Neuroadaptive combined lateral and longitudinal control of highway vehicles using RBF networks

A neural network (NN) adaptive model-based combined lateral and longitudinal vehicle control algorithm for highway applications is presented in this paper. The controller is synthesized using a proportional plus derivative control coupled with an online adaptive neural module that acts as a dynamic compensator to counteract inherent model discrepancies, strong nonlinearities, and coupling effects. The closed-loop stability issues of this combined control, scheme are analyzed using a Lyapunov-based method. The neurocontrol approach can guarantee the uniform ultimate bounds of the tracking errors and bounds of NN weights. A complex nonlinear three-degree-of-freedom dynamic model of a passenger wagon is developed to simulate the vehicle motion and for controller design. The controller is tested and verified via computer simulations in the presence of parametric uncertainties and severe driving conditions.