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ADAPTIVE FUZZY-WAVELET NEURAL NETWORKS-BASED REAL-TIME MODEL GENERATION FOR INCREASING TRACKING PRECISION OF MULTIVARIABLE SERVO ACTUATOR, 49-54.
Sadeq Yaqubi, Mahdi Homaeinezhad, and Mohammad R. Homaeinezhad
References
[1] M.R. Homaeinezhad and S. Yaqubi, Adaptive fuzzy-waveletneural network identification core for reinforced control of gen-eral arbitrarily switched nonlinear multi input-multi outputdynamic systems, Applied Soft Computing, 91, 2020, 106265.https://doi.org/10.1016/j.asoc.2020.106265.
[2] M. Khenfer, B. Daaou, and M. Bouhamida, Two nonlinearobservers based sliding mode controller for a multivariable con-tinuous stirred tank reactor, Mechatronic Systems and Control,48(1), 2020. https://doi.org/10.2316/J.2020.201-0031.
[3] H. Du, X. Yu, M.Z.Q. Chen, and S. Li, Chattering-freediscrete-time sliding mode control, Automatica, 68, 2016, 87–91.https://doi.org/10.1016/j.automatica.2016.01.047.
[4] M.R. Homaeinezhad, S. Yaqubi, and M. Abolhasani Dolatabad,Friction-tracker-embedded discrete finite-time sliding mode con-trol algorithm for precise motion control of worm-gear reduc-ers under unknown switched assistive/resistive loading, Journalof Control, Automation and Electrical Systems, March 2020.https://doi.org/10.1007/s40313-020-00583-y.
[5] E. Hairer, G. Wanner, and S.P. Nørsett, Solving OrdinaryDifferential Equations I: Nonstiff Problems (Berlin: Springer,1993).
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Abstract
DOI:
10.2316/J.2022.201-0131
From Journal
(201) Mechatronic Systems and Control - 2022
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