Open AccessAdaptive neural network control for active suspension system with actuator saturation
Author(s) -
Zhao Feng,
Ge Shuzhi Sam,
Tu Fangwen,
Qin Yechen,
Dong Mingming
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.1317
Subject(s) - control theory (sociology) , lyapunov function , artificial neural network , parametric statistics , plant , adaptive control , actuator , active suspension , controller (irrigation) , state observer , computer science , observer (physics) , control engineering , engineering , mathematics , control (management) , nonlinear system , artificial intelligence , statistics , physics , quantum mechanics , agronomy , biology
This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non‐linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer‐based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed‐loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non‐linear dynamics and control input constraints.