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Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism

Author
Azar, Ahmad T.; Serrano, F.; Rossell, Josep M.; Vaidyanathan, S.; Zhu, Q.
Type of activity
Journal article
Journal
International journal of computer applications in technology
Date of publication
2020-10-03
Volume
63
Number
4
First page
273
Last page
285
DOI
10.1504/IJCAT.2020.10032593
Repository
http://hdl.handle.net/2117/331398 Open in new window
URL
https://www.inderscience.com/info/inarticle.php?artid=110404 Open in new window
Abstract
In this paper, a novel control strategy based on an adaptive Self-Recurrent Wavelet Neural Network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists of two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and the...
Citation
Azar, A.T. [et al.]. Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism. "International journal of computer applications in technology", 3 Octubre 2020, vol. 63, núm. 4, p. 273-285.
Keywords
Adaptive wavelet neural networks, Slider crank mechanism, Sliding mode control, Sliding mode observer
Group of research
CoDAlab - Control, Dynamics and Applications

Participants

  • Azar, Ahmad T.  (author)
  • Serrano, Fernando E.  (author)
  • Rossell Garriga, Josep Maria  (author)
  • Vaidyanathan, Sundarapandian  (author)
  • Zhu, Quanmin  (author)