Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 230

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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

Feng, Hao, Song, Qianyu, Ma, Shoulei, Ma, Wei, Yin, Chenbo, Cao, Donghui, Yu, Hongfu·2022·ISA Transactions
DOI 10.1016/j.isatra.2021.12.044
Open this paper in CoChat — free account

Abstract

Accuracy and robust trajectory tracking for electro-hydraulic servo systems in the presence of load disturbances and model uncertainties are of great importance in many fields. In this work, a new adaptive sliding mode control method based on the RBF neural networks (SMC-RBF) is proposed to improve the performances of a robotic excavator. Model uncertainties and load disturbances of the electro-hydraulic servo system are approximated and compensated using the RBF neural networks. Adaptive mechanisms are designed to adjust the connection weights of the RBF neural networks in real time to guarantee the stability. A nonlinear term is introduced into the sliding mode to design an adaptive terminal sliding mode control structure to improve dynamic performances and the convergence speed. Moreover, a sliding mode chattering reduction method is proposed to suppress the chattering phenomenon. Three types of step, ramp and sine signals are used as the simulation reference trajectories to compare different controllers on a co-simulation platform. Experiments with leveling and triangle conditions are presented on a robotic excavator. Results show that the proposed SMC-RBF controller is superior to existing proportional integral derivative (PID) and sliding mode controller (SMC) in terms of tracking accuracy and disturbance rejection.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.isatra.2021.12.044

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 39

  1. Guo, 2018, Iterative learning based output feedback control for electro-hydraulic loading system of a gait simulator, Mechatronics, 54, 110, 10.1016/j.mechatronics.2018.07.011 · DOI
  2. Feng, 2018, Robotic excavator trajectory control using an improved GA based PID controller, Mech Syst Signal Process, 105, 153, 10.1016/j.ymssp.2017.12.014 · DOI · Paper page
  3. Zhang, 2019, The effects of control methods on energy efficiency and position tracking of an electro-hydraulic excavator equipped with zonal hydraulics, Autom Constr, 100, 129, 10.1016/j.autcon.2019.01.003 · doi:10.1016/j.autcon.2019.01.003
  4. Liu, 2020, Research on energy consumption of injection molding machine driven by five different types of electro-hydraulic power units, J Clean Prod, 242, 10.1016/j.jclepro.2019.118355 · DOI
  5. Kim, 2019, Discrete time delay control for hydraulic excavator motion control with terminal sliding mode control, Mechatronics, 60, 15, 10.1016/j.mechatronics.2019.04.008 · DOI
  6. Jin, 2020, Simulation of hydraulic transplanting robot control system based on fuzzy PID controller, Measurement, 164, 10.1016/j.measurement.2020.108023 · doi:10.1016/j.measurement.2020.108023
  7. Feng, 2019, Parameters identification and trajectory control for a hydraulic system, Isa Trans, 92, 228, 10.1016/j.isatra.2019.02.022 · DOI
  8. Wrat, 2020, Energy saving and Fuzzy-PID position control of electro-hydraulic system by leakage compensation through proportional flow control valve, Isa Trans, 101, 269, 10.1016/j.isatra.2020.01.003 · DOI
  9. Ding, 2017, Tracking control of electro-hydraulic servo multi-closed-chain mechanisms with the use of an approximate nonlinear internal model, Control Eng Pract, 58, 225, 10.1016/j.conengprac.2016.11.003 · doi:10.1016/j.conengprac.2016.11.003
  10. Bai, 2018, A least mean square based active disturbance rejection control for an inertially stabilized platform, Optik, 174, 609, 10.1016/j.ijleo.2018.08.099 · doi:10.1016/j.ijleo.2018.08.099
  11. Gao, 2020, Distributed active disturbance rejection control for ackermann steering of a four-in-wheel motor drive vehicle with deception attacks on controller area networks, Inform Sci, 540, 370, 10.1016/j.ins.2020.06.012 · DOI
  12. Guo, 2015, Robust H∞ positional control of 2-DOF robotic arm driven by electro-hydraulic servo system, Isa Trans, 59, 55, 10.1016/j.isatra.2015.09.014 · DOI
  13. Yang, 2020, Nonlinear adaptive output feedback robust control of hydraulic actuators with largely unknown modeling uncertainties, Appl Math Model, 79, 824, 10.1016/j.apm.2019.10.062 · DOI
  14. Yang, 2016, Adaptive integral robust control of hydraulic systems with asymptotic tracking, Mechatronics, 40, 8, 10.1016/j.mechatronics.2016.10.007 · DOI
  15. Feng, 2019, Identification and compensation of non-linear friction for a electro-hydraulic system, Mech Mach Theory, 141, 1, 10.1016/j.mechmachtheory.2019.07.004 · DOI
  16. Luo, 2019, Extended-state-observer-based output feedback adaptive control of hydraulic system with continuous friction compensation, J Franklin Inst, 356, 8414, 10.1016/j.jfranklin.2019.08.015 · DOI
  17. Liu, 2020, Trajectory tracking with quaternion-based attitude representation for autonomous underwater vehicle based on terminal sliding mode control, Appl Ocean Res, 104, 10.1016/j.apor.2020.102342 · doi:10.1016/j.apor.2020.102342
  18. Yang, 2021, Asynchronous fault-tolerant control for stochastic jumping singularly perturbed systems: An sh∞ sliding mode control scheme, Appl Math Comput, 389
  19. Zhang, 2020, Robust integral sliding mode control for uncertain switched systems under arbitrary switching rules, Nonlinear Anal-Hybri, 37
  20. Yin, 2019, Adaptive robust integral sliding mode pitch angle control of an electro-hydraulic servo pitch system for wind turbine, Mech Syst Signal Process, 133, 10.1016/j.ymssp.2018.09.026 · DOI
  21. Tang, 2011, Dynamic sliding mode control scheme for electro-hydraulic position servo system, Procedia Eng, 24, 28, 10.1016/j.proeng.2011.11.2596 · DOI
  22. Cerman, 2012, Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism, Expert Syst Appl, 39, 10269, 10.1016/j.eswa.2012.02.172 · DOI
  23. Li, 2019, Robust internal model control of servo motor based on sliding mode control approach, Isa Trans, 93, 199, 10.1016/j.isatra.2019.03.021 · doi:10.1016/j.isatra.2019.03.021
  24. Norsahperi, 2020, An improved optimal integral sliding mode control for uncertain robotic manipulators with reduced tracking error, chattering, and energy consumption, Mech Syst Signal Process, 142, 10.1016/j.ymssp.2020.106747 · doi:10.1016/j.ymssp.2020.106747
  25. Gao, 2020, Chattering-free model free adaptive sliding mode control for gas collection process with data dropout, J Process Control, 93, 1, 10.1016/j.jprocont.2020.07.003 · doi:10.1016/j.jprocont.2020.07.003
  26. Kumari, 2020, Event-triggered sliding mode control for a high-order system via reduced-order model based design, Automatica, 121, 10.1016/j.automatica.2020.109163 · doi:10.1016/j.automatica.2020.109163
  27. Sun, 2020, Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control, Appl Math Comput, 375
  28. Li, 2020, Objective-oriented genetic algorithm based dynamical sliding mode control for slurry level and air pressure in shield tunneling, Autom Constr, 109, 10.1016/j.autcon.2019.102987 · doi:10.1016/j.autcon.2019.102987
  29. Mishra, 2020, Development and implementation of induction motor drive using sliding-mode based simplified neuro-fuzzy control, Eng Appl Artif Intel, 91, 10.1016/j.engappai.2020.103593 · doi:10.1016/j.engappai.2020.103593
  30. Mirinejad, 2017, An RBF collocation method for solving optimal control problems, Robot Auton Syst, 87, 219, 10.1016/j.robot.2016.10.015 · doi:10.1016/j.robot.2016.10.015
  31. Han, 2020, Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton, Isa Trans, 97, 171, 10.1016/j.isatra.2019.07.030 · DOI
  32. Shi, 2019, Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system, Neurocomputing, 330, 490, 10.1016/j.neucom.2018.11.029 · DOI
  33. Sun, 2019, Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method, Measurement, 141, 217, 10.1016/j.measurement.2019.03.006 · doi:10.1016/j.measurement.2019.03.006
  34. Qi, 2013, Adaptive position tracking control of permanent magnet synchronous motor based on RBF fast terminal sliding mode control, Neurocomputing, 115, 23, 10.1016/j.neucom.2012.11.018 · doi:10.1016/j.neucom.2012.11.018
  35. Sharafian, 2020, Fractional sliding mode based on RBF neural network observer: Application to HIV infection mathematical model, Comput Math Appl, 79, 3179, 10.1016/j.camwa.2020.01.014 · doi:10.1016/j.camwa.2020.01.014
  36. Hui, 2020, Rbf-based adaptive sliding mode controller with extended state observer for load following of nuclear power plant, Nucl Eng Des, 360, 10.1016/j.nucengdes.2019.110465 · doi:10.1016/j.nucengdes.2019.110465
  37. Fang, 2015, Model reference adaptive sliding mode control using RBF neural network for active power filter, Int J Elec Power, 73, 249, 10.1016/j.ijepes.2015.05.009 · DOI
  38. Ye, 2014, Effects of groove shape of notch on the flow characteristics of spool valve, Energ Convers Manage, 86, 1091, 10.1016/j.enconman.2014.06.081 · DOI
  39. Feng, 2021, Trajectory control of electro-hydraulic position servo system using improved pso-pid controller, Autom Constr, 127, 10.1016/j.autcon.2021.103722 · DOI · Paper page

Cited by

See all 230

Metadata

Authors
Feng, H., Song, Q., Ma, S., Ma, W., Yin, C., Cao, D., & Yu, H.
Venue
ISA Transactions
Publisher
Elsevier BV
Published
2022-10
Volume
129
Issue
Pt A
Pages
472-484
ISSN
0019-0578 (ISSN-L)
DOI
10.1016/j.isatra.2021.12.044
OpenAlex
W4206149507
Cited by
230
References
2
FWCI
17.09
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • National Natural Science Foundation of China (52105064)
  • Nanjing University of Information Science and Technology (2021R042)
  • Transformation Program of Scientific and Technological Achievements of Jiangsu Province (SBA2020030413)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system. https://app.cochat.ai/papers/10.1016/j.isatra.2021.12.044
The paper
Feng, H., Song, Q., Ma, S., Ma, W., Yin, C., Cao, D., & Yu, H. (2022). A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system. ISA Transactions. https://doi.org/10.1016/j.isatra.2021.12.044

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.