Universal approximation using radial-basis-function networks
Neural Computation
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Fast learning in networks of locally-tuned processing units
Neural Computation
Adaptive fuzzy control for inter-vehicle gap keeping
IEEE Transactions on Intelligent Transportation Systems
Neuroadaptive Combined Lateral and Longitudinal Control of Highway Vehicles Using RBF Networks
IEEE Transactions on Intelligent Transportation Systems
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
An improved radial basis function network for visual autonomous road following
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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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.