Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory


  • Tim Chen
  • Chih Ching Hung
  • Yu Ching Huang
  • John C.Y. Chen
  • Samiur Rahman
  • Towfiqul Islam Mozumder


Evolved control, MEVW, Nonlinear Lyapunov method, Adaptive fuzzy control, artificial intelligence tool, Grey DGM (2,1) model


In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels.

Author Biographies

Tim Chen

Faculty of Information Technology
Ton Duc Thang University, Ho Chi Minh City, Vietnam

Chih Ching Hung

Department of Mechanical Engineering, National Taiwan University, Taipei, Taiwan
Faculty of Electronic Engineering, Taipei Municipal Muzha Vocational High School, Taipei, Taiwan

Yu Ching Huang

Department of Earth Science, National Taiwan Normal University, Taipei, Taiwan
Center of Natural Science, Kaohsiung Municipal Fushan Junior High School, Kaohsiung, Taiwan

John C.Y. Chen

Department of Artificial Intelligence
University of Maryland, USA

Samiur Rahman

School of Engineering and Physical Sciences
North South University, Dhaka, Bangladesh

Towfiqul Islam Mozumder

School of Engineering and Physical Sciences
North South University, Dhaka, Bangladesh


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