Adaptive Fractional-Order Damping via Extremum Seeking Control for Intelligent Vehicle Suspension Systems
- 1 Independent Researcher, Nanjing, China
Abstract
Conventional vehicle suspension systems, often relying on integer-order models with fixed damping coefficients, struggle to deliver optimal performance across diverse and dynamic road conditions. This paper introduces a novel intelligent adaptive suspension framework that leverages fractional-order calculus and real-time optimization. The core of the system is a damping model employing a Caputo fractional derivative of order α ∈ ( 1 , 2 ) , where α itself is dynamically tuned. This adaptation is driven by an Extremum Seeking Control (ESC) algorithm, which continuously adjusts α to minimize a predefined cost function reflecting ride comfort and road holding, based on fused sensor data (e.g., from IMUs and wheel encoders processed via a Kalman Filter). This model-free online optimization allows the suspension to adapt its fundamental damping characteristics to changing terrains without requiring explicit road classification models. Simulation results for a quarter-car model demonstrate the ESC’s ability to converge towards an optimal α , enhancing the suspension’s adaptability and performance across varying operating scenarios, thereby indicating a promising path for next-generation terrain-aware vehicle dynamics control. This model-free, online optimization allows the suspension to adapt its fundamental damping characteristics to changing terrains without requiring explicit road classification models. Real-time feasibility is achieved through computationally efficient numerical approximations of the fractional derivative and the inherent filtering within the ESC loop, making the framework suitable for implementation on modern automotive controllers. Simulation results for a quarter-car model demonstrate the ESC’s ability to converge towards an optimal α , enhancing the suspension’s adaptability and performance across varying operating scenarios, thereby indicating a promising path for next-generation terrain-aware vehicle dynamics control.
- Gillespie, T.D. (1992) Fundamentals of Vehicle Dynamics. SAE International.
- Savaresi, S.M., Poussot-Vassal, C., Spelta, C., Sename, O. and Dugard, L. (2010) Semi-active Suspension Technologies and Models. In: Semi - Active Suspension Control Design for Vehicles , Elsevier, 15-39. https://doi.org/10.1016/b978-0-08-096678-6.00002-x.
- Podlubny, I. (1998) Fractional Differential Equations. Academic Press.
- Diethelm, K. (2010) The Analysis of Fractional Differential Equations: An Application-Oriented Exposition Using Differential Operators of Caputo Type. Springer.
- Mainardi, F. (2010) Fractional Calculus and Waves in Linear Viscoelasticity. Imperial College Press. https://doi.org/10.1142/9781848163300
- Valerio, D. and Machado, J.T. (2009) Fractional Order Dynamics in Mechanical Systems: Modeling and Control. Mechatronics , 19, No. 7.
- Chen, W.C., Chen, C.L. and Feng, G. (2006) A Fractional Order PID Controller for a Quarter-Car Active Suspension System. 2006 International Conference on Mechatronics and Automation , Luoyang, 25-28 June 2006, 1093-1098.
- Oldham, K.B. and Spanier, J. (1974) The Fractional Calculus. Academic Press.
- Padovan, J. (1987) Computational Algorithms for FE Formulations Involving Fractional Operators. Computational Mechanics , 2, 271-287. https://doi.org/10.1007/bf00296422
- Kumar, A. and Kumar, P. (2017) Fractional Order PID Controller for Active Suspension System of a Quarter Car Model. International Journal of Dynamics and Control , 5, 711-721.
- Li, C. and Chen, G. (2004) Chaos in a Fractional-Order Chua’s System. Chaos , Solitons & Fractals , 19, 1301-1309.
- Pu, Y., et al . (2010) Fractional Calculus Model of a Vehicle Suspension System. Science China Technological Sciences , 53, 535-540.
- Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E. and Stricker, D. (2017) Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion. Sensors , 17, Article 1257. https://doi.org/10.3390/s17061257
- Das, A. and Morris, D. (2015) Road-Type Classification Using Vehicle Motion Features. 2015 IEEE Intelligent Vehicles Symposium , Seoul, 28 June-1 July 2015, 843-848.
- Eriksson, M., Jacobson, B. and Strömberg, H. (2008) Classification of Road Surface Conditions Using Vehicle Sensor Signals. Vehicle System Dynamics , 46, 441-451.