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Neural-network-based robust control for steer-by-wire systems with uncertain dynamics

Wang, H.ORCID: 0000-0003-2789-9530, Xu, Z., Do, M.T., Zheng, J., Cao, Z. and Xie, L. (2015) Neural-network-based robust control for steer-by-wire systems with uncertain dynamics. Neural Computing and Applications, 26 (7). pp. 1575-1586.

Link to Published Version: https://doi.org/10.1007/s00521-014-1819-2
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Abstract

This study develops a neural-network-based robust control scheme for steer-by-wire systems with uncertain dynamics. The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the effects of uncertainties can be effectively eliminated in the closed-loop system. Using the proposed neural control scheme, not only the robust steering performance against parameter variations and road disturbances is obtained, but also both the control gain and the control design complexity are greatly reduced due to the use of the RBFNN. Simulation results are demonstrated to verify the superior control performance of the proposed control scheme, in comparison with other control strategies.

Item Type: Journal Article
Publisher: Springer
Copyright: © The Natural Computing Applications Forum 2015
URI: http://researchrepository.murdoch.edu.au/id/eprint/53407
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