Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
For the pure feedback systems with uncertain actuator nonlinearity and non-differentiable non-affine function, a novel adaptive neural control scheme is proposed. Firstly, the assumption that the non-affine function must be differentiable everywhere with respect to control input has been canceled; in addition, the proposed approach can not only be applicable to actuator input dead zone nonlinearity, but also to backlash nonlinearity without changing the controller. Secondly, the neural network (NN) is used to approximate unknown nonlinear functions of system generated in the process of control design and a nonlinear robust term is introduced to eliminate the actuator nonlinearity modeling error, the NN approximation error and the external disturbances. Semi-globally uniformly ultimately boundedness of all signals in the closed loop system is analytically proved by utilizing Lyapunov theory. Finally, the effectiveness of the designed method is demonstrated via two examples.
Original language | English |
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Title of host publication | Neural Information Processing |
Subtitle of host publication | Proceedings of the 24th International Conference (ICONIP 2017) |
Editors | Derong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy |
Place of Publication | Cham, Switzerland |
Publisher | Springer |
Pages | 201-211 |
ISBN (Electronic) | 978-3-319-70136-3 |
ISBN (Print) | 978-3-319-70135-6 |
DOIs | |
Publication status | Published - 2017 |
Event | ICONIP 2017: 24th International Conference on Neural Information Processing - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017 |
Name | Lecture Notes in Computer Science |
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Volume | 10639 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference | ICONIP 2017: 24th International Conference on Neural Information Processing |
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Country | China |
City | Guangzhou |
Period | 14/11/17 → 18/11/17 |
ID: 35661613