Adaptive Critic Design for Intelligent Steering and Speed Control of a 2-Axle Vehicle

Published In

Proceedings of the International Joint Conference on Neural Networks

Document Type


Publication Date



Selected Adaptive Critic (AC) methods are known to be capable of designing (approximately) optimal control policies for non-linear plants (in the sense of approximating Bellman Dynamic Programming). The present research focuses on an AC method known as Dual Heuristic Programming. There are many issues related to the pragmatics of successfully applying the AC methods, but now that the operational aspects of the DHP method are becoming refined and better understood, it is instructive to carry out empirical research with the method, to inform theoretical research being carried out in parallel. In particular, it is seen as useful to explore correspondences between the form of a Utility function and the resulting controllers designed by the DHP method. The task of designing a steering controller for a 2-axle, terrestrial, autonomous vehicle is the basis of the empirical work reported here (and in a companion paper). The new aspect in the present paper relates to using a pair of critics (distinct from the shadow critics described elsewhere by the authors) to `divide the labor' of training the controller. Improvements in convergence of the training process is realized in this way. The controllers designed by the DHP method are pleasingly robust, and demonstrate good performance on disturbances not even trained on - 1.) encountering a patch of ice during a steering maneuver, and 2.) encountering a wind gust perpendicular to direction of travel.

Locate the Document




Persistent Identifier