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Full Length Article | Open Access

Quasi-synchronous control of uncertain multiple electrohydraulic systems with prescribed performance constraint and input saturation

Shuai LIa, Qing GUOa( ), Yan SHIb, Yao YANa, Dan JIANGc
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Peer review under responsibility of Editorial Committee of CJA.

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Abstract

This article focuses on the high accuracy quasi-synchronous control issue of multiple electrohydraulic systems (MEHS). In order to overcome the negative effects of parameter uncertainty and external load interference of MEHS, a kind of finite-time disturbance observer (FTDO) via terminal sliding mode method is constructed based on the MEHS model to achieve fast and accuracy estimation and compensation ability. To avoid the differential explosion in backstepping iteration, the dynamic surface control is used in this paper to guarantee the follower electrohydraulic nodes synchronize to the leader motion with a better performance. Furthermore, a time-varying barrier Lyapunov function (tvBLF) is adopted during the controller design process to constraint the output tracking error of MEHS in a prescribed performance with time-varying exponential function. As the initial state condition is relax by tvBLF, the input saturation law is also adopted during the controller design process in this paper to restrain the surges of input signals, which can avoid the circuit and mechanical structure damage caused by the volatile input signal. An MEHS experimental bench is constructed to verify the effectiveness of the theoretical conclusions proposed in this paper and the advantages of the proposed conclusions in this paper are illustrated by a series of contradistinctive experimental results.

Keywords

Input saturationBacksteppingDynamic surfaceFinite-time disturbance observerMultiple Electrohydraulic SystemsOutput error constraintQuasi-synchronous control

References

1

Hang J, Li YH, Yang LM. A novel low pressure-difference fluctuation electro-hydraulic large flowrate control valve for fuel flowrate control of aeroengine afterburner system. Chin J Aeronaut 2022;35(11):363–76.
Crossref Google Scholar
2

Zhang JH, Chen Y, Xu B, et al. Effects of splined shaft bending rigidity on cylinder tilt behaviour for high-speed electro-hydrostatic actuator pumps. Chin J Aeronaut 2019;32(2):499–512.
Crossref Google Scholar
3

Yao JJ, Di DT, Jiang GL, et al. Real-time acceleration harmonics estimation for an electro-hydraulic servo shaking table using Kalman filter with a linear model. IEEE Trans Control Syst Technol 2014;22(2):794–800.
Crossref Google Scholar
4

Yang CF, Qu ZY, Han JW. Decoupled-space control and experimental evaluation of spatial electrohydraulic robotic manipulators using singular value decomposition algorithms. IEEE Trans Ind Electron 2014;61(7):3427–38.
Crossref Google Scholar
5

He W, Ge SS. Cooperative control of a nonuniform gantry crane with constrained tension. Automatica 2016;66:146–54.
Crossref Google Scholar
6

Helian BB, Chen Z, Yao B. Energy-saving and accurate motion control of a hydraulic actuator with uncertain negative loads. Chin J Aeronaut 2021;34(5):253–64.
Crossref Google Scholar
7

Guo Q, Chen ZL. Neural adaptive control of single-rod electrohydraulic system with lumped uncertainty. Mech Syst Signal Proc 2021;146 106869.
Crossref Google Scholar
8

Ren HP, Jiao SS, Wang X, et al. Fractional order integral sliding mode controller based on neural network: theory and electro-hydraulic benchmark test. IEEE/ASME Trans Mechatron 2022;27(3):1457–66.
Crossref Google Scholar
9

Zhang Y, Wang SP, Shi J, et al. SAR performance-based fault diagnosis for electro-hydraulic control system: a novel FDI framework for closed-loop system. Chin J Aeronaut 2022;35(10):381–92.
Crossref Google Scholar
10

Guo Q, Chen ZL, Shi Y, et al. Synchronous control of multiple electrohydraulic actuators under distributed switching topologies with lumped uncertainty. J Frankl Inst 2022;359(9):4288–306.
Crossref Google Scholar
11

Swaroop D, Hedrick JK, Yip PP, et al. Dynamic surface control for a class of nonlinear systems. IEEE Trans Autom Control 2000;45(10):1893–9.
Crossref Google Scholar
12

Lu Y. Disturbance observer-based backstepping control for hypersonic flight vehicles without use of measured flight path angle. Chin J Aeronaut 2021;34(2):396–406.
Crossref Google Scholar
13

Bao CY, Wang P, Tang GJ. Integrated method of guidance, control and morphing for hypersonic morphing vehicle in glide phase. Chin J Aeronaut 2021;34(5):535–53.
Crossref Google Scholar
14
Duraiswamy S, Chiu GTC. Nonlinear adaptive nonsmooth dynamic surface control of electro-hydraulic systems. Proceedings of the 2003 American Control Conference; 2003 June 4-6; Colorado, USA. Piscataway: IEEE Press; 2003. p.3287–92.
Crossref
15

Jia Q, Tang WKS. Consensus of nonlinear agents in directed network with switching topology and communication delay. IEEE Trans Circuits Syst I 2012;59(12):3015–23.
Crossref Google Scholar
16

Li ZK, Ren W, Liu XD, et al. Consensus of multi-agent systems with general linear and lipschitz nonlinear dynamics using distributed adaptive protocols. IEEE Trans Autom Control 2013;58(7):1786–91.
Crossref Google Scholar
17

Du HB, He YG, Cheng YY. Finite-time synchronization of a class of second-order nonlinear multi-agent systems using output feedback control. IEEE Trans Circuits Syst I 2014;61(6):1778–88.
Crossref Google Scholar
18

Zuo ZQ, Zhang J, Wang YJ. Adaptive fault-tolerant tracking control for linear and lipschitz nonlinear multi-agent systems. IEEE Trans Ind Electron 2015;62(6):3923–31.
Google Scholar
19

Li HY, Wu Y, Chen M. Adaptive fault-tolerant tracking control for discrete-time multiagent systems via reinforcement learning algorithm. IEEE Trans Cybern 2021;51(3):1163–74.
Crossref Google Scholar
20

Qin JH, Zhang GS, Zheng WX, et al. Adaptive sliding mode consensus tracking for second-order nonlinear multiagent systems with actuator faults. IEEE Trans Cybern 2019;49(5):1605–15.
Crossref Google Scholar
21

Chen CLP, Ren CG, Du T. Fuzzy observed-based adaptive consensus tracking control for second-order multiagent systems with heterogeneous nonlinear dynamics. IEEE Trans Fuzzy Syst 2016;24(4):906–15.
Crossref Google Scholar
22

Wen GX, Chen CLP, Liu YJ, et al. Neural network-based adaptive leader-following consensus control for a class of nonlinear multiagent state-delay systems. IEEE Trans Cybern 2017;47(8):2151–60.
Crossref Google Scholar
23

Chen CLP, Wen GX, Liu YJ, et al. Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems. IEEE Trans Cybern 2016;46(7):1591–601.
Crossref Google Scholar
24

Guo Q, Li XC, Zuo ZY, et al. Quasi-synchronization control of multiple electrohydraulic actuators with load disturbance and uncertain parameters. IEEE/ASME Trans Mechatron 2021;26(4):2048–58.
Crossref Google Scholar
25

Yao JY, Jiao ZX, Ma DW. Extended-state-observer -based output feedback nonlinear robust control of hydraulic systems with backstepping. IEEE Trans Ind Electron 2014;61(11):6285–93.
Crossref Google Scholar
26

Shi Y, Li L, Yang J, et al. Center-based transfer feature learning with classifier adaptation for surface defect recognition. Mech Syst Signal Proc 2023;188 110001.
Crossref Google Scholar
27

Chen M, Wu QX, Cui RX. Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems. ISA Trans 2013;52(2):198–206.
Crossref Google Scholar
28

Won D, Kim W, Shin D, et al. High-gain disturbance observer-based backstepping control with output tracking error constraint for electro-hydraulic systems. IEEE Trans Control Syst Technol 2015;23(2):787–95.
Crossref Google Scholar
29

Liu YJ, Ma L, Liu L, et al. Adaptive neural network learning controller design for a class of nonlinear systems with time-varying state constraints. IEEE Trans Neural Netw Learn Syst 2020;31(1):66–75.
Crossref Google Scholar
30

Li DJ, Lu SM, Liu L. Adaptive NN cross backstepping control for nonlinear systems with partial time-varying state constraints and its applications to hyper-chaotic systems. IEEE Trans Syst Man Cybern Syst 2021;51(5):2821–32.
Crossref Google Scholar
31

Chen M, Yan K, Wu QX. Multiapproximator-based fault-tolerant tracking control for unmanned autonomous helicopter with input saturation. IEEE Trans Syst Man Cybern Syst 2022;52(9):5710–22.
Crossref Google Scholar
32

Ma ZQ, Huang PF. Adaptive neural-network controller for an uncertain rigid manipulator with input saturation and full-order state constraint. IEEE Trans Cybern 2022;52(5):2907–15.
Crossref Google Scholar
33

Won D, Kim W, Tomizuka M. Nonlinear control with high-gain extended state observer for position tracking of electro-hydraulic systems. IEEE/ASME Trans Mechatron 2020;25(6):2610–21.
Crossref Google Scholar
34

Ding SB, Wang ZS, Zhang HG. Quasi-synchronization of delayed memristive neural networks via region-partitioning-dependent intermittent control. IEEE Trans Cybern 2019;49(12):4066–77.
Crossref Google Scholar
35

Ren W, Beard RW. Distributed Consensus in Multi-vehicle Cooperative Control. London: Springer, London; 2008.
Crossref
36

Zhang HC, Song AG, Li HJ, et al. Adaptive finite-time control scheme for teleoperation with time-varying delay and uncertainties. IEEE Trans Syst Man Cybern Syst 2020;52(3):1552–66.
Crossref Google Scholar
37

Qiu YN, Liang XG, Dai ZY. Backstepping dynamic surface control for an anti-skid braking system. Control Eng Pract 2015;42:140–52.
Crossref Google Scholar
38

Tee KP, Ge SS, Tay EH. Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica 2009;45(4):918–27.
Crossref Google Scholar
Chinese Journal of Aeronautics
Volume 36 Issue 9,
September 2023
Pages 416-425
DOI: 10.1016/j.cja.2023.02.029
Cite this article:
LI S, GUO Q, SHI Y, et al. Quasi-synchronous control of uncertain multiple electrohydraulic systems with prescribed performance constraint and input saturation. Chinese Journal of Aeronautics, 2023, 36(9): 416-425. https://doi.org/10.1016/j.cja.2023.02.029

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Received: 05 August 2022
Revised: 14 September 2022
Accepted: 16 December 2022
Published: 24 February 2023
© 2023 Chinese Society of Aeronautics and Astronautics.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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