Adaptive Asymptotic Tracking Control of Uncertain Nonlinear Systems Based on Taylor Decoupling and Event-Trigger

Wu, Li‐Bing; Park, Ju H.; Xie, Xiangpeng; Li, Yafeng

doi:10.1109/tsmc.2020.3034579

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…Remark 2. The event-triggered mechanism in this paper is inspired by pioneer works Wu et al 38 and Li et al 39 . Different from previous works of the relative threshold method in, 60 an improved event-triggered mechanism is presented here based on the relative threshold strategy by incorporating tracking error function Z 0 with Hyperbolic Cosecant function csch(·) in threshold value.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…Therefore, in, 37 Kong et al designed a relative threshold strategy that varies dynamically with the input signal to improve the controller’s performance. Moreover, in, 38 Wu et al considered the tracking error variables in the threshold value based on the relative threshold strategy to improve the system’s tracking performance. In, 39 Li et al proposed a new event-triggering mechanism that reduced conservatism in control design and stability analysis while simultaneously considering the system’s finite-time stability.…”

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confidence: 99%

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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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Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

mentioning

confidence: 99%

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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“…As the increase of the safety and reliability requirements for actual control systems, the fault tolerant control (FTC) has become a hot topic in the industrial and theoretical areas and some excellent works have emerged, see [1][2][3][4][5][6][7][8][9][10][11][12][13]. Meanwhile, as one of the important means of FTC, the research of fault diagnosis (FD) has become more and more frequent.…”

Section: Introductionmentioning

confidence: 99%

Fault decomposition-based convergent FE and FTC for Lipschitz nonlinear systems

Wang

Huang

2023

Nonlinear Dyn

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The problem of fault estimation and fault tolerant control for Lipschitz nonlinear systems subject to actuator and sensor faults is investigated in this paper. A fault decomposition technique is introduced to design a set of iterative estimation observers for the system in consideration. The obtained mean sequence of estimates, under certain conditions, converge to the true values of state and multi-faults. A convergence theory with a theoretical proof is given. A sufficient LMIbased stability condition dependent on the coefficient matrix of disturbance is proposed to determined the observer gains. Finally, an output feedback FTC is designed to stabilize the Lipschitz nonlinear system. The longitudinal dynamics of an aircraft is applied to test the proposed method.

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“…21,22 In comparison with the conventional periodic samplings, the ETC strategy is more desirable when limited bandwidth is encountered. [23][24][25][26] To be specific, an event-driven adaptive finite-time control scheme for stochastic nonlinear systems was proposed to improve the transmission efficiency in the controller-to-actuator (C-A) channel. 23 Moreover, an input-based triggering approach was integrated with an asymptotic tracking control strategy of limited-bandwidth nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

“…23 Moreover, an input-based triggering approach was integrated with an asymptotic tracking control strategy of limited-bandwidth nonlinear systems. 24 Meanwhile, an adaptive event-triggered controller working in an aperiodic manner was developed for MIMO nonlinear systems in Reference 25. It should be emphasized that the aforementioned event-triggered strategies only aim at the resource saving of the C-A channel, while the feedback information was still transmitted periodically from the sensor to the controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic surface control of MIMO nonlinear systems: A hybrid event triggering mechanism

Huang,

Zong,

et al. 2023

Adaptive Control & Signal

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SummaryThis paper addresses the event‐triggered stabilization control for multi‐input and multi‐output (MIMO) nonlinear systems. To handle the “explosion of complexity”, the dynamic surface control (DSC) technology is introduced to the proposed control scheme. In addition, the event‐driven idea is adopted to mitigate the data transmission pressure in sensor‐to‐controller and controller‐to‐actuator channels. Considering that the non‐derivable virtual controllers are commonly constructed in the event‐triggered control schemes aiming at saving dual‐channel resources, in this paper, a hybrid event‐triggering mechanism with multiple event detectors is developed to obtain differentiable virtual control signals, thereby providing a suitable application environment for the DSC technology. Meanwhile, an output‐based adaptive threshold is designed to obtain more communication benefits compared with the fixed threshold strategy. Finally, all signals in the closed‐loop system are ensured to be bounded under a standard Lyapunov stability analysis framework, and the effectiveness of the proposed control scheme is validated by a simulation example.

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Adaptive Asymptotic Tracking Control of Uncertain Nonlinear Systems Based on Taylor Decoupling and Event-Trigger

Cited by 17 publications

References 43 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

Fault decomposition-based convergent FE and FTC for Lipschitz nonlinear systems

Adaptive dynamic surface control of MIMO nonlinear systems: A hybrid event triggering mechanism

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