BMe Research Grant
The semi-active suspension system plays a key role in the safety and comfortable automated driving, while in autonomous vehicles the adaptation of this system to different road conditions and vehicle velocities is significant. There are several control approaches for semi-active suspension control. The skyhook control strategy is commonly used in semi-active suspension control [1,2]. Skyhook strategy is based on designing the active suspension control so that the chassis is linked to the sky to reduce the vertical acceleration of the chassis and the axle independently. It is easy to implement with few status information, and effectively increases the driving comfort of the vehicle, but disrupts the dynamic tire load. Both model predictive control (MPC)  and hybrid model predictive control (Hybrid MPC) methods are also commonly used but they still lack robustness properties, and their application is not easy. Unlike previous methods, control approach  guarantees road holding, vehicle stability, and driving comfort performances, however, dynamic control reconfiguration is not possible due to a fixed weighting of the performances. Reconfiguring the control is key in controlling semi-active suspension systems to configure the controller according to different road conditions. For instance, the damper needs to act differently for each road irregularities, such as bumps, potholes, roughness, etc. Thus, the control of semi-active suspension in this study is based on the Linear Parameter-Varying (LPV) framework, due to its availability for dynamic configuration by modifying the scheduling variable.
According to a detailed literature review, publications related to similar research topics do not simultaneously address the integration of velocity design, oncoming road conditions, and the trade-off between vehicle safety, and driving comfort. This research flaw necessitates the design of a control architecture consisting of a velocity controller, a look-ahead controller, and a semi-active suspension controller. This control architecture ensures and enhances both automated driving comfort and vehicle safety by considering different velocities and road conditions.
The aim of this research is to integrate velocity and semi-active suspension control by considering oncoming road conditions to enhance automated driving comfort and ensure vehicle safety. To achieve this goal, a reconfigurable adaptive semi-active suspension controller has been designed and an energy-efficient velocity and look-ahead controller has been integrated. The road condition is known and stored in a cloud system. This adaptive semi-active suspension control is based on the Linear Parameter Varying (LPV) framework, where a trade-off between driving comfort and vehicle safety is possible with a dedicated scheduling variable. The targeted research was presented in the TruckSim simulation environment, which delivers the most accurate, detailed, and efficient methods for simulating vehicle performance.
There are several simulations that demonstrate the effectiveness of the proposed system. Only one of these simulations result is explained here[S3]. The simulation was run in a TruckSim environment with a real geographical highway route. The effectiveness of the introduced method is demonstrated with two different simulations, these are the utility truck with adaptive semi-active suspension control and conventional semi-active suspension.
The simulation route is 3 km long with the following irregularities and selected scheduling variables for the adaptive suspension scenario: first several bumps(5cm) at 200 m with selected scheduling variable as 0.52, 10 cm bump at 950 m with ⍴=0.6, for second 10cm bump at 2000 m with ⍴=0.54, for second several bumps at 2200m with ⍴=0.47 and the final distortion sine sweep at 2650m with ⍴=0.5 and ⍴=1 for the flat road. In conventional suspension scenario, which is non-adaptive, scheduling variable is selected as 1.
List of corresponding own publications
[S1]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2020a) “Integrated multi-criteria velocity and semi-active suspension control based on look-ahead road information.”, In 28th Mediterranean Conference on Control and Automation, Saint-Raphaël, France, pp. 25–30, https://doi.org/10.1109/MED48518.2020.9182953
[S2]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2020b) “Adaptive semi-active suspension control considering look-ahead road Information and irregularities.”, In 17th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, Budapest, Hungary.
[S3]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2021a) “Adaptive Semi-Active Suspension and Cruise Control through LPV Technique”, Applied Sciences, 11(1), 290, https://doi.org/10.3390/app11010290
[S4]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2021b) “Road adaptive semi-active suspension and cruise control through LPV technique.”, In European Control Conference 2021, Rotterdam, The Netherlands.
[S5]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2021c) “Fault-tolerant semi-active suspension control for degradation in damper performance.”, In 29th Mediterranean Conference on Control and Automation 2021, Bari, Puglia, Italy.
[S6]Basargan, H., Mihály, A., Gáspár, P., Sename, O. (2021d) “Road quality information based adaptive semi-active suspension control.”, Journal of Periodica Polytechnica Transportation Engineering (accepted).
[S7]Basargan, H., Mihály, A., Kisari, Á., Gáspár, P., Sename, O. (2021e) “Vehicle semi-active suspension control with cloud-based road information.”, Journal of Periodica Polytechnica Transportation Engineering (accepted).
List of references
Shimoya, N., Katsuyama, E. (2019) “A Study of Vehicle Ride Comfort using Triple Skyhook Control for Semi-active Suspension System”, Transactions of Society of Automotive Engineers of Japan, 50(6), pp. 1631–1636, https://doi.org/10.11351/jsaeronbun.50.1631
Liu, C., Chen, L., Yang, X., Zhang, X., Yang, Y. (2019) “General theory of skyhook control and its application to semi-active suspension control strategy design.”, IEEE Access, 7, pp. 101552–101560, https://doi.org/10.1109/ACCESS.2019.2930567
Rathai, K. M. M., Sename, O., Alamir, M. (2019) “Reachability based Model Predictive Control for Semi-active Suspension System.” In 2019 Fifth Indian Control Conference (ICC), New Delhi, India, pp. 68–73, https://doi.org/10.1109/INDIANCC.2019.8715601
Yu, S., Zhang, J., Xu, F., Chen, H. (2019) “ Control of Semi-Active MR Damper Suspensions.” In 12th Asian Control Conference, Orléans, France, pp. 337–342, https://doi.org/10.1016/j.ifacol.2019.09.042
ISO(1997) "ISO 2631-1, Mechanical vibration and shock-Evaluation of human exposure to whole-body vibration", ISO.