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Adaptive super-twisting sliding mode control for micro gyroscope based on double loop fuzzy neural network structure
- Source :
- International Journal of Machine Learning and Cybernetics. 12:611-624
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- In this paper, a new adaptive super-twisting sliding mode control (STSMC) scheme based on a double loop fuzzy neural network (DLFNN) is proposed to solve the problem of the external disturbances and approximate the unknown model for a micro gyroscopes. The STSMC algorithm can effectively suppress chattering since it can hide the high-frequency switching part in the high-order derivative of the sliding mode variable and transfer the discrete control law to the high-order sliding mode surface. Because it not only combines the advantages of fuzzy systems, but also incorporates the advantages of neural network control, the proposed double loop fuzzy neural network can better approximate the system model with excellent approximation. Moreover, it has the advantage of full adjustment, and the initial values of all parameters in the network can be arbitrarily set, then the parameters can be adjusted to the optimal stable value adaptively according to the adaptive algorithm. Finally, the superiority of the STSMC algorithm is also discussed. Simulation results verify the superiority of the STSMC algorithm, showing it can improve system performance and estimate unknown models more accurately compared with conventional neural network sliding mode control (CNNSMC).
- Subjects :
- 0209 industrial biotechnology
Adaptive control
Artificial neural network
Adaptive algorithm
Computer science
020208 electrical & electronic engineering
Mode (statistics)
Computational intelligence
Gyroscope
02 engineering and technology
Fuzzy control system
Sliding mode control
law.invention
020901 industrial engineering & automation
Artificial Intelligence
law
Control theory
0202 electrical engineering, electronic engineering, information engineering
Computer Vision and Pattern Recognition
Software
Subjects
Details
- ISSN :
- 1868808X and 18688071
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- International Journal of Machine Learning and Cybernetics
- Accession number :
- edsair.doi...........947f069fd90d0fbdc3ca416c997996d9