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

IAENG Internaitonal journal of computer science

article

Tim Chen¹  Chih Ching Hung²  Yu Ching Huang³  John C.Y. Chen⁴  Samiur Rahman⁵  Towfiqul Islam Mozumder⁵ 

[1] Faculty of Information Technology Ton Duc Thang University;Department of Mechanical Engineering, National Taiwan University, Taiwan Faculty of Electronic Engineering, Taipei Municipal Muzha Vocational High School;Department of Earth Science, National Taiwan Normal University, Taiwan Center of Natural Science, Kaohsiung Municipal Fushan Junior High School;Department of Artificial Intelligence University of Maryland;School of Engineering and Physical Sciences North South University

关键词: Evolved control;    MEVW;    Nonlinear Lyapunov method;    Adaptive fuzzy control;    artificial intelligence tool;    Grey DGM (2;    1) model.;   

DOI  :  10.15837/ijccc.2021.3.3991

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 摘 要 】

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.

【 授权许可】

CC BY