Fuzzy logic control algorithms for MagneShock semiactive vehicle shock absorbers: design and experimental evaluations

Automotive ride quality and handling performance remain challenging design tradeoffs for modern, passive automobile suspension systems. Despite extensive published research outlining the benefits of active vehicle suspensions in addressing this tradeoff, the cost and complexity of these systems frequently prohibit commercial adoption. Semi-active suspensions can provide performance benefits over passive suspensions without the cost and complexity associated with fully active systems. This paper outlines the development and experimental evaluation of a fuzzy logic control algorithm for a commercial semi-active suspension component, Carrera's MagneShockTM shock absorber. The MagneShockTM utilizes an electromagnet to change the viscosity of magnetorheological (MR) fluid, which changes the damping characteristics of the shock. Damping for each shock is controlled by manipulating the coil current using real-time algorithms. The performance capabilities of fuzzy logic control (FLC) algorithms are demonstrated through experimental evaluations on a passenger vehicle. Results show reductions of 25% or more in sprung mass absorbed power (U.S. Army 6 Watt Absorbed Power Criterion) as compared to typical passive shock absorbers over urban terrains in both simulation and experimentation. Average sprung-mass RMS accelerations were also reduced by as much as 9%, but usually with an increase in total suspension travel over the passive systems. Additionally, a negligible decrease in RMS tire normal force was documented through computer simulations. And although the FLC absorbed power was comparable to that of the fixed-current MagneShockTM the FLC revealed reduced average RMS sprung-mass accelerations over the fixed-current MagneShocks by 2-9%. Possible means for improvement of this system include reducing the suspension spring stiffness and increasing the dynamic damping range of the MagneShockTM.

[1]  R S Sharp,et al.  Performance and Design Considerations for Dissipative Semi-Active Suspension Systems for Automobiles , 1987 .

[2]  J. Landaluze,et al.  Application of fuzzy logic control to the design of semi-active suspension systems , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[3]  L. R. Miller.  Tuning passive, semi-active, and fully active suspension systems , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[4]  D. Passaquay,et al.  Performance evaluation of a fuzzy rule base for control purpose , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[5]  Dean Karnopp,et al.  Vibration Control Using Semi-Active Force Generators , 1974 .

[6]  G.D. Buckner,et al.  Active vehicle suspension control using intelligent feedback linearization , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[7]  T D Gillespie,et al.  Fundamentals of Vehicle Dynamics , 1992 .

[8]  Mohd Azlan Hussain,et al.  Semi-active car suspension controller design using fuzzy-logic technique , 2002, Student Conference on Research and Development.

[9]  Keum-Shik Hong,et al.  Semi-active control of the Macpherson suspension system: hardware-in-the-loop simulations , 2000, Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162).

[10]  N. Al-Holou,et al.  The development of fuzzy logic based controller for semi-active suspension system , 1994, Proceedings of 1994 37th Midwest Symposium on Circuits and Systems.

[11]  A. J. Barr,et al.  Control of an active suspension using fuzzy logic , 1996, Proceedings of IEEE 5th International Fuzzy Systems.