This paper deals with multi-objective active suspension systems that employ electromechanical actuators having significant unknown lags. In the presence of actuator lags, the sprung mass system becomes an uncertain mismatched system for which the conventional sliding mode control (SMC) loses its celebrated property of invariance. Apart from compensating for the actuator lags, the suspension system is also required to fulfil a multi-objective role in which ride comfort and ride handling are both catered to. For this purpose, this paper proposes a new functionally graded sliding mode control (FGSMC). The effectiveness of FGSMC in meeting the conflicting objectives of the ride comfort and ride handling is analysed and validated by simulation for a bump and dip profile and an ISO class D profile and by experimentation on a popular laboratory setup.
AhmadIGeXHanQL (2024) Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles. Journal of Automation and Intelligence3(1): 2–18.
2.
CaoZZhaoWHouX, et al. (2020) Multi-objective robust control for vehicle active suspension systems via parameterized controller. IEEE Access8: 7455–7465.
3.
ChenPCHuangAC (2005) Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings. Journal of Sound and Vibration282: 1119–1135.
4.
CorlessMLeitmanG (1981) Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems. IEEE Transactions on Automatic Control26(5): 139–1144.
5.
DeshpandeVSShendgePDPhadkeSB (2016) Dual objective active suspension system based on a novel nonlinear disturbance compensator. Vehicle System Dynamics54(9): 1269–1290.
6.
DeshpandeVSShendgePDPhadkeSB (2017) Nonlinear control for dual objective active suspension systems. IEEE Transactions on Intelligent Transportation Systems18(3): 656–665.
7.
GongMBiJYangH, et al. (2024) Balance control for dynamic displacement of active suspension and ride comfort. Journal of Vibration and Control10775463241273780.
8.
HacA (1992) Optimal linear preview control of active vehicle suspension. Vehicle System Dynamics21(1): 167–195.
9.
HuangYNaJWuX, et al. (2018) Approximation-free control for vehicle active suspensions with hydraulic actuator. IEEE Transactions on Industrial Electronics65(9): 7258–7267.
10.
ISO (1997) Mechanical vibration and shock: evaluation of human exposure to whole-body vibration part 1. In: General Requirements. Geneva: ISO, 2631.
11.
ISO (2016) Mechanical Vibration — Road Surface Profiles — Reporting of Measured Data. Geneva: ISO.
12.
KochGKloiberT (2014) Driving state adaptive control of an active vehicle suspension system. IEEE Transactions on Control Systems Technology22: 44–57.
13.
KonoikoAKadhemASaifulI, et al. (2019a) Deep learning framework for controlling an active suspension system. Journal of Vibration and Control25(17): 2316–2329.
14.
LiHLiuHGaoH, et al. (2012) Reliable fuzzy control for active suspension systems with actuator delay and fault. IEEE Transactions on Fuzzy Systems20(2): 342–357.
15.
LiuYJZengQLiuL, et al. (2020) An adaptive neural network controller for active suspension systems with hydraulic actuator. IEEE Transactions on Systems, Man, and Cybernetics: Systems50(12): 5351–5360.
16.
LiuJDingFWangJ, et al. (2024a) Dynamic uncertainty propagation analysis framework for nonlinear control problem based on manifold learning and optimal polynomial method and its application on active suspension system. Journal of Vibration and Control530: 10775463241276242.
17.
LiuZSiYSunW (2024b) Ride comfort oriented integrated design of preview active suspension control and longitudinal velocity planning. Mechanical Systems and Signal Processing208: 110992.
18.
MustafaGIWangH (2024b) A new adaptive fuzzy logic control for nonlinear car active suspension systems based on the time-delay. Journal of Vibration and Control10775463241281396.
19.
PapadimitrakisMAlexandridisA (2022) Active vehicle suspension control using road preview model predictive control and radial basis function networks. Applied Soft Computing120: 108646.
20.
PengYQinYTangX, et al. (2022) Survey on image and point-cloud fusion-based object detection in autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems23(12): 22772–22789.
21.
Quanser (2013) Active Suspension System: User Manual. Markham: Quanser Corporation.
22.
QueNDengWZhouN, et al. (2024) Disturbance observer-based prescribed-time tracking control of nonlinear systems with non-vanishing uncertainties. IEEE Trans. Circuits Syst. II71(6): 3131–3135.
23.
RenYXieXSunJ, et al. (2024) Enhanced fuzzy control of active vehicle suspension systems based on a novel hppd-type non-pdc control scheme. IEEE/ASME Trans. Mechatronics99: 1–10.
24.
TheunissenJSorniottiAGruberP, et al. (2020) Regionless explicit model predictive control of active suspension systems with preview. IEEE Transactions on Industrial Electronics67(6): 4877–4888.
25.
ThiyagarajanJSathishkumarPMuthuramalingamT (2023) Experimental investigation of active suspension force control of electric motor driven actuator with estimation and tracking control strategy. Materials Today: Proceedings90: 279–286.
26.
TsengHEHrovatD (2015) State of the art survey: active and semi-active suspension control. Vehicle System Dynamics53(7): 1034–1062.
27.
WangHChangLTianY (2021) Extended state observer–based backstepping fast terminal sliding mode control for active suspension vibration. Journal of Vibration and Control27(19–20): 2303–2318.
28.
WuYSuD (2024) Simulation analysis of quarter car active suspension control based on QBP-PID. Proceedings of the Institution of Mechanical Engineers - Part D: Journal of Automobile Engineering09544070241265152.
29.
YuMEvangelouSADiniD (2024) Advances in active suspension systems for road vehicles. Engineering33: 160–177.
30.
ZhouYZhouJWangS, et al. (2024) Practical fixed-time attitude consensus tracking for multi-quadrotor systems: a composite learning backstepping approach. IEEE Trans. Circuits Syst. II71(6): 3066–3070.
31.
ZuoLNayfehSA (2003) Low order continuous-time filters for approximation of the ISO 2631-1 human vibration sensitivity weightings. Journal of Sound and Vibration265: 459–465.
