Fuzzy Sliding Mode Control of an MR Mount for Vibration Attenuation

In this paper, a magnetorheological (MR) mount with fuzzy sliding mode controller (FSMC) is studied for vibration suppression when the system is subject to base excitations. In recent years, magnetorheological fluids are becoming a popular material in the field of the semi-active control. However, the dynamic equation of an MR mount is highly nonlinear and it is difficult to identify. FSMC provides a simple method to achieve vibration attenuation of the nonlinear system with uncertain disturbances. This method is capable of handling the chattering problem of sliding mode control effectively and the fuzzy control rules are obtained by using the Lyapunov stability theory. The numerical simulations using one-dimension and two-dimension FSMC show effectiveness of the proposed controller for vibration suppression. Further, the well-known skyhook control scheme and an adaptive sliding mode controller are also included in the simulation for comparison with the proposed FSMC..

[1]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

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

[3]  Hung-Yuan Chung,et al.  Fuzzy sliding mode controller design for uncertain time-delayed systems with nonlinear input , 2003, Fuzzy Sets Syst..

[4]  Chieh-Li Chen,et al.  Optimal design of fuzzy sliding-mode control: A comparative study , 1998, Fuzzy Sets Syst..

[5]  J. K. Hedrick,et al.  A model following sliding mode controller for semi-active suspension systems with MR dampers , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[6]  Young-Pil Park,et al.  H8 Control Performance of a Full-Vehicle Suspension Featuring Magnetorheological Dampers , 2002 .

[7]  NEUROPHYSIOLOGICAL CHARACTERISTICS OF THE FUZZY LOGIC OF PROBLEM-SOLVING , 1984 .

[8]  Minho Lee,et al.  A new neuro-fuzzy identification model of nonlinear dynamic systems , 1994, Int. J. Approx. Reason..

[9]  Nikola K. Kasabov,et al.  HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems , 1999, Neural Networks.

[10]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[11]  J. Shaw,et al.  ICSV 14 Cairns • Australia 9-12 July , 2007 ADAPTIVE CONTROL OF AN MR MOUNT FOR VIBRATION ATTENUATION , 2007 .

[12]  Byeonghwa Kim,et al.  Linearization of magnetorheological behaviour using a neural network , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[13]  Lu Zheng Study on active and semi-active air-spring suspension systems for railway vehicles , 2001 .

[14]  Shirley J. Dyke,et al.  An experimental study of MR dampers for seismic protection , 1998 .

[15]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Shiuh-Jer Huang,et al.  Adaptive fuzzy controller with sliding surface for vehicle suspension control , 2003, IEEE Trans. Fuzzy Syst..

[17]  Jaehwan Kim,et al.  VIBRATION CONTROL OF A PASSENGER VEHICLE FEATURING MAGNETORHEOLOGICAL ENGINE MOUNTS , 2003 .

[18]  H. E. Tseng,et al.  Semi-Active Control Laws - Optimal and Sub-Optimal , 1994 .