Dynamic Event-Triggering Neural Learning Control for Partially Unknown Nonlinear Systems

Mu, Chaoxu; Wang, Ke; Qiu, Tie

doi:10.1109/tcyb.2020.3004493

Cited by 69 publications

(24 citation statements)

References 41 publications

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“…Remark 7. Compared with the existing literatures, especially, 16,26,27,29,32,43,46,51 the contributions of the proposed control method are listed as follows:…”

Section: Stability Analysismentioning

confidence: 99%

“…Remark Compared with the existing literatures, especially, 16,26,27,29,32,43,46,51 the contributions of the proposed control method are listed as follows: 1.The method proposed in this article extends the works of References 16,43, and 46 to achieve the optimal tracking control for a class of uncertain systems with partial loss of actuator effectiveness faults.…”

Section: Event‐triggered Guaranteed Cost Control Based On Adpmentioning

confidence: 99%

“…">2.A dynamic event‐triggered mechanism involved a dynamic rule is proposed in this article, which can significantly reduce the triggering times and calculation burden compared with static event‐triggered mechanism in References 26,27,29, and 51. 3.The dynamic event‐triggered mechanism proposed in Reference 32 requires the actor‐critic dual network structure and admissible initial control. In this article, a critic NN ADP algorithm with modified weights updating law is proposed to derive the optimal controller.…”

Section: Event‐triggered Guaranteed Cost Control Based On Adpmentioning

confidence: 99%

“…To further improve the execution efficiency of event‐based controller, a dynamic event‐triggered strategy with internal dynamic signal was proposed 31 . Recently, the dynamic event‐triggered mechanism was first introduced into ADP algorithm in Reference 32, in which a larger triggering interval was achieved with a larger triggering threshold. Then, in Reference 33, the dynamic event‐triggered ADP algorithm was further applied to industrial system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Guo

Yao

Bai

et al. 2021

Intl J Robust & Nonlinear

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In this article, an event-triggered guaranteed cost optimal tracking control problem is investigated for a class of uncertain nonlinear system with partial loss of actuator effectiveness faults. To begin with, an augmented system consisted of error system and reference system is constructed to simplify the tracking controller design. Then, in order to consider both actuator faults and system uncertainties in optimal tracking control, an improved discounted cost function is developed. Furthermore, a single critic neural network adaptive dynamic programming algorithm is utilized to implement the event-based approximate optimal controller design. Different from static event-triggered mechanism, the dynamic event-triggered mechanism proposed in this article with an internal dynamic signal can further improve execution efficiency. A Lyapunov-based stability analysis is given to demonstrate the uniformly ultimately bounded stability of the closed-loop system. Finally, two typical nonlinear simulations are presented to verify the developed control method.

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“…Remark 7. Compared with the existing literatures, especially, 16,26,27,29,32,43,46,51 the contributions of the proposed control method are listed as follows:…”

Section: Stability Analysismentioning

confidence: 99%

Section: Event‐triggered Guaranteed Cost Control Based On Adpmentioning

confidence: 99%

Section: Event‐triggered Guaranteed Cost Control Based On Adpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Guo

Yao

Bai

et al. 2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…In comparison with SETM, DETM can generate a larger execution interval and save resources more effectively. Therefore, DETM is used to solve different problems [15][16][17][18][19][20][21][22][23][24][25][26][27][28]. For networked switched linear systems with time-varying delays [18], a DETM is constructed to guarantee the exponential stability of the closed-loop system, and the simulation comparison is performed to show that the proposed DETM can reduce the times of data transmissions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

et al. 2021

Symmetry

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In this paper, the dynamic event-triggered tracking control issue is studied for a class of unknown stochastic nonlinear systems with strict-feedback form. At first, neural networks (NNs) are used to approximate the unknown nonlinear functions. Then, a dynamic event-triggered controller (DETC) is designed through the adaptive backstepping method. Especially, the triggered threshold is dynamically adjusted. Compared with its corresponding static event-triggered mechanism (SETM), the dynamic event-triggered mechanism (DETM) can generate a larger execution interval and further save resources. Moreover, it is verified by two simulation examples that show that the closed-loop stochastic system signals are ultimately fourth moment semi-globally uniformly bounded (SGUUB).

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Optimal control of partially unknown constrained‐input systems: A dynamic event‐triggered‐based approach

Zou

Zhang

2023

Optim Control Appl Methods

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This article presents an identifier‐based dynamic event‐triggered optimal control scheme for partially unknown constrained‐input systems. First, an event‐triggered‐based neural network (NN) identifier is constructed to estimate the unknown system dynamics. Then, an adaptive dynamic programming algorithm with actor‐critic NN structure is adopted to obtain an approximate solution of the Hamilton–Jacobi–Bellman equation. The above considers that transmitted measurements are only available at the triggering instants, and the update of all three NN weights depends on the established dynamic event‐triggered mechanism. Different from existing static event‐triggered mechanism, the proposed dynamic event‐triggered mechanism can further obtain a reasonable trade‐off between performance and communication resources by introducing a dynamic variable, and the Zeno behavior can be excluded by devising an exponential term. It is proved that all the closed‐loop system signals are uniformly ultimately bounded under the established event‐triggered mechanism. Finally, two numerical examples are provided, including the spring‐mass‐damper system, to validate the proposed control scheme.

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Dynamic Event-Triggering Neural Learning Control for Partially Unknown Nonlinear Systems

Cited by 69 publications

References 41 publications

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

Optimal control of partially unknown constrained‐input systems: A dynamic event‐triggered‐based approach

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