RBF-NN based Upper-bound Adaptive learning Sliding Mode Control for Grid-Connected Converter

Wu, Duidi; Liu, Bo; Cheng, Yaozong

doi:10.1109/icma49215.2020.9233615

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been specified in [3].Basically; it contains two main disadvantages of unpredictability in the nonlinear system. The initial one is the matching condition [4] and to control such type of the system some methods are proposed [5]. Final one is triangularity assumption.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Adjustable Radial Basis Function estimations for Non-Linear System

Mulla

Chavan

Lodha

et al. 2022

SMSJ

View full text Add to dashboard Cite

This paper gives idea about design method for adaptive neural controller is proposed and it is applied to the non-linear system Continuous stirred tank reactor CSTR. The investigating controller used in this paper is designed in tuned with adaptive process. To analyze the performance of effect of foot print of uncertainty on the controllers’ performance two various types of algorithms namely state feedback control and observer based control are used Radial basis function Neural network is utilized for approximation of the nonlinear function . Software validation result of suggested method is discussed below.

show abstract

Section: Introductionmentioning

confidence: 99%