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Robust guaranteed-cost path-following control for autonomous vehicles on unstructured roads

This paper deals with the problem of automatic path-following control for a class of autonomous vehicle systems with parametric uncertainties and external disturbances in cross-country conditions. In the unstructured environments, the unevenness, the discontinuity and the variability of the terrain greatly increase the parametric uncertainties and the external perturbations of autonomous vehicles. To overcome these difficulties, a novel automatic path-following control scheme of vision-based autonomous vehicles is presented by utilizing the guaranteed-cost control theory. First, a new road detection algorithm used for segmenting and extracting the traversable path in unstructured terrains is achieved by using a combination consisting of multiple sensors, and the local relative position information between the vehicles and the desired trajectories can be acquired by the proposed detected algorithm in real time. Then, an optimal guaranteed-cost path-following control system is proposed, which can deal with the parametric uncertainties of autonomous vehicles and ensure the stability of the closed-loop control system. Finally, both simulation tests and experimental results demonstrate that the proposed control scheme can guarantee high path-tracking accuracy irrespective of the parametric uncertainties.