Research on Nonlinear Automation for First Order Delays System

Roshanzamir, Ali; Piltan, Farzin; Jahed, Arman; Namvarchi, Saman; Nazari, Iman; Sulaiman, Nasri

doi:10.14257/ijhit.2015.8.9.29

Cited by 3 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main reasons to use fuzzy logic methodology are able to give approximate recommended solution for uncertain and also certain complicated systems to easy understanding and flexible. Fuzzy logic provides a method to design a model-free controller for nonlinear plant with a set of IF-THEN rules [16][17][18][19][20][21][22]. Fuzzy logic controls are used in many applications and they are used to modify linear and nonlinear controllers.…”

Section: Figure 1 Main Parts Of Robot Manipulatormentioning

confidence: 99%

Research on PID-Based Minimum Rule Base Fuzzy Controller in Active Joint Dental Automation

Matin¹,

Cheraghi²,

Sobhani³

et al. 2016

IJGDC

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Figure 1 Main Parts Of Robot Manipulatormentioning

confidence: 99%

Research on PID-Based Minimum Rule Base Fuzzy Controller in Active Joint Dental Automation

Matin¹,

Cheraghi²,

Sobhani³

et al. 2016

IJGDC

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the most challenging issues in mathematics is the design of a nonlinear model for multi nodes of networks [3]. Nonlinear consensus for reaching an agreement is presented by [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Convergence Consensus: Extreme Doubly Stochastic Quadratic Operators for Multi-Agent Systems

et al. 2020

View full text Add to dashboard Cite

We investigate a novel nonlinear consensus from the extreme points of doubly stochastic quadratic operators (EDSQO), based on majorization theory and Markov chains for time-varying multi-agent distributed systems. We describe a dynamic system that has a local interaction network among agents. EDSQO has been applied for distributed agent systems, on a finite dimensional stochastic matrix. We prove that multi-agent systems converge at a center (common value) via the extreme waited value of doubly stochastic quadratic operators (DSQO), which are only 1 or 0 or 1/2 1 2 if the exchanges of each agent member has no selfish communication. Applying this rule means that the consensus is nonlinear and low-complexity computational for fast time convergence. The investigated nonlinear model of EDSQO follows the structure of the DeGroot linear (DGL) consensus model. However, EDSQO is nonlinear and faster convergent than the DGL model and is of lower complexity than DSQO and cubic stochastic quadratic operators (CSQO). The simulation result and theoretical proof are illustrated.

show abstract

“…A quaternion-based resolved acceleration controller was presented in [2], and quaternion-based resolved rate and resolved acceleration task-space controllers were proposed in [4][5]. Output feedback task-space controllers using quaternion feedback were ability to model uncertain or ambiguous data, Figure 1, so often encountered in real life [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%