Abstract:
Automated vehicle control systems are a key technology
for intelligent vehicle highway systems (IVHSs). This paper
presents an automated vehicle control algorithm for combined longitudinal
and lateral motion control of highway vehicles, with special
emphasis on front-wheel-steered four-wheel road vehicles. The
controller is synthesized using an online neural-estimator-based
control law that works in combination with a lateral velocity
observer. The online adaptive neural-estimator-based design approach
enables the controller to counteract for inherent model
discrepancies, strong nonlinearities, and coupling effects. The neurocontrol
approach can guarantee the uniform ultimate bounds
(UUBs) of the tracking and observer errors and the bounds of the
neural weights. The key design features are 1) inherent coupling
effects will be taken into account as a result of combining of the
two control issues, viz., lateral and longitudinal control; 2) rather
ad hoc numerical approximations of lateral velocity will be avoided
via a combined controller–observer design; and 3) closed-loop
stability issues of the overall system will be established. The algorithm
is validated via a formative mathematical analysis based on
a Lyapunov approach and numerical simulations in the presence
of parametric uncertainties, as well as severe and adverse driving
conditions
