A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system

Feng, Hao; Song, Qianyu; Ma, Shugen; Ma, Wei; Yin, Chenbo; Cao, Donghui; Huang, Yu

doi:10.1016/j.isatra.2021.12.044

Cited by 100 publications

(47 citation statements)

References 38 publications

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“…The proposed system allowed the reduction in absolute tracking error by 3-4 times compared to the classic PID algorithm. A similar application of the RBF-type network to control the hydraulic manipulator was presented by Tran [31], while Feng [32] and Nguyen [33] developed adaptive control methods based on the same type of neural network to control the servo valve under conditions of uncertainty and disturbance. All authors demonstrated the advantages of the proposed approach compared to standard regulators.…”

Section: Neural Controlmentioning

confidence: 97%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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Section: Neural Controlmentioning

confidence: 97%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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“…[26][27][28] Zhou et al, 29 developed a hybrid control scheme which is consisted of a nonsingular fast terminal sliding mode method and RBF neural network, and the RBFNN algorithm was employed to estimate the composite disturbance for an underwater vehicle. Feng et al 30 proposed a novel sliding mode control algorithm that combined RBF neural network with adaptive control to estimate and compensate perturbation and unmodeled part of a nonlinear robotic excavator.…”

Section: Introductionmentioning

confidence: 99%

Decoupled adaptive terminal sliding mode control strategy for a 6-DOF electro-hydraulic suspension test rig with RBF coupling force compensator

Zhou

Wan

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Aiming at the problems of poor tracking accuracy, low convergence speed, weak robustness of a 6-DOF electro-hydraulic suspension test system under system uncertainties and external disturbance, this article proposes double closed-loop control scheme. In the outer loop controller, this article designs a new decoupled adaptive fast nonsingular terminal sliding mode (DAFNTSM) control to achieve fast convergence rate, high accurate and adaptive disturbances estimation during trajectory tracking. Additionally, a coupling force compensator based on RBF neural network is employed, which can reduce online calculation quantity and guarantee accuracy. For six inner loop controllers, each hydraulic actuator can precisely follow the required force solved by outer loop controller. The hard-in-loop experiment results reveal that the control approach we proposed in this paper realize superior trajectory tracking characteristics in comparison with the conventional nonsingular terminal sliding mode (CNTSM) method.

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“…Fei et al (2021c) introduced the fractional sliding mode SMC multilayer recurrent fuzzy neural control used to micro gyroscope. Feng et al (2022) initiatively applied a novel adaptive SMC controller based on the RBF neural model for an electro-hydraulic servo plant. Liu et al (2021c) proposed an adaptive RBF neural-based control for a robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique

Ánh

Đạt

2023

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Purpose The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.Design/methodology/approach In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.Findings A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.Originality/value A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.

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A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system

Cited by 100 publications

References 38 publications

Artificial Intelligence Methods in Hydraulic System Design

Artificial Intelligence Methods in Hydraulic System Design

Decoupled adaptive terminal sliding mode control strategy for a 6-DOF electro-hydraulic suspension test rig with RBF coupling force compensator

Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique

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