Conference paper
RBF-neural-network-based sliding mode controller of automotive steer-by-wire systems
2015 11th International Conference on Natural Computation (ICNC), pp.907-914
IEEE
2015 11th International Conference on Natural Computation (ICNC) (Zhangjiajie, China, 15/08/2015–17/08/2015)
2015
Abstract
This study proposes a robust steering controller for Steer-by-Wire systems using neural network. The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is utilized to adaptively learn the uncertainty bound in the Lyapunov sense and thus the uncertainty effects are effectively eliminated. Using the proposed neural controller, 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 validate the superior control performance of the proposed control as compared with other controllers.
Details
- Title
- RBF-neural-network-based sliding mode controller of automotive steer-by-wire systems
- Authors/Creators
- H. Wang (Author/Creator) - Hefei University of TechnologyH. Kong (Author/Creator) - Hefei University of TechnologyM. Yu (Author/Creator) - Hefei University of TechnologyZ. Man (Author/Creator) - Swinburne University of TechnologyJ. Zheng (Author/Creator) - Swinburne University of TechnologyM.T. Do (Author/Creator) - Swinburne University of Technology
- Publication Details
- 2015 11th International Conference on Natural Computation (ICNC), pp.907-914
- Conference
- 2015 11th International Conference on Natural Computation (ICNC) (Zhangjiajie, China, 15/08/2015–17/08/2015)
- Publisher
- IEEE
- Identifiers
- 991005543246207891
- Copyright
- © 2015 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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