Applications on Adaptive Recurrent Cerebellar Model Articulation Controller for Switched Reluctance Motor Drive Systems

Wang, Shun-Yuan; Liu, Foun-Yuan; Chou, Jen-Hsiang

doi:10.1109/is3c.2016.12

Cited by 2 publications

(2 citation statements)

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“…The recurrent neural network (RNN) is a special kind of neural network that naturally comprises feedback connections used as internal memories [52]. Many studies have used RNNs in their control network design [53][54][55][56][57] due to their advantages of simple architecture and dynamic characteristics. In 2018, Yen et al proposed robust adaptive sliding-mode control using recurrent fuzzy wavelet neural networks [54].…”

Section: Introductionmentioning

confidence: 99%

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Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Lin

Quynh

et al. 2020

Mathematics

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In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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Section: Introductionmentioning

confidence: 99%

“…In 2016, Sharma et al presented a robotic manipulator using a RNN and an adaptive controller similar to proportional-integral-derivative controllers [56]. In 2016, Wang et al proposed a switched-reluctance motor-drive system using adaptive recurrent CMAC [57].…”

Section: Introductionmentioning

confidence: 99%