2009
DOI: 10.1108/00022660910997856
|View full text |Cite
|
Sign up to set email alerts
|

Linearization‐based attitude error regulation: multiplicative error case

Abstract: Linearization-based attitude error regulation: multiplicative error case R. Ozgur Doruk Article information:To cite this document: R. Ozgur Doruk, (2009),"Linearization-based attitude error regulation: multiplicative error case", Aircraft Engineering and Aerospace Technology, Vol. 81 Iss 6 pp. 536 -540 Permanent link to this document: http://dx.(2010),"State space identification and implementation of H∞ control design for small-scale helicopter", Aircraft Engineering and Aerospace Technology, Vol. 82 Iss 6 pp.… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2017
2017
2017
2017

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 1 publication
0
2
0
Order By: Relevance
“…There are many studies about different types of linear and nonlinear techniques implemented on quadrotors such as PID (Naidoo et al , 2011; Pounds et al , 2010; Salih et al , 2010), fuzzy controller (Zare et al , 2014; Zemalache and Maaref, 2009), nonlinear H ∞ controller (Raffo et al , 2010; Schafroth et al , 2010), linear quadratic regulator (LQR) controller (Kumar et al , 2008; Ozgur Doruk, 2009), linear quadratic gaussian (LQG) controller (Oktay, 2014; Reza Dharmayanda et al , 2010) and adaptive controller (Nicol et al , 2011).…”
Section: Mathematical Model Of Quadrotormentioning
confidence: 99%
“…There are many studies about different types of linear and nonlinear techniques implemented on quadrotors such as PID (Naidoo et al , 2011; Pounds et al , 2010; Salih et al , 2010), fuzzy controller (Zare et al , 2014; Zemalache and Maaref, 2009), nonlinear H ∞ controller (Raffo et al , 2010; Schafroth et al , 2010), linear quadratic regulator (LQR) controller (Kumar et al , 2008; Ozgur Doruk, 2009), linear quadratic gaussian (LQG) controller (Oktay, 2014; Reza Dharmayanda et al , 2010) and adaptive controller (Nicol et al , 2011).…”
Section: Mathematical Model Of Quadrotormentioning
confidence: 99%
“…On the other side, linear quadratic regulator (LQR) is one of the well-known and powerful methods in the design of optimal controllers which ensures a satisfactory robustness 9 and has shown good capabilities in a wide variety of applications. 10–14 Besides guaranteed Gain Margin (GM) and Phase Margin (PM), LQR also provides the designer the ability to manage the amount of error and control effort via state and control weighting matrices available in the cost function. 15 These two matrices in fact govern the feedback gain matrix and the designer can tune them to modify the system eigenstructure.…”
Section: Introductionmentioning
confidence: 99%