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Frontiers of Mechanical Engineering

Front. Mech. Eng.    2018, Vol. 13 Issue (2) : 179-210
Model-based nonlinear control of hydraulic servo systems: Challenges, developments and perspectives
Jianyong YAO()
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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Hydraulic servo system plays a significant role in industries, and usually acts as a core point in control and power transmission. Although linear theory-based control methods have been well established, advanced controller design methods for hydraulic servo system to achieve high performance is still an unending pursuit along with the development of modern industry. Essential nonlinearity is a unique feature and makes model-based nonlinear control more attractive, due to benefit from prior knowledge of the servo valve controlled hydraulic system. In this paper, a discussion for challenges in model-based nonlinear control, latest developments and brief perspectives of hydraulic servo systems are presented: Modelling uncertainty in hydraulic system is a major challenge, which includes parametric uncertainty and time-varying disturbance; some specific requirements also arise ad hoc difficulties such as nonlinear friction during low velocity tracking, severe disturbance, periodic disturbance, etc.; to handle various challenges, nonlinear solutions including parameter adaptation, nonlinear robust control, state and disturbance observation, backstepping design and so on, are proposed and integrated, theoretical analysis and lots of applications reveal their powerful capability to solve pertinent problems; and at the end, some perspectives and associated research topics (measurement noise, constraints, inner valve dynamics, input nonlinearity, etc.) in nonlinear hydraulic servo control are briefly explored and discussed.

Keywords hydraulic servo system      adaptive control      robust control      nonlinear friction      disturbance compensation      repetitive control      noise alleviation      constraint control     
Corresponding Authors: Jianyong YAO   
Just Accepted Date: 13 September 2017   Online First Date: 06 November 2017    Issue Date: 16 March 2018
 Cite this article:   
Jianyong YAO. Model-based nonlinear control of hydraulic servo systems: Challenges, developments and perspectives[J]. Front. Mech. Eng., 2018, 13(2): 179-210.
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Fig.1  The schematic diagram of the hydraulic servo system. (a) Servo-valve controlled double rod hydraulic cylinder; (b) servo-valve controlled bidirectional hydraulic motor
Fig.2  Tracking performance of VFPI and FBL for 1°–20 Hz motion. (a) Trajectory tracking; (b) tracking errors of FBL and VFPI
Frequency/Hz Max velocity/(°·s−1) Controller Max error/(° ) Phase lag
5 31.4 VFPI 0.100 0.5°
FBL 0.030 Invisible
10 62.8 VFPI
15 94.2 VFPI
20 125.6 VFPI
Tab.1  Performances summary with 1° amplitude testing
Fig.3  Tracking performance under PID controller
Fig.4  Tracking performance under ARC controller
Fig.5  Tracking performance of ARISE for normal motion
Fig.6  Tracking errors of ARC and PID for normal motion
Fig.7  Parameter estimation of ARISE for normal motion
Fig.8  Tracking performance of ARISE for slow motion
Fig.9  Tracking errors of ARC and PID controllers for slow motion
Fig.10  Tracking performance of ARISE for fast motion
Fig.11  Tracking errors of ARC and PID controllers for fast motion
Fig.12  Tracking errors of APC, ARC, FLC and PID for normal motion
Indices Me m s
PID 0.0896 0.0532 0.0274
FLC 0.0637 0.0198 0.0125
ARC 0.0136 0.0035 0.0026
APC 0.0089 0.0016 0.0012
Tab.2  Performance indices for normal tracking case
Fig.13  Parameter estimation of APC for normal motion
Indices Me m s
ARC 0.2899 0.0935 0.0623
APC 0.1084 0.0517 0.0244
Tab.3  Performance indices for fast tracking case
Fig.14  Tracking errors of ARC and APC controllers for fast motion
Indices Me m s
PIVF 0.0903 0.0531 0.0274
FLC 0.0663 0.0200 0.0132
AC 0.0123 0.0030 0.0024
ALuGre 0.0081 0.0019 0.0015
Tab.4  Performance indices in normal tracking case
Fig.15  Tracking performance of ALuGre for normal motion
Fig.16  Tracking errors of the other three controllers for normal motion
Indices Me m s
PIVF 0.0213 0.0044 0.0047
FLC 0.0414 0.0050 0.0092
AC 0.0125 0.0013 0.0016
ALuGre 0.0041 0.0008 0.0006
Tab.5  Performance indices in slow tracking case
Fig.17  Tracking performance of ALuGre for slow motion
Fig.18  Tracking errors of the other three controllers for slow motion
Fig.19  Tracking errors of OFRC and PI controllers in normal case. (a) Tracking errors during the whole period in normal case; (b) tracking errors during the last two cycles in normal case
Indices Me m s
PI 0.0501 0.0097 0.0072
OFRC 0.0383 0.0517 0.0065
Tab.6  Performance indices in normal case
Fig.20  Tracking errors of OFRC and PI controllers in slow tracking case. (a) Tracking errors during the whole period in slow tracking case; (b) comparison of tracking errors
Indices Me m s
PI 0.0321 0.0019 0.0033
OFRC 0.0152 0.0008 0.0012
Tab.7  Performance indices in slow tracking case
Fig.21  Tracking errors of OFRC in fast tracking case with disturbance
Fig.22  Tracking errors with z1(0)= ?1
Fig.23  Velocity output with z1(0)= ?1
Fig.24  Acceleration output with z1(0)= ?1
Fig.25  Dead-zone effects and its smooth inverse
Fig.26  Multi-valued effects of an example of hysteresis
1 Merritt H E. Hydraulic Control Systems. New York: Wiley, 1967
2 Yao B, Bu F, Reedy J, et al.Adaptive robust motion control of single-rod hydraulic actuators: Theory and experiments. IEEE/ASME Transactions on Mechatronics, 2000, 5(1): 79–91
3 Krstic M, Kanellakopoulos I, Kokotovic P V. Nonlinear and Adaptive Control Design. New York: Wiley, 1995
4 Armstrong-Hélouvry B, Dupont P, Canudas de Wit C. A survey of models, analysis tools and compensation methods for the control of machines with friction. Automatica, 1994, 30(7): 1083–1138
5 Canudas de Wit C, Olsson H, Astrom K J, et al.A new model for control of systems with friction. IEEE Transactions on Automatic Control, 1995, 40(3): 419–425
6 Swevers J, Al-Bender F, Ganseman C G, et al.An integrated friction model structure with improved presliding behavior for accurate friction compensation. IEEE Transactions on Automatic Control, 2000, 45(4): 675–686
7 Yao J, Jiao Z, Ma D, et al.High-accuracy tracking control of hydraulic rotary actuators with modelling uncertainties. IEEE/ASME Transactions on Mechatronics, 2014, 19(2): 633–641
8 Yao J, Deng W, Jiao Z.RISE-based adaptive control of hydraulic systems with asymptotic tracking. IEEE Transactions on Automation Science and Engineering, 2017, 14(3): 1524–1531
9 Lischinsky P, Canudas de Wit C, Morel G. Friction compensation for an industrial hydraulic robot. IEEE Control Systems Magazine, 1999, 19 (1): 25–32
10 Swaroop D, Hedrick J K, Yip P P, et al.Dynamic surface control for a class of nonlinear systems. IEEE Transactions on Automatic Control, 2000, 45(10): 1893–1899
11 Yip P P, Hedrick J K. Adaptive dynamic surface control: A simplified algorithm for adaptive backstepping control of nonlinear systems. International Journal of Control, 1998, 71(5): 959–979
12 Farrell J A, Polycarpou M, Sharma M, et al.Command filtered backstepping. IEEE Transactions on Automatic Control, 2009, 54(6): 1391–1395
13 Dong W, Farrell J A, Polycarpou M M, et al.Command filtered adaptive backstepping. IEEE Transactions on Control Systems Technology, 2012, 20(3): 566–580
14 Guan C, Pan S. Adaptive sliding mode control of electro-hydraulic system with nonlinear unknown parameters. Control Engineering Practice, 2008, 16(11): 1275–1284
15 Guan C, Pan S. Nonlinear adaptive robust control of single-rod electro-hydraulic actuator with unknown nonlinear parameters. IEEE Transactions on Control Systems Technology, 2008, 16(3): 434–445
16 Mintsa H A, Venugopal R, Kenne J P, et al.Feedback linearization-based position control of an electrohydraulic servo system with supply pressure uncertainty. IEEE Transactions on Control Systems Technology, 2012, 20(4): 1092–1099
17 Yao J, Yang G, Jiao Z. High dynamic feedback linearization control of hydraulic actuators with backstepping. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2015, 229: 728–737
18 Yao J, Jiao Z, Yao B, et al.Nonlinear adaptive robust force control of hydraulic load simulator. Chinese Journal of Aeronautics, 2012, 25(5): 766–775
19 Yang G, Yao J, Le G, et al.Adaptive integral robust control of hydraulic systems with asymptotic tracking. Mechatronics, 2016, 40: 78–86
20 Deng W, Yao J. Adaptive integral robust control and application to electromechanical servo systems. ISA Transactions, 2017, 67: 256–265
21 Yang G, Yao J, Le G, et al.Asymptotic output tracking control of electro-hydraulic systems with unmatched disturbances. IET Control Theory & Applications, 2016, 10(18): 2543–2551
22 Yao B, Xu L. On the design of adaptive robust repetitive controllers. In: Proceedings of 2001 ASME International Mechanical Engineering Congress and Exposition. New York: ASME, 2001, 1–9
23 Yao J, Jiao Z, Ma D. A practical nonlinear adaptive control of hydraulic servomechanisms with periodic-like disturbances. IEEE/ASME Transactions on Mechatronics, 2015, 20(6): 2752–2760
24 Yao J, Deng W, Jiao Z. Adaptive control of hydraulic actuators with LuGre model based friction compensation. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6469–6477
25 Xu L, Yao B. Adaptive robust control of mechanical systems with nonlinear dynamic friction compensation. International Journal of Control, 2008, 81(2): 167–176
26 Tan Y, Kanellakopoulos I. Adaptive nonlinear friction compensation with parametric uncertainties. In: Proceedings of the 1999 American Control Conference. San Diego: IEEE, 1999, 2511–2515
27 Zheng Q, Gao L, Gao Z. On stability analysis of active disturbance rejection control for nonlinear time-varying plants with unknown dynamics. In: Proceedings of 2007 46th IEEE Conference on Decision and Control. New Orleans: IEEE, 2007, 3501–3506
28 Yao J, Jiao Z, Ma D. Extended-state-observer-based output feedback nonlinear robust control of hydraulic systems with backstepping. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6285–6293
29 Tee K P, Ge S S, Tay E H. Barrier Lyapunov function for the control of output-constrained nonlinear systems. Automatica, 2009, 45(4): 918–927
30 Ngo K B, Mahony R, Jiang Z P. Integrator backstepping using barrier functions for systems with multiple state constraints. In: Proceedings of 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. Seville: IEEE, 2005, 8306–8312
31 Deng W, Yao J, Jiao Z. Adaptive backstepping motion control of hydraulic actuators with velocity and acceleration constraints. In: Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). Yantai: IEEE, 2014, 219–224
32 Sadegh N, Horowitz R. Stability and robustness analysis of a class of adaptive controllers for robot manipulators. International Journal of Robotics Research, 1990, 9(3): 74–92
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