Efficient Learning for Selecting Important Nodes in Random Network

Li, Haidong; Xu, Xiaoyun; Peng, Yijie; Chen, Chun‐Hung

doi:10.1109/tac.2020.2989753

Cited by 9 publications

(16 citation statements)

References 27 publications

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“…Moreover, according to the stochastic differential in Definition 1 and the Theorem 1 in, 13 one can deduce that…”

Section: Stochastic Finite-time Stability Theoremmentioning

confidence: 98%

“…where 1 2 trace g T ∂ 2 V ∂x 2 g is positive semi-definite calling the Herssian term. Definition 2: 13 For the system (1), there exists an unique solution x 0 2 R n , and the setting time T ðx 0 , εÞ is finite almost surely. For t ≥ T ðx 0 , εÞ, such that Eðkxðt; x 0 ÞkÞ < ε, Then, the solution of the system (1) is semi-globally finite-time stability in probability (SGFSP).…”

Section: Stochastic Finite-time Stability Theoremmentioning

confidence: 99%

“…where 0 < m < m, 0 < j9ðtÞj < 9 and m, 9 are positive unknown constants. As mentioned before, four conditions can be formulated by equation ( 18) such as saturation, 13 dead-zone, backlash, and hysteresis. 14 To end this section, some useful assumptions and lemmas are given for the design of controllers.…”

Section: Input Nonlinearitiesmentioning

confidence: 99%

See 2 more Smart Citations

Event-triggered adaptive neural finite-time tracking control for non-strict feedback stochastic nonlinear systems with unknown time-varying control directions and input nonlinearities

Yue

Zhang

Chen

2022

Journal of Low Frequency Noise, Vibration and Active Control

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This paper focuses on dynamic-surface-based event-triggered adaptive neural finite-time tracking control for a category of non-strict feedback stochastic nonlinear system subject to unknown time-varying control directions, and input nonlinearities. Firstly, an improved event-triggered mechanism is presented, which simultaneously considers tracking error variables and compensating for event errors in threshold value. Secondly, a novel Lemma is provided to address concurrently the issues of Nussbaum terms in stability analysis and stochastic system finite-time stability. Thirdly, neural networks are utilized to cope with unknown functions. In addition, the Nussbaum functions are applied to identify time-varying unknown control directions and input nonlinearities. Moreover, the errors of the dynamic surface technique are compensated successfully by error compensation signals. By combining the proposed Lemma 1 and event-triggered adaptive neural finite-time control scheme, finite-time stability of considered systems in probability can be guaranteed, the tracking error can drive to a small neighborhood of the origin in finite time, and the Zeno behavior can be excluded. Finally, the effectiveness of the designed control scheme has been demonstrated through a second-order numerical system and a mass-spring-damper example.

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“…Moreover, according to the stochastic differential in Definition 1 and the Theorem 1 in, 13 one can deduce that…”

Section: Stochastic Finite-time Stability Theoremmentioning

confidence: 98%

Section: Stochastic Finite-time Stability Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Event-triggered adaptive neural finite-time tracking control for non-strict feedback stochastic nonlinear systems with unknown time-varying control directions and input nonlinearities

Yue

Zhang

Chen

2022

Journal of Low Frequency Noise, Vibration and Active Control

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show abstract

“…Nonlinear systems widely exist in practical applications such as robot systems, flight systems, and mechanical systems, whose control and design have been the focus in control fields for many years 1‐12 . With the development of the basic nonlinear stability theory, many effective design techniques have been emerged for nonlinear systems with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…As one of these techniques, the recursive backstepping proposed by Krstić 1 with Lyapunov function design has been a foundation approach of strict‐feedback nonlinear system control. Based on this approach, many significant developments have been achieved for various nonlinear systems; see, eg, References 5‐12.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based tracking control for constrained nonlinear systems with mismatching disturbances and its application

Min

Fei

et al. 2020

Intl J Robust & Nonlinear

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In this article, a novel output-feedback control scheme is proposed for nonlinear systems involving unknown input saturation, control gain, and unmeasurable disturbances under the condition of an output constraint. First, a proper barrier Lyapunov function and an opportune backstepping are used to alleviate the control burdens caused by output constraint. Then, a novel auxiliary system is introduced with appropriate coordinate transformation, which can create extra freedoms to attenuate the effects brought by input saturation. As a remarkable feature, a composite observer with the estimate of unknown control gain is constructed on the basis of disturbance observer-based control and state observer control. It can deal with the unmeasurable disturbances and states simultaneously. According to the proposed scheme, it is proven that all the signals in the closed-loop system are uniformly ultimately bounded, and the tracking error is bounded by the design parameters and saturated input error without the violation of the output constraint. Finally, the effectiveness of the proposed control is verified by two simulation examples.

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Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

Han

2021

Asian Journal of Control

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In the paper, a new control algorithm to realize the tracking control for a class of stochastic nonlinear systems with both full state constraints and input saturation is put forward using multi-dimensional Taylor network (MTN) approach. By introducing a continuous function, the input saturation is converted into a linear model with bounded error. With the help of approximation properties of MTN, by proposing barrier Lyapunov functions (BLFs) and involving the idea of state-constrained control into backstepping technique, a simple and efficient control scheme is developed. The proposed control scheme can guarantee the system states fall in the given constrained bounds as well as keeping the resulting control system stable. The effectiveness of the proposed control method is demonstrated through two examples.

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Efficient Learning for Selecting Important Nodes in Random Network

Cited by 9 publications

References 27 publications

Event-triggered adaptive neural finite-time tracking control for non-strict feedback stochastic nonlinear systems with unknown time-varying control directions and input nonlinearities

Event-triggered adaptive neural finite-time tracking control for non-strict feedback stochastic nonlinear systems with unknown time-varying control directions and input nonlinearities

Observer‐based tracking control for constrained nonlinear systems with mismatching disturbances and its application

Adaptive control of a class of stochastic nonlinear systems with full state constraints and input saturation using multi‐dimensional Taylor network

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