  This paper presents a coupled, neural network-aided longitudinal cruise and
lateral path-tracking controller for an autonomous vehicle with model
uncertainties and experiencing unknown external disturbances. Using a feedback
error learning mechanism, an inverse vehicle dynamics learning scheme utilizing
an adaptive Radial Basis Function (RBF) neural network, referred to as the
Extended Minimal Resource Allocating Network (EMRAN) is employed. EMRAN uses an
extended Kalman filter for online learning and weight updates, and also
incorporates a growing/pruning strategy for maintaining a compact network for
easier real-time implementation. The online learning algorithm handles the
parametric uncertainties and eliminates the effect of unknown disturbances on
the road. Combined with a self-regulating learning scheme for improving
generalization performance, the proposed EMRAN-aided control architecture aids
a basic PID cruise and Stanley path-tracking controllers in a coupled form. Its
performance and robustness to various disturbances and uncertainties are
compared with the conventional PID and Stanley controllers, along with a
comparison with a fuzzy-based PID controller and an active disturbance
rejection control (ADRC) scheme. Simulation results are presented for both slow
and high speed scenarios. The root mean square (RMS) and maximum tracking
errors clearly indicate the effectiveness of the proposed control scheme in
achieving better tracking performance in autonomous vehicles under unknown
environments.
