This paper presents a novel idea processing the complex non-linear dynamics of a magneto-rheological (MR) damper and the external road disturbance based on the linear extended state observer (LESO) technology, and further verifies its reasonability by application of linear active disturbance rejection control (LADRC) in the quarter-car non-linear semi-active suspension system. In order to optimize the body acceleration and dynamic tyre load to improve the ride comfort and road-handling ability, a modified active disturbance rejection control, the double linear active disturbance rejection control (DLADRC), is further proposed based on the idea of the hybrid skyhook–groundhook control strategy. LESO is used to estimate the total disturbance including the external road disturbance and the internal non-linear dynamic of the MR damper. For effectiveness validation of the proposed control scheme, comparison results with the existing linear quadratic regulation (LQR) control, hybrid skyhook–groundhook control and adaptive control strategies are presented for the same quarter-car semi-active suspension. It is shown from the simulation comparisons among these several control strategies that the semi-active suspension system with DLADRC has a better control performance on the ride comfort and road-handling ability corresponding to the body acceleration and dynamic tyre load.
ChengMXJiaoXH (2016) Observer-based adaptive L2 disturbance attenuation control of semi-active suspension with MR damper. Asian Journal of Control Published online in wileyonlinelibrary.com, DOI: 10.1002/asjc.1347.
2.
DongXYuMLiaoCet al. (2010) Comparative research on semi-active control strategies for magneto-rheological suspension. Nonlinear Dynamics59(3): 433–453.
3.
DuHSzeKYLamJ (2005) Semi-active H∞ control of vehicle suspension with magneto-rheological dampers. Journal of Sound and Vibration283(3): 981–996.
4.
DuXChenHLiuZet al. (2015) Semi-active control of space manipulator soft contacting based on magneto-rheological rotational damper. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science DOI: 0954406215594827.
5.
El MajdoubKGhaniDGiriFet al. (2015) Adaptive semi-active suspension of quarter-vehicle with magnetorheological damper. Journal of Dynamic Systems, Measurement, and Control137(2): 1–10.
6.
EltantawieMA (2012) Decentralized neuro-fuzzy control for half car with semi-active suspension system. International Journal of Automotive Technology13(3): 423–431.
7.
FerdausMMRashidMMHasanMHet al. (2014) Temperature effect analysis on magneto-rheological damper’s performance. Journal of Automation & Control Engineering2(4): 392–396.
8.
FerganiSSenameODugardL (2014) A LPV/Hinf fault tolerant control of vehicle roll dynamics under semi-active damper malfunction. In:Proceedings of the 2014 American Control Conference, Portland, OR, pp. 4482–4487.
9.
GaoHLamJWangC (2006) Multi-objective control of vehicle active suspension systems via load-dependent controllers. Journal of Sound & Vibration290(3): 654–675.
10.
GaoZ (2006) Active disturbance rejection control: a paradigm shift in feedback control system design. In:Proceedings of the American Control Conference, Minneapolis, MN, 15 June, pp. 2399–2405.
11.
Gopala RaoLVVNarayananS (2009) Sky-hook control of nonlinear quarter car model traversing rough road matching performance of LQR control. Journal of Sound and Vibration323(3): 515–529.
12.
GuoDLHuHYYiJQ (2004) Neural network control for a semi-active vehicle suspension with a magneto-rheological damper. Journal of Vibration and Control10(3): 461–471.
13.
HasbullahFFarisWFDarsivanFJet al. (2015) Ride comfort performance of a vehicle using active suspension system with active disturbance rejection control. International Journal of Vehicle Noise & Vibration11(1): 78–101.
14.
KhiaviAMMirzaeiMHajimohammadiS (2013) A new optimal control law for semi-active suspension system considering nonlinear magneto-rheological damper model. Journal of Vibration & Control20(14): 2221–2233.
15.
KochGKloiberT (2014) Driving state adaptive control of an active vehicle suspension system. IEEE Transactions on Control Systems Technology22(1): 44–57.
16.
LiLRanXWuKet al. (2015) A novel fuzzy logic correction algorithm for traction control systems with uneven low-adhesion road conditions. Vehicle System Dynamics53(6): 1–23.
17.
LianRJ (2013) Enhanced adaptive self-organizing fuzzy sliding-mode controller for active suspension systems. IEEE Transactions on Industrial Electronics60(3): 958–968.
18.
MaMMChenH (2011) Disturbance attenuation control of active suspension with non-linear actuator dynamics. IET Control Theory & Applications5(1): 112–122.
19.
MullaAJalwadiSUnauneD (2014) Performance analysis of skyhook, groundhook and hybrid control strategies on semi-active suspension system. International Journal of Current Engineering and Technology3: 265–269.
20.
NerbinMMGharamalekiRMMirzaeiM (2015) Novel optimal control of semi-active suspension considering a hysteresis model for MR damper. Transactions of the Institute of Measurement and Control. DOI: 0142331215618446.
21.
NguyenLHHongKSParkS (2010) Road-frequency adaptive control for semi-active suspension systems. International Journal of Control Automation & Systems8(5): 1029–1038.
22.
NilkhamhangISanoA (2008) Stability-assured robust adaptive control of semi-active suspension systems. In:Proceedings of the American Control Conference, Seattle, WA, 11–13 June, pp. 2358–2364.
23.
NugrohoPWLiWDuHet al. (2014) An adaptive neuro fuzzy hybrid control strategy for a semi-active suspension with magneto rheological damper. Advances in Mechanical Engineering6(2): 487312–487312.
24.
OnatCKucukdemiralIBSivriogluSet al. (2009) LPV gain-scheduling controller design for a non-linear quarter-vehicle active suspension system. Transactions of the Institute of Measurement & Control31(1): 71–95.
25.
PanHSunWGaoHet al. (2015) Nonlinear tracking control based on extended state observer for vehicle active suspensions with performance constraints. Mechatronics30: 363–370.
26.
QiuJGaoHDingSX (2016) Recent advances on fuzzy-model-based nonlinear networked control systems: a survey. IEEE Transactions on Industrial Electronics63(2): 1207–1217.
27.
SpencerBFDykeSJSainMKet al. (1997) Phenomenological model for magneto-rheological dampers. Journal of Engineering Mechanics123(3): 230–238.
28.
WangTQiuJYinSet al. (2016) Performance-based adaptive fuzzy tracking control for networked industrial processes. IEEE Transactions on Cybernetics46(8): 1760–1770.
29.
WenYK (1976) Method for random vibration of hysteretic systems. Journal of the Engineering Mechanics Division102(2): 249–263.
30.
XiaoPWangQNiuLet al. (2015) Research on suspension system with embedded-permanent-magnet magneto-rheological damper based on V-model. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science DOI: 0954406215583691.
31.
YıldızASSivrioğluSZergeroğluEet al. (2015) Nonlinear adaptive control of semi-active MR damper suspension with uncertainties in model parameters. Nonlinear Dynamics79(4): 2753–2766.
32.
ZhangCWOuJPZhangJQ (2006) Parameter optimization and analysis of a vehicle suspension system controlled by magnetorheological fluid dampers. Structural Control and Health Monitoring13(5): 885–896.
33.
ZhangZKarimiHRHuangHet al. (2014) Vibration control of a semi-active vehicle suspension system based on extended state observer techniques. Journal of Applied Mathematics2014(3): 1–10.
