Backstepping based fuzzy logic control of active vehicle suspension systems

In this paper, we present a backstepping based fuzzy logic (FL) scheme for the active control of vehicle suspension systems using the two-degrees of freedom or 1/4 car model. The full dynamics of a novel hydraulic strut are considered. The servo-valve dynamics are also included. A fuzzy logic system is used to estimate the nonlinear hydraulic strut dynamics. The backstepping fuzzy logic scheme is shown to give superior performance over passive suspension and active suspension control using conventional proportional-integral-derivative (PID) schemes. The FL system is adapted in such a way as to estimate online the unknown hydraulic dynamics and provide the backstepping loop with the desired servo-valve positioning so that the scheme becomes adaptive, guaranteeing bounded tracking errors and parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard adaptive backstepping techniques, no linear-in-the-parameters assumptions are needed.

[1]  Edwin K. P. Chong,et al.  Fuzzy precompensation of PID controllers , 1993, Proceedings of IEEE International Conference on Control and Applications.

[2]  Toshio Yoshimura,et al.  Active control for the suspension of large-sized buses using fuzzy logic , 1996, Int. J. Syst. Sci..

[3]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[4]  David Levy,et al.  Book review: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence by Bart Kosko (Prentice Hall 1992) , 1992, CARN.

[5]  Gang Tao,et al.  Adaptive Control of Systems with Actuator and Sensor Nonlinearities , 1996 .

[6]  Jean-Jacques E. Slotine,et al.  Adaptive manipulator control: A case study , 1988 .

[7]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[8]  Jung-Shan Lin,et al.  Nonlinear design of active suspensions , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[9]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[10]  Dean Karnopp,et al.  Electronically Controllable Vehicle Suspensions , 1991 .

[11]  Weiping Li,et al.  Adaptive high-precision control of positioning tables-theory and experiments , 1994, IEEE Trans. Control. Syst. Technol..

[12]  Frank L. Lewis,et al.  Active Suspension Control of Ground Vehicle Heave and Pitch Motions , 1999 .

[13]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[14]  Xiao-Jun Zeng,et al.  Approximation theory of fuzzy systems-MIMO case , 1995, IEEE Trans. Fuzzy Syst..

[15]  Hao Ying,et al.  General analytical structure of typical fuzzy controllers and their limiting structure theorems , 1993, Autom..

[16]  Frank L. Lewis,et al.  Active suspension control using a novel strut and active filtered feedback: design and implementation , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[17]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[18]  Nader Sadegh,et al.  A perceptron network for functional identification and control of nonlinear systems , 1993, IEEE Trans. Neural Networks.

[19]  Andrew G. Alleyne,et al.  On the limitations of force tracking control for hydraulic active suspensions , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[20]  Jung-Shan Lin,et al.  Nonlinear design of active suspensions , 1997 .

[21]  Frank L. Lewis,et al.  Robust backstepping control of nonlinear systems using neural networks , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[22]  Dean Karnopp.  Passive and Active Control of Road Vehicle Heave and Pitch Motion , 1987 .

[23]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[24]  B. Cai,et al.  Intelligent Vehicle Active Suspension Control Using Fuzzy Logic , 1993 .

[25]  Hao Ying,et al.  Sufficient conditions on general fuzzy systems as function approximators , 1994, Autom..

[26]  Joos Vandewalle,et al.  Fuzzy systems as universal approximators : Constructive theory , 1997 .

[27]  Giorgio Rizzoni,et al.  Including the Force Generation Process in Active Suspension Control Formulation , 1993 .

[28]  Dean Karnopp,et al.  ACTIVE DAMPING IN ROAD VEHICLE SUSPENSION SYSTEMS , 1983 .

[29]  Lakmal Seneviratne,et al.  Adaptive Control Of Robot Manipulators , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Xiao-Jun Zeng,et al.  Approximation theory of fuzzy systems-SISO case , 1994, IEEE Trans. Fuzzy Syst..

[31]  Frank L. Lewis,et al.  Towards a paradigm for fuzzy logic control , 1994, NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intellige.

[32]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[33]  Norihiko Adachi,et al.  Adaptive control of partially known systems using backstepping: application to H/sub /spl infin// design of active suspension , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[34]  J. K. Hedrick,et al.  Nonlinear Control of a Quarter Car Active Suspension , 1992, 1992 American Control Conference.

[35]  Andrew G. Alleyne,et al.  Nonlinear adaptive control of active suspensions , 1995, IEEE Trans. Control. Syst. Technol..

[36]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.