Event‐triggered neural control for non‐strict‐feedback systems with actuator failures

Xu, Yan; Zhou, Qi; Li, Tieshan; Liang, Hongjing

doi:10.1049/iet-cta.2018.5403

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…) is a smooth function, seeing [1][2][3]5]. If the ETC is designed in the channel of controller to actuator like [40][41][42][43][44], it has u(t) = v(t j ) for the inter-event time t ∈ (t j , t j+1 ], where v(t) = h n (x, t) is the command control signal. Denote the tracking error in the ith step as z i+1 = x i+1 − α i .…”

Section: Problem Formulationmentioning

confidence: 99%

Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

Deng

Wang

2021

Preprint

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This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with the time-varying disturbances. While the fuzzy logic systems (FLSs) serve as the approximators to the unknown dynamics, the compound disturbance is comprised of the time-varying disturbance and the approximation error of the FLS. An event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of "algebraic loop", seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC) that functions in the channel of sensor to controller. The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. While the satisfactory learning performance of the FLSs and the compound disturbances estimation are maintained, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment is carried out to exemplify the effectiveness of the proposed control scheme.

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Section: Problem Formulationmentioning

confidence: 99%

Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

Deng

Wang

2021

Preprint

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“…Following the backstepping design, the virtual control laws for (1) at the ith step usually has the form of [1][2][3]5]. If the ETC is designed in the channel of controller to actuator like [40][41][42][43][44], it has u(t) = v(t j ) for the inter-event time t ∈ (t j , t j+1 ], where v(t) = h n (x, t) is the command control signal. Denote the tracking error in the ith step as z i+1 = x i+1 − α i .…”

Section: Remarkmentioning

confidence: 99%

“…As one of the hotspots in the control field, ETC has attracted compelling interests in recent years. Due to its convincible merit to save the communication load, ETC has been widely employed in the strict-feedback systems [31][32][33][34][35][36][37][38][39], the nonstrict-feedback systems [40][41][42][43][44][45], and the other industrial plants [46][47][48][49][50][51][52][53]. It can be found that [31-35, 40-44, 46, 49-51] only addressed the ETC in the channel of controller to actuator, namely the real control inputs kept unchanged by the zero order holder (ZOH) during the inter-event time and replaced by the command control signals at the triggering instants.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered compound learning tracking control of nonstrict-feedback nonlinear systems in sensor-to-controller channel

Deng

Wang

2021

Nonlinear Dyn

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This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with the time-varying disturbances. While the fuzzy logic systems (FLSs) serve as the approximators to the unknown dynamics, the compound disturbance is comprised of the time-varying disturbance and the approximation error of the FLS. An event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of "algebraic loop", seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based eventtriggered control (ETC) that functions in the channel of sensor to controller. The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. While the satisfactory learning performance of the FLSs and the compound disturbances estimation are maintained, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment is carried out to exemplify the effectiveness of the proposed control scheme.

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“…However, in the case of limited network resources, it will increase the difficulty of control design. Meanwhile, as a feasible technology to save network communication resources, event-triggered control has been widely used in linear and non-linear systems [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. For example, in [29], based on event-triggered mechanism and command filtering, the issue of tracking control in finite time is addressed for stochastic non-linear systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control for non‐strict‐feedback stochastic uncertain non‐linear systems based on event‐triggered strategy

Lian

Xia

Sun

et al. 2021

IET Control Theory & Appl

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This paper investigates the issue of adaptive fuzzy control for a category of non-strictfeedback stochastic uncertain non-linear systems. Based on event-triggered mechanism, an adaptive fuzzy controller with a simple form is designed by combining backstepping technique with fuzzy logic system. The devised controller not only ensures the output signal tracking reference signal and all signals of the closed-loop system are bounded in probability but also reduces the communication load between controller and actuator. In addition, the feasibility of the considered event-triggered mechanism is guaranteed by eliminating Zeno behaviour. Finally, the validity of the proposed result is proved by an example. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Event‐triggered neural control for non‐strict‐feedback systems with actuator failures

Cited by 9 publications

References 42 publications

Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel

Event-triggered compound learning tracking control of nonstrict-feedback nonlinear systems in sensor-to-controller channel

Adaptive fuzzy control for non‐strict‐feedback stochastic uncertain non‐linear systems based on event‐triggered strategy

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