@article{ref1,
title="Adaptive steering angle controller for autonomous vehicles in the presence of parameter uncertainty and disturbances",
journal="International journal of automotive technology",
year="2022",
author="Khasawneh, Lubna and Das, Manohar",
volume="23",
number="5",
pages="1313-1321",
abstract="This paper addresses the problem of controlling the electric power steering angle for autonomous vehicles in the presence of model parameter uncertainty and disturbances. Usually, the electric power steering manufacturer provides it as a black box to the automotive companies and does not provide the model or the model parameters. Identifying those parameters is a time-consuming process that requires a special vehicle setting, and does not always give accurate results. Inaccuracies on those parameters deteriorate the behavior of the model-based controller that is using them. To overcome those problems, an adaptive backstepping steering angle controller is designed, which assumes the steering model parameters are unknown and develops parameter update laws to estimate them. Adaptive laws are also developed to estimate the disturbance resulting from self-aligning moment. Usually, self-aligning moment estimation requires tire parameters knowledge. In the adaptive backstepping method, it is treated as disturbance and no knowledge of tire parameters is required in order to estimate it. Coulomb friction and static friction are considered as disturbances and estimated together with the self-aligning moment disturbance. The performance of the proposed method was validated with numerical simulation.<p /> <p>Language: en</p>",
language="en",
issn="1229-9138",
doi="10.1007/s12239-022-0114-y",
url="http://dx.doi.org/10.1007/s12239-022-0114-y"
}