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Robust EMRAN-aided coupled controller for autonomous vehicles

Debarshi, S and Sundaram, S and Sundararajan, N (2022) Robust EMRAN-aided coupled controller for autonomous vehicles. In: Engineering Applications of Artificial Intelligence, 110 .

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Official URL: https://doi.org/10.1016/j.engappai.2022.104717


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. © 2022 Elsevier Ltd

Item Type: Journal Article
Publication: Engineering Applications of Artificial Intelligence
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Authors
Keywords: Adaptive control systems; Adaptive cruise control; Autonomous vehicles; Controllers; Disturbance rejection; E-learning; Kalman filters; Learning systems; Radial basis function networks; Real time control; Three term control systems; Uncertainty analysis, Autonomous Vehicles; Extended minimal resource allocating network neural network; Inverse learning; Inverse learning control; Learning control; Learning schemes; Minimal resource allocating networks; Neural-networks; Path tracking; Tracking controller, Learning algorithms
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 16 Mar 2022 06:06
Last Modified: 16 Mar 2022 06:06
URI: http://eprints.iisc.ac.in/id/eprint/71431

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