Experimental research for semi-active suspension system with piecewise learning fuzzy neural network-based adaptive control

Abstract

The semi-active suspension system, which adjusts the damping characteristics in real-time based on road conditions, effectively enhances ride comfort and stability while reducing energy consumption. Although fuzzy control has been applied to vehicle suspension systems, the formulation of fuzzy rule functions involves a certain degree of subjectivity, limiting control accuracy. To address the limitations of traditional fuzzy control, this paper proposed a semi-active suspension control strategy based on a fuzzy neural network (FNN). The learning capability of neural network was used to optimize the fuzzy rule base of traditional fuzzy controllers. Moreover, the piecewise learning approach is employed to optimize the initial parameters of the neural network. In first stage, a clustering algorithm is employed to optimize the mean value c_jm and the standard deviation σ_jm within the neural network. Based on the obtained optimal initial values, the second stage applies a gradient descent method to further optimize the connection weight coefficient w_ij. A segmented and improved fuzzy neural network system has been built. Then the different weight ratios of ride comfort and stability are discussed to the control strategy. Furthermore, a semi-active suspension dynamic performance test rig was built to analyze the proposed control strategy. Compared to the traditional fuzzy control, the FNN control strategy further reduces the a_rms and φ_rms. Under B-class random road condition at vehicle speeds of 10 km/h, 20 km/h, and 30 km/h, the a_rms decreased by 9.19%, 13.60%, and 15.44%, respectively. Meanwhile, φ_rms decreased by 10.49%, 8.96%, and 7.82%, when the vehicle speeds increased. Under 10% eccentric load conditions, for A, B, and C-class random roads, the a_rms decreased by 16.78%, 13.18%, and 9.59%, while φ_rms decreased by 11.63%, 15.03%, and 17.09%, respectively. These experimental results demonstrate that the designed FNN control strategy can effectively improve the ride comfort and stability of vehicle.

Keywords

semi-active suspension fuzzy neural network four-degree-of-freedom vehicle dynamics model ride comfort stability

Get full access to this article

View all access options for this article.

References

Zhao

Liu

, et al. (2023) Self-constructed deep fuzzy neural network for traffic flow prediction. Electronics 12(8): 1885.

Ata

Salem

(2017) Semi-active control of tracked vehicle suspension incorporating magnetorheological dampers. Vehicle System Dynamics 55(5): 626–647.

Cao

Xie

, et al. (2022) Bioinspired central pattern generator and T-S fuzzy neural network-based control of a robotic manta for depth and heading tracking. Journal of Marine Science and Engineering 10(6): 1–13.

Chen

Liang

, et al. (2023) Semi-active control of a new quasi-zero stiffness air suspension for commercial vehicles based on H₂H_∞ state feedback. Journal of Vibration and Control 29(7): 1910–1926.

Ding

Zhu

Liu

, et al. (2024) Adaptive memory event-triggered output feedback finite-time lane-keeping control for autonomous heavy truck with roll prevention. IEEE Transactions on Fuzzy Systems 32(12): 6607–6621.

Ergin

Yatak

(2023) Optimal control method of semi-active suspension system and processor-in-the-loop verification. Applied Sciences 13(20): 1–23.

Fang

, et al. (2024) Self-learning takagi–sugeno fuzzy control with application to semicar active suspension model. IEEE Transactions on Fuzzy Systems 32(1): 64–74.

Feng

Zhen

Tian

, et al. (2023) An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data. International Journal of Hydromechatronics 6(2): 108–132.

Gao

Han

, et al. (2025) Multi-agent reinforcement learning for vibration control of regenerative active suspension. Engineering Applications of Artificial Intelligence 153: 1–13.

10.

Wang

, et al. (2025) Research on vertical vibration characteristics of rolling mill based on magnetorheological fluid damper absorber. Mechanical Systems and Signal Processing 224: 1–20.

11.

Feng

, et al. (2024) A novel DNFS control strategy for vehicle semi-active suspension system. Measurement Science and Technology 35(12): 1–14.

12.

Lee

Jin

Lee

(2023) Deep reinforcement learning of semi-active suspension controller for vehicle ride comfort. IEEE Transactions on Vehicular Technology 72(1): 327–339.

13.

Shi

(2024) Adaptive endocrine PID control for active suspension based on reinforcement learning. Proceedings of the Institution of Mechanical Engineers - Part D: Journal of Automobile Engineering 239(4): 354.

14.

Wang

Pei

, et al. (2023) An improved energy management strategy of diesel-electric hybrid propulsion system based on FNN-DP strategy. Electronics 12(3): 1–18.

15.

Ruan

, et al. (2024) Design and performance evaluation of a novel fractional order PID control strategy for vehicle semi-active suspension. Advances in Mechanical Engineering 16(4): 1–11.

16.

Wang

, et al. (2021) Optimized fuzzy skyhook control for semi-active vehicle suspension with new inverse model of magnetorheological fluid damper. Energies 14(6): 1–21.

17.

Mamdani

Assilian

(1999) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Human-Computer Studies 7(1): 1–15.

18.

Pang

Liu

(2016) Enhanced variable-universe fuzzy control for vehicle semi-active suspension systems. Journal of Intelligent and Fuzzy Systems 31(6): 2999–3006.

19.

Pazooki

Khajepour

Goodarzi

, et al. (2018) A novel approach for the design and analysis of nonlinear dampers for automotive suspensions. Journal of Vibration and Control 25(14): 3132–3147.

20.

Phu

Huy

Mien

, et al. (2018) A new composite adaptive controller featuring the neural network and prescribed sliding surface with application to vibration control. Mechanical Systems and Signal Processing 107: 409–428.

21.

Sun

Deng

, et al. (2015) A compact variable stiffness and damping shock absorber for vehicle suspension. IEEE 20(5): 1–9.

22.

Sun

, et al. (2024) Enhancing position control in pneumatic systems using ANFIS and high-speed on-off valves with compound PWM. International Journal of Hydromechatronics 7(4): 347–367.

23.

Trikande

Karve

Raj

, et al. (2018) Semi-active vibration control of an 8x8 armored wheeled platform. Journal of Vibration and Control 24(2): 283–302.

24.

Wang

Zeng

, et al. (2020) Study on semi-active suspension applied on car body underneath suspended system of high-speed railway vehicle. Journal of Vibration and Control 26(9): 671–679.

25.

Xiong

Pan

Liu

, et al. (2024) An adaptive backstepping sliding mode control strategy for the magnetorheological semi-active suspension. Proceedings of the Institution of Mechanical Engineers - Part D: Journal of Automobile Engineering 239(9): 454.

26.

Yang

Liu

Zhang

, et al. (2021) An adaptive sliding mode fault-tolerant control for semi-active suspensions with magnetorheological dampers based on T-S fuzzy vehicle models. Journal of Vibration and Control 29: 251–264.

27.

Yoon

Kim

Choi

(2021) Response time of magnetorheological dampers to current inputs in a semi-active suspension system: modeling, control and sensitivity analysis. Mechanical Systems and Signal Processing 146(Jana): 106999.

28.

Yuan

Zhou

Chen

, et al. (2023) Designing a Switched Takagi-sugeno fuzzy controller for CDC semi-active suspensions with current input constraint. Mechanical Systems and Signal Processing 199: 1–21.

29.

Zhang

Yuan

, et al. (2024) Design of hybrid controller for broadband vibration isolation with active negative stiffness and ground-hook. Mechanical Systems and Signal Processing 222: 1–15.

30.

Zhu

(2022) Vertical negative effect suppression of In-Wheel electric vehicle based on hybrid variable-universe fuzzy control. Journal of Circuits, Systems, and Computers 13(15): 1–13.