Open AccessGrey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory
Author(s) -
Timothy Chen,
Chih Ching Hung,
Hui Yu,
John C.Y. Chen,
Sejuti Rahman,
Towfiqul Islam Mozumder
Publication year - 2021
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2021.3.3991
Subject(s) - control theory (sociology) , backstepping , controller (irrigation) , lyapunov function , lyapunov stability , active suspension , computer science , convergence (economics) , nonlinear system , fuzzy logic , artificial neural network , mathematics , signal (programming language) , adaptive control , artificial intelligence , control (management) , actuator , physics , quantum mechanics , economics , agronomy , biology , programming language , economic growth
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.