Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지

A Model Reference Variable Structure Control based on a Neural Network System Identification for an Active Four Wheel Steering System

  • Published : 2000.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.

Keywords

References

  1. Proc. IEEE Conf. Neural Inform. Process. Systems-Neuraland Synthetic(Denver) Nonlinear Signal Processing Using Neural Networks A. Lapedes;R. Farber
  2. IEEE Trans Neural Networks v.1 Identification and Control of Dynamical Systems Using Neural Networks K. S. Narendra;K Partharathy
  3. Proc. Int. Joint Conf. Neural Networks Theory of the Back-propagation Neural network R. Hecht-Nielson
  4. Neural Networks v.2 no.5 Multilayer Feedforward Networks are Universl Approximators K. Hornik
  5. Proc. 1989 American control conf. Use of Neural Nets for Dynamic Modeling and Control of Chemical Pocess Systems N. Bhat;T. J. McAvoy
  6. IEEE Control Syst. magazine Modeling Chemical Process Systems via Neural Computation N. Bhat;P. Minderman;T.J. McAvoy;N. S. Wang
  7. Proc. 1987 Int. Conf. Syst. Man Cybern. Learning How the world Works;Specifications for predictive networks in robots and brains P. J. Werbos
  8. Int. J. Contr. v.51 no.6 Nonlinear System Identification Using Neural Networks S. Chen;S. A. Billings;P.M. Grant
  9. Intelligent Automation and Soft Computing, An International Journal v.2 no.3 A Robust Learning Control of Neural Network for an Active four wheel streering system H.Y.Kim;P.I.Ro
  10. JSAE Review v.9 no.3 Theoretical Study on Active Four-Wheel-Steering System by Virtual Vehicle Model Following Control Nagai, M.;Ohki, M.
  11. Proceedings of 12th IAVSD A Robust Adaptive Rear Wheel Steering Control System for Handling Improvement of Four-Wheel Steering Vehicles N. Yuhara;S. Horiuchi;Y. Arato.
  12. Proceedings of the Institution of Mechanical Engineers, Journal of Automobile Engineering v.210 no.D4 A Robust Control of Four wheel steering system for vehicle handling improvement using the sliding mode P.I.Ro;H.Y.Kim
  13. Proc. Institution of Mechanical Engineers Theoretical Prediction and experimental Substantiation of the Response of the Automobile to steering Control L. Segel
  14. Journal of Passenger Cars A tire Side Force Model by Artificial Neural Network H.Y. Kim;P.I. Ro
  15. IEEE Trans Neural Networks v.3 no.1 Comparison of Four Neural Net Learning Methods for Dynamic System Identification S. Qin;H. Su;T. McAvoy
  16. Proc. of ACC Improvement of High Speed 4-WS Vehicle Handling Performance by Sliding Mode Control P.I. Ro;H.Y. Kim