Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm. Academic Article

abstract

• This paper presents the theoretical results on sliding mode control (SMC) of neural networks via continuous or periodic sampling event-triggered algorithm. Firstly, SMC with continuous sampling event-triggered scheme is developed and the practical sliding mode can be achieved. In addition, there is a consistent positive lower bound for the time interval between two successive trigger events which implies that the Zeno phenomenon will not occur. Next, a more economical and realistic SMC technique is presented with periodic sampling event-triggered algorithm, which guarantees the robust stability of the augmented system. Finally, two illustrative examples are presented to substantiate the effectiveness of the derived theoretical results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Wang, S., Cao, Y., Huang, T., Chen, Y., Li, P., & Wen, S.

citation count

• 50

complete list of authors

• Wang, Shiqin||Cao, Yuting||Huang, Tingwen||Chen, Yiran||Li, Peng||Wen, Shiping

publication date

• January 2020

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Event-triggering
• Neural Network
• Neural Networks, Computer
• Periodic Sampling
• Sampling Studies
• Sliding Mode Control

PubMed Central ID

• 31546126

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2019.09.001

start page

• 140

end page

• 147

volume

• 121

URL

• http://dx.doi.org/10.1016/j.neunet.2019.09.001