Research on Position / Velocity Synergistic Control of Electro Hydraulic Servo System

Author(s): Bingwei Gao*, Yongtai Ye

Journal Name: Recent Patents on Mechanical Engineering

Volume 13 , Issue 4 , 2020


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Abstract:

Background: In some applications, the requirements of electro-hydraulic servo system are not only precise positioning, but also the speediness capability at which the actuator is operated.

Objective: In order to enable the system to achieve rapid start and stop during the work process, reduce the vibration and impact caused by the change of the velocity, at the same time improve the positioning accuracy, and further strengthen the stability and the work efficiency of the system, it is necessary to perform the synergistic control between the position and the velocity of the electrohydraulic servo system.

Methods: In order to achieve synergistic control between the position and the velocity, a control method of velocity feed-forward and position feedback is adopted. That is, based on the position control, the speed feed-forward is added to the outer loop as compensation. The position control adopts the PID controller, and the velocity control adopts the adaptive fuzzy neural network controller. At the same time, the position and velocity sensors are used for feedback, and the deviation signals between the position and the velocity obtained by superimposing the feedback are used as the final input of the control object, thereby controlling the whole system.

Results: The control effect of the designed position / velocity synergistic controller is verified by simulation and experiment. The results show that the designed controller can effectively reduce the vibration and impact caused by the change of the velocity, and greatly improve the response velocity and the position accuracy of the system.

Conclusion: The proposed method provides technical support for multi-objective synergistic control of the electro-hydraulic servo system, completes the requirements of multi-task operation, improves the positioning accuracy and response velocity of the electro-hydraulic servo system, and realizes the synergy between the position and the velocity. In this article, various patents have been discussed.

Keywords: Adaptive fuzzy neural network control, electro-hydraulic servo system, position/velocity synergistic control, PID control, position feedback, velocity feed-forward.

[1]
Xu XY, Lin H. Integrated design for permanent magnet synchronous motor servo systems based on dynamic sliding mode control. Transactions of China Electrotechnical Society 2014; 29(5): 77-83.
[2]
Moreno VJ. Velocity field control of robot manipulators by using only position measurements. J Franklin Inst 2007; 344(8): 1021-38.
[http://dx.doi.org/10.1016/j.jfranklin.2007.05.006]
[3]
Liu J, Ge SR, Zhu H, Tang CQ. Mine rescue robot power matching based on multi-objective particle swarm optimization. J Mech Eng 2015; 51(3): 18-28.
[http://dx.doi.org/10.3901/JME.2015.03.018]
[4]
Maver T. Dual valve systems for actuator control. US201715684651 (2017).
[5]
Brault J. Power-automated traction for skis. US201614998718 (2017).
[6]
Adams J, Amari M, Crowley T. Multivariable actuator pressure control. US201514927004 (2015).
[7]
Brault J. Multi-adaptable power automated traction apparatus. US201414120857 (2014).
[8]
Silvestro G. Servo control device, in particular an electro-hydraulic actuator for flight control. EP20030290142 (2010).
[9]
Takagi S. Hydraulic servo control actuator device. JP19980345754 (2000).
[10]
Wang YX, Liu LC, Mu SJ, Wu C. Constrained multi-objective optimization evolutionary algorithm. J Tsinghua Univ 2005; 45(1): 103-6.
[11]
Zhang KF, Cheng H, Li Y. Multi-objective harmonious colony decision algorithm for more efficiently evaluating assembly sequences. Assembly Autom 2008; 28(4): 348-55.
[http://dx.doi.org/10.1108/01445150810904503]
[12]
Enan E. Synergistic pest-control compositions. EP20180200447 (2019).
[13]
Mann R. Synergistic weed control from applications synergistic weed control from applications of clomazone and benzobicyclon. EP20180171405 (2018).
[14]
Mehta S, Turini B. Synergistic silica scale control. US201314386925 (2013).
[15]
Enan E. Synergistic pest-control compositions. US20080532604 (2014).
[16]
Dotan A. Synergistic agricultural pest control. AU20120232689 (2012).
[17]
Yao C, Mathieson J. Synergistic mixtures for fungal control in cereals. WO2018US30559 (2018).
[18]
Wei Z, Xu XF, Zhan DC, Deng SC. Multi objective optimization model for collaborative multi-echelon inventory control in supply chain. Acta Automatica Sinica 2007; 33(2): 181-7.
[http://dx.doi.org/10.1360/aas-007-0181]
[19]
Swamidason S. Rollover control algorithm. US201715637603 (2017).
[20]
Hanson R, Remkes R, Merrill M, Tanner K. Biometric-based punch-in/punch-out management. US201816170624 (2018).
[21]
Crafton J, Lawrence M, Rogoshchenkov N, Goss L. Spray application of agrochemicals. US201816102020 (2018).
[22]
Marsch S. A system for enhanced object tracking. WO2019001993 (2019).
[23]
Costas P. Method and system for autonomously operating an aircraft. US201815990447 (2018).
[24]
Wang ZH. Digital servo and control method thereof. WO2018CN87573 (2018).
[25]
Fakhry M, Buchet R, Magne D. Molecular mechanisms of mesenchymal stem cell differentiation towards osteoblasts. World J Stem Cells 2013; 5(4): 136-48.
[http://dx.doi.org/10.4252/wjsc.v5.i4.136 PMID: 24179602]
[26]
Staicu S. Inverse dynamics of the 3-PRR planar parallel robot. Robot Auton Syst 2009; 57(5): 556-63.
[http://dx.doi.org/10.1016/j.robot.2008.09.005]
[27]
Noshadi A, Mailah M, Zolfagharian A. Intelligent active force control of a 3-RRR parallel manipulator incorporating fuzzy resolved acceleration control. Appl Math Model 2012; 36(6): 2370-83.
[http://dx.doi.org/10.1016/j.apm.2011.08.033]
[28]
Guo C, Hao K, Ding Y. Neuroendocrine based cooperative intelligent control system for multi-objective integrated control of a parallel manipulator. Math Probl Eng 2012; 3(1): 327-402.
[29]
Sun ZJ, Xing RT, Zhao CS, Huang WQ. Fuzzy auto-tuning PID control of multiple joint robot driven by ultrasonic motors. Ultrasonics 2007; 46(4): 303-12.
[http://dx.doi.org/10.1016/j.ultras.2007.04.001 PMID: 17540429]
[30]
Xia SW, Duan SK, Wang LD, Hu XF. Design of memristive neural network PID controller. Chinese J Computers 2013; 36(12): 2577-86.
[31]
Tabuchi Y, Kura T. Methods and apparatus for autofocus. US201816191525 (2018).
[32]
Mangano D. Method and system for performing division/ multiplication operations in digital processors, corresponding device and computer program product. US201816166977 (2018).
[33]
Francis J. System and method for hybrid control of reduced pressures delivered to a tissue site. US201816158525 (2018).
[34]
Altonen GM, Burns BM. Systems and methods for autotuning PID control of injection molding machines. WO2019051009 (2019).
[35]
Su MT. Signal processing system and method thereof. US201815980757 (2018).
[36]
Sugai T. Slip control device. US201815960973 (2018).
[37]
Uno K. Numerical controller. US201815935575 (2018).
[38]
Ravari MA, Yaghoobi M. Optimum design of fractional order PID controller using chaotic firefly algorithms for a control CSTR system. Asian J Control 2019; 21(5): 2245-55.
[http://dx.doi.org/10.1002/asjc.1836]
[39]
Wai RJ, Huang YC, Yang ZW, Shih CY. Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking. IET Control Theory Appl 2010; 4(6): 1079-93.
[http://dx.doi.org/10.1049/iet-cta.2009.0166]
[40]
Georgios T, Chen ZT, Geng YH. Method for generating routing control action in software-defined network and related device. US201816226577 (2018).
[41]
Fan XY, Feng XY, Chai XP, Wang ZX, Zhang S. Video interpolation based on deep learning. AU20180101526 (2018).
[42]
Ahn H. Electronic apparatus, method for controlling the same, and non-transitory computer readable recording medium. WO2018KR02593 (2018).
[43]
Arai H. Information processing apparatus, information processing method, and program. US201616067352 (2016).
[44]
Yan ZB, Shu Z. Method and apparatus for model predictive control. WO2015CN86848 (2015).
[45]
Bai SA. Human intention detection system for motion assistance. WO2017DK50290 (2017).
[46]
Bondarev V, Nechaev Y. Method of preventing marine vessels collision. RU20170103234 (2017).
[47]
Valdes JJ, Barton AJ. Mufti-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting. Neural Netw 2007; 20(4): 498-508.
[http://dx.doi.org/10.1016/j.neunet.2007.04.009 PMID: 17532610]
[48]
Li T, Hou XY, Lin HY. Research on neural network PID control for the wind energy conversion systems based on Matlab/Simulink. Energy Sci Res 2013; 10: 1531-6.
[49]
Milovancevic M, Nikolic V, Pavlovic NT. Vibration prediction of pellet mills power transmission by artificial neural network. Assem Autom 2017; 37(4): 464-70.
[http://dx.doi.org/10.1108/AA-06-2016-060]
[50]
Morris J. Mobile electric power generation for hydraulic fracturing of subsurface geological formations. AU20190200899 (2019).
[51]
Randall B. Steerable hydraulic jetting nozzle, and guidance system for downhole boring device. AU20190200875 (2019).
[52]
Pomerleau D. Dual screen assembly for vibrating screening machine. AU20190200761 (2019).
[53]
McCormick P. Hydraulic system and method for a flight control system of an aircraft. US201816224794 (2018).
[54]
Staake M, Baeumler R, Brk N. Lubricant supply for an electric drive system and motor vehicle with such a lubricant supply. US201816221786 (2018).
[55]
Wood N. A steering assembly for docking a marine vessel having at least three propulsion units. AU20180278970 (2018).
[56]
Nelson D. Airflow for an agricultural harvesting combine. US201816214199 (2018).
[57]
Ma C. Neural-network-based containment control of nonlinear multi-agent systems under communication constraints. Assembly Autom 2016; 36(2): 179-85.
[http://dx.doi.org/10.1108/AA-11-2015-107]
[58]
Zhang PD, Gao Z. Optimal kinematic calibration of parallel manipulators error theory and cooperative coevolutionary network. IEEE Trans Ind Electron 2012; 59(8): 3221-31.
[http://dx.doi.org/10.1109/TIE.2011.2166229]
[59]
Nolan Z. Recommendation engine for a cognitive reservoir system. US201816157764 (2018).
[60]
Pedersen R. Multicast expert system information dissemination system and method. US201815885242 (2018).
[61]
Xia CL, Guo C, Shi TN. Mechanical decoupling control method of permanent magnet spherical motor based on neural network identifier. CN101369132 (2009).
[62]
Henry S. Using a neural network to optimize procession of user requests. US20190065948 (2019).
[63]
Mellempudi N, Das D. Scaling half-precision floating point tensors for training deep neural networks. US20180322382 (2018).
[64]
Wang J, Deng YM. Intelligent setting method for tea machine processing control parameters based on RBF neural network.CN108719516 (2018).
[65]
Wang XB, Ge S, Meng MR. DNN (Depth Neural Network)neural network self-adaptive control method based on tendon-driven dexterous hand. CN108555914 (2018).


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Article Details

VOLUME: 13
ISSUE: 4
Year: 2020
Published on: 12 October, 2020
Page: [366 - 377]
Pages: 12
DOI: 10.2174/2212797613999200420082115
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