Introductionmentioning

confidence: 99%

“…This deficiency makes it difficult to apply these results to control of event‐triggered stochastic nonlinear systems. In order to solve this problem, many effective ETC methods have been proposed 26–34 . In Reference 26, the finite‐time control problem of uncertain nonlinear systems was solved by event‐triggered strategy.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered adaptive neural network control design for stochastic nonlinear systems with output constraint

Shen,

Wang,

Pan

2023

Adaptive Control & Signal

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SummaryThis paper is concerned with the adaptive neural network event‐triggered control (ETC) problem for stochastic nonlinear systems with output constraint. The influence of stochastic disturbance inevitably exists in many practical systems, which leads to system instability. Meanwhile, a novel tan type barrier Lyapunov function (Tan‐BLF) structure is proposed to deal with the constraint requirements of stochastic systems. In the sense of probability, the output constraints will not be violated during the operation of the system. In addition, the ETC strategy is adopted to reduce the burden of communication. The asymptotic stability of the closed‐loop system is guaranteed without violating output constraints. Meanwhile, the tracking error converges to a small region of the origin. Two simulations results demonstrate the effectiveness of theoretical analysis.

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Command filter-based adaptive fuzzy fixed-time tracking control for strict-feedback nonlinear systems with nonaffine nonlinear faults

Wang

Cui

2023

Neural Comput & Applic

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Event-triggered fixed-time adaptive neural tracking control for stochastic non-triangular structure nonlinear systems

Cited by 9 publications

References 45 publications

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

Event‐triggered adaptive neural control for stochastic nontriangular structure nonlinear systems with multisensor and actuator failures: The practical fixed‐time stability

Event‐triggered adaptive neural network control design for stochastic nonlinear systems with output constraint

Command filter-based adaptive fuzzy fixed-time tracking control for strict-feedback nonlinear systems with nonaffine nonlinear faults

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