Integrated Control of Suspension and Steering Systems Based on Vehicle Handling and Performance

Abstract:

In this paper, the features of electrical power steering (EPS) and semi-active suspension (SAS) systems have been carefully researched, and an integrated control strategy based on neural network theory was proposed to achieve the integrated control of EPS and SAS systems. In order to achieve the simulation of the integrated control strategy, a neural network controller of EPS and SAS systems was designed under MATLAB environment, and a passenger car virtual prototyping model including the EPS and SAS systems was established in vehicle dynamics simulation software SIMPACK. By ride comfort and handing stability simulation, the integrated control strategy was proved to be effective. The primary goal of this paper is to propose an effective and reliable integrated control strategy of EPS and SAS systems, and improve the ride comfort as well as handing stability of automobile.

• Periodical: Advanced Materials Research (Volumes 457-458)
• Pages: 953-960
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.457-458.953
• Online since: January 2012
• Authors: Wei Liu, Wen Ku Shi, De Guang Fang, Fu Xiang Guo
• Keywords: Electrical Power Steering, Handing Stability, Integrated Control, Ride Comfort, Semi-Active Suspension

References

[1] G. Paolo, R. Gianpiero and G. Massimiliano, A neural-network-based model for the dynamic simulation of the tire/ suspension system while traversing road irregularities, IEEE Transactions on Neural Networks, 2008, v 19, n 9, pp.1549-1563.

DOI: 10.1109/tnn.2008.2000806

[2] E. Ikbal and Y. Sahin, Vibration control of vehicle active suspension system using a new robust neural network control system, Simulation Modelling Practice and Theory, May 2009, v 17, n 5, pp.778-793.

DOI: 10.1016/j.simpat.2009.01.004

[3] C. Shankararaman, Artificial neural network model for transient crossflow microfiltration of polydispersed suspensions, Journal of Membrane Science, August 2005, v 258, n 1-2, pp.35-42.

DOI: 10.1016/j.memsci.2004.11.038

[4] G. Rahmi and G. Kayhan, Neural network control of seat vibrations of a non-linear full vehicle model using PMSM, Mathematical and Computer Modelling, June 2008, v 47, n 11-12, pp.1356-1371.

DOI: 10.1016/j.mcm.2007.08.013

[5] K. Yoshinori and T. Kazuyoshi, Hopping height control of an active suspension type leg module based on reinforcement learning and a neural network, IEEE International Conference on Intelligent Robots and Systems, 2002, v 3, pp.2672-2677.

DOI: 10.1109/irds.2002.1041673

[6] A. Zafer, A neural network-based estimation of electric fields along high voltage insulators, Expert Systems with Applications, May 2009, v 36, n 4, pp.8705-8710.

DOI: 10.1016/j.eswa.2008.11.030

[7] T. Omer and H. Senad, Hybrid fuzzy-neural network structure for vehicle seat vibration isolation, 2009 IEEE International Conference on Control and Automation, 2009, ICCA 2009, pp.2354-2359.

DOI: 10.1109/icca.2009.5410379

[8] A. Murat and H. Kerem, Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data, Environmental Modelling and Software, January 2007 v 22, n 1, pp.2-13.

DOI: 10.1016/j.envsoft.2005.09.009