NDLI: A RBF neural network sliding mode controller for SMA actuator

Content Provider	Springer Nature Link
Author	Tai, Nguyen Trong Ahn, Kyoung Kwan
Copyright Year	2010
Abstract	A radial basis function neural network sliding-mode controller (RBFSMC) is proposed to control a shape memory alloy (SMA) actuator. This approach, which combines a RBF neural network with sliding-mode control (SMC), is presented for the tracking control of a class of nonlinear systems having parameter uncertainties. The centers and output weights of the RBF neural network are updated through on-line learning, which causes the output of the neural network control to approximate the sliding-mode equivalent control along the direction that makes the sliding-mode asymptotically stable. Using Lyapunov theory, the asymptotic stability of the overall system is proven. Then, the controller is applied to compensate for the hysteresis phenomenon seen in SMA. The results show that the controller was applied successfully. The control results are also compared to those of a conventional SMC.
Starting Page	1296
Ending Page	1305
Page Count	10
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	8
Issue Number	6
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2011-01-08
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adaptive control RBF neural network shape memory alloy control sliding mode control Control Robotics Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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