Neural network–based adaptive composite dynamic surface control for electro-hydraulic system with very low velocity

Guo, Qinyang; Shi, Guanglin; Wang, Dongmei; He, Changyu

doi:10.1177/0959651817731976

Cited by 11 publications

(13 citation statements)

References 40 publications

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“…Inspired by the idea of DSC, 16,19 a new state variable b 1 with a time constant t 1 is introduced to avoid repeatedly differentiating the virtual control a 1 in the next procedure, and let a 1 pass through a first-order filter (equation ( 17)). Then, the filter error (equation ( 18)) is defined…”

Section: Event-triggered Mechanism Designmentioning

confidence: 99%

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

Zhang

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

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Section: Event-triggered Mechanism Designmentioning

confidence: 99%

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

Zhang

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The effects of inherent nonlinearity, dynamic changes and external uncertainties of the system. In the same year, in [50] an adaptive compound dynamic surface control strategy based on neural network for low-speed and high-accuracy tracking control of electro-hydraulic shaking table system in response to the problem of poor control of the shaking table due to nonlinearity is proposed. In addition, in [51] neural networks is used to solve the time lag problem of the vibration table substructure test system.…”

Section: Neural Network Controlmentioning

confidence: 99%

Recent Progress on Control Techniques of Shaking Table and Array Systems in China: An Overview

Gao¹,

Yuan²,

Wang³

et al. 2020

AJCEA

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Shaking table is one of the important equipments for seismic research. It can accurately reproduce the seismic wave in the laboratory to accurately study the structural response. Based on the summary of domestic shaking table and array system control algorithms, the applications of various control algorithms and their advantages and disadvantages are summarized. The development trend of the shaking table control algorithm is analyzed and explained. It can be used as a reference for research on control technology of shaking tables at home and abroad.

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“…Artificial intelligence methods such as c-regression model algorithm and RBF neural network were also presented to estimate unknown parameters and realize dynamic prognosis of the system. 26,27 Some research has used the composite adaptive control (CAC) to improve the controller performance, 28,29 and the CAC method combined with other control strategies have also been presented recently. 30,31 The prediction error in the CAC is usually defined as ϵ = φ θ ~ , where φ is exciting signal vector and θ ~ is estimation error vector, usually using the instantaneous data to update the adaptive law.…”

Section: Introductionmentioning

confidence: 99%

Composite adaptive control based on historical data for electro-hydraulic system driven by dual servo motor-pump with unknown disturbance

Zhang

Shi

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a composite adaptive control method to improve the position-tracking performance of an electro-hydraulic system driven by dual constant displacement pump and dual servo motor named as a novel electro-hydraulic system with unknown disturbance. A composite adaptive controller based on backstepping method is designed to estimate the uncertainties of electro-hydraulic control system, including the damping coefficient and elastic modulus. In order to release the persistent excitation condition of conventional adaptive control, which is often infeasible in practice, a prediction error based on the online historical data is used to update the estimated parameters. Furthermore, a disturbance observer is used to estimate the disturbance including the unmeasurable load force, friction and other unmodeled disturbance. The experiment results are provided and compared with other methods to verify the effectiveness of the proposed method, and the results have indicated that the proposed method has a better position-tracking performance with the convergent estimated parameters.

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Neural network–based adaptive composite dynamic surface control for electro-hydraulic system with very low velocity

Cited by 11 publications

References 40 publications

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

Recent Progress on Control Techniques of Shaking Table and Array Systems in China: An Overview

Composite adaptive control based on historical data for electro-hydraulic system driven by dual servo motor-pump with unknown disturbance

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