Robust EMRAN-aided Coupled Controller for Autonomous Vehicles

TL;DR

This paper introduces a neural network-aided coupled control system for autonomous vehicles that improves robustness and tracking accuracy under uncertainties and disturbances, using an adaptive EMRAN neural network with online learning.

Contribution

It proposes a novel EMRAN-based adaptive control architecture that enhances vehicle control robustness and accuracy in uncertain environments, outperforming traditional controllers.

Findings

Enhanced tracking accuracy demonstrated in simulations.

Superior robustness against disturbances compared to conventional controllers.

Effective online learning and adaptation in real-time scenarios.

Abstract

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.