2020
DOI: 10.1016/j.isatra.2020.02.027
|View full text |Cite
|
Sign up to set email alerts
|

On the stability of ADRC for manipulators with modelling uncertainties

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
19
0
4

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
2
1

Relationship

0
8

Authors

Journals

citations
Cited by 52 publications
(23 citation statements)
references
References 17 publications
0
19
0
4
Order By: Relevance
“…For a closed-loop system, disturbance rejection ability is usually evaluated by dynamic stiffness, which can be obtained by adopting numerical solution. According to (3), by assuming G p (s) = G s (s)/Js, the following can be derived (see details in appendix B) Based on (17), block diagram of the nonlinear ADRC which originally depicted in Fig. 2 can be converted and shown in Fig.…”
Section: A Disturbance Rejection Ability Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…For a closed-loop system, disturbance rejection ability is usually evaluated by dynamic stiffness, which can be obtained by adopting numerical solution. According to (3), by assuming G p (s) = G s (s)/Js, the following can be derived (see details in appendix B) Based on (17), block diagram of the nonlinear ADRC which originally depicted in Fig. 2 can be converted and shown in Fig.…”
Section: A Disturbance Rejection Ability Analysismentioning
confidence: 99%
“…In recent years, theoretical studies on ADRC have drawn many attentions. Some of the achievements are rather prominent, such as the stability analysis with boundary disturbance and modelling uncertainties [15]- [17], the frequency-domain analysis [18], [19], the capability analysis of the extended state observer for nonlinear systems [20], [21], to name a few. Meanwhile, by combining ADRC with specific algorithms, such as fractional order control algorithm, intelligent algorithm, sliding mode control algorithm, the differential flatness theory, the performances of ADRC have been improved to a large extent [22]- [25].…”
Section: Introductionmentioning
confidence: 99%
“…The classical differentiator can solve the problem that position signals haven't derivation when there are signal noise [25] [26], but it magnifies signal noise many times. Tracking differentials [27] [28] are used to extract velocity signals, it has a certain inhibitory effect on signal noise. But there are at least 4 parameters that need to be debugged and phase delay.…”
Section: Introductionmentioning
confidence: 99%
“…Esta variação pode ocorrer por vários motivos: envelhecimento de componentes, mudança de características estruturais durante sua operação, variação de massa/carga, falta de conhecimento exato dos valores, por imperfeições na modelagem, dentre outras. Neste contexto, várias estratégias de controle robusto têm sido reportadas na literatura para solucionar o problema, com garantidas propriedades de estabilidade e convergência em malha fechada (Wu et al, 2018;Xia et al, 2018;Patelski and Dutkiewicz, 2020). O método de Controle com Rejeição Ativa de Distúrbios (do inglês, Active Disturbance Rejection Control-ADRC) está entre estas várias estratégias propostas, e será explorada neste artigo sob um paradigma diferenciado da teoria convencional (Madoński et al, 2015;Zuo et al, 2018;Meng et al, 2019).…”
Section: Introductionunclassified
“…A mudança de paradigma ADRC proposta no presente trabalho consiste em modificar a estrutura do ESO para projetar o sinal da lei de controle utilizando apenas umá unica estimativa do observador. Esta característica particular do método proposto contrasta com a abordagem ADRC tradicional, na qual a lei de controleé formada via uma parametrização linear nos estados estimados pelo ESO (Sun et al, 2016b,a;Wu et al, 2018;Xia et al, 2018;Patelski and Dutkiewicz, 2020). Para facilitar o entendimento do método proposto e suas características, este trabalho discute a aplicação do mesmo em plantas de segunda ordem.…”
Section: Introductionunclassified