Difference between revisions of "2015 AModifiedWeightedPseudoInverseC"

From GM-RKB
Jump to: navigation, search
m (Omoreira moved page Test:2015 AModifiedWeightedPseudoInverseC to 2015 AModifiedWeightedPseudoInverseC without leaving a redirect)
 
(4 intermediate revisions by 2 users not shown)
Line 1: Line 1:
* ([[2015_AModifiedWeightedPseudoInverseC|Shao et al., 2015]]) ⇒ [[author::Xingyue Shao]], [[author::Zixuan Liang]], [[author::Bai Chen]], and [[author::Cunjia Liu]]. ([[year::2015]]). “[https://www.semanticscholar.org/paper/A-modified-weighted-pseudo-inverse-control-using-Shao-Liang/d1e4312010d7473432db5e74c2db5b2a6c926d04 A Modified Weighted Pseudo-inverse Control Allocation Using Genetic Algorithm].” In: Proceedings of 34th Chinese Control Conference (CCC 2015).. [http://dx.doi.org/10.1109/ChiCC.2015.7260507 doi:10.1109/ChiCC.2015.7260507]  
+
* ([[2015_AModifiedWeightedPseudoInverseC|Shao et al., 2015]]) [[author::Xingyue Shao]], [[author::Zixuan Liang]], [[author::Bai Chen]], and [[author::Cunjia Liu]]. ([[year::2015]]). “[https://www.semanticscholar.org/paper/A-modified-weighted-pseudo-inverse-control-using-Shao-Liang/d1e4312010d7473432db5e74c2db5b2a6c926d04 A Modified Weighted Pseudo-inverse Control Allocation Using Genetic Algorithm].” In: Proceedings of 34th Chinese Control Conference (CCC 2015).. [http://dx.doi.org/10.1109/ChiCC.2015.7260507 doi:10.1109/ChiCC.2015.7260507]  
  
<B>Subject Headings:</B> [[Pseudo-Inverse Algorithm]]; [[Weighted Pseudo-Inverse Algorithm ]]
+
<B>Subject Headings:</B> [[Pseudo-Inverse Algorithm]]; [[Weighted Pseudo-Inverse Algorithm]], [[Modified Weighted Pseudo-Inverse Algorithm]].
  
==Notes==
+
== Notes ==
  
==Cited By==
+
== Cited By ==
* http://scholar.google.com/scholar?q=%222015%22+A+Modified+Weighted+Pseudo-inverse+Control+Allocation+Using+Genetic+Algorithm
+
* [[Google Scholar]]: 1 citation(s) [http://scholar.google.com/scholar?q=%222015%22+A+Modified+Weighted+Pseudo-inverse+Control+Allocation+Using+Genetic+Algorithm]
 +
* [[Semantic Scholar]]: 0 citation(s) [https://www.semanticscholar.org/paper/A-modified-weighted-pseudo-inverse-control-using-Shao-Liang/d1e4312010d7473432db5e74c2db5b2a6c926d04]
 +
* [[IEEE]]: 1 citation [https://ieeexplore.ieee.org/document/7260507/metrics#metrics]
  
 +
== Quotes ==
  
==Quotes==
+
=== Author Keywords ===
===Author Keywords===
 
 
* [[Control allocation]]; [[direct allication]]; [[weighted pseudo-inverse]]; [[genetic algorithm]]; [[weighted matrix]]
 
* [[Control allocation]]; [[direct allication]]; [[weighted pseudo-inverse]]; [[genetic algorithm]]; [[weighted matrix]]
  
===Abstract===
+
=== Abstract ===
  
 
[[Control allocation]] is used to [[distribute]] the [[control command]] into multiple [[effector]]s without exceeding the [[saturation]]s. </s>
 
[[Control allocation]] is used to [[distribute]] the [[control command]] into multiple [[effector]]s without exceeding the [[saturation]]s. </s>
A powerful [[control allocation algorithm]] could enhance the [[performance of the control system]]. </s>
+
A powerful [[control allocation algorithm]] could enhance the [[performance]] of the [[control system]]. </s>
[[This paper]] proposes a new [[control allocation methodology]] for the two desired [[moment problem]]. </s>
+
[[2015 AModifiedWeightedPseudoInverseC |This paper]] proposes a new [[control allocation methodology]] for the two desired [[moment problem]]. </s>
[[The proposed strategy]] is based on the [[weighted pseudo-inverse]]. </s>
+
[[2015 AModifiedWeightedPseudoInverseC|The proposed strategy]] is based on the [[weighted pseudo-inverse]]. </s>
It has high [[computational efficiency]] and [[easy structure]], but could not give the [[optimal result]]. </s>
+
It has high [[computational efficiency]] and easy [[structure]], but could not give the [[optimal result]]. </s>
[[Genetic algorithm]] is used to optimize the [[weighted matrix for better allocation result]]. </s>
+
[[Genetic algorithm]] is used to optimize the [[weighted matrix]] for better [[allocation result]]. </s>
Furthermore, the plane of desired moment is divided into several [[part]]s and each part will be bound with one [[weighted matrix]] chosen by [[genetic algorithm]]. </s>
+
Furthermore, the plane of desired [[moment]] is divided into several [[part]]s and each part will be [[bound]] with one [[weighted matrix]] chosen by [[genetic algorithm]]. </s>
 
[[Multiple weighted matrice]]s could guarantee better [[performance]] in each part than one [[constant matrix]]. </s>
 
[[Multiple weighted matrice]]s could guarantee better [[performance]] in each part than one [[constant matrix]]. </s>
To demonstrate the effectiveness of the [[algorithm]], [[direct allocation]] and the [[standard weighted pseudo-inverse]] are used for [[comparison]]. </s>
+
To demonstrate the [[effectiveness]] of the [[algorithm]], [[direct allocation]] and the [[standard weighted pseudo-inverse]] are used for comparison. </s>
[[Simulation result]]s show that the [[newly proposed algorithm]] could obtain [[allocation results approximate]] to or [[identical]] with the [[optimal solution]]s. </s>
+
[[Simulation result]]s show that the [[Modified Weighted Pseudo-Inverse Algorithm|newly proposed algorithm]] could obtain [[allocation result]]s [[approximate]] to or [[identical]] with the [[optimal solution]]s. </s>
  
==References==
+
== References ==
 +
# M. Bodson, "Evaluation of optimization methods for control allocation", Journal of Guidance Control and Dynamics, vol. 25, no. 4, pp. 703-711, Jul.-Aug. 2002.
 +
# J. A.M. Petersen, M. Bodson, "Constrained quadratic programming techniques for control allocation", IEEE Trans. Control Systems Technology, vol. 14, no. 1, pp. 91-98, Jan. 2006.
 +
# W. C. Durham, "Constrained control allocation", Journal of Guidance Control and Dynamics, vol. 17, no. 4, pp. 713-725, 1993.
 +
# J. Jin, "Modified pseudoinverse redistribution methodsfor redundant controls allocation", Journal of Guidance Control and Dynamics, vol. 28, no. 5,2005, pp. 1076-1079.
 +
# W. C. Durham, "Attainable moments for the constrained control allocation problem", Journal of Guidance Control and Dynamics, vol. 17, no. 6, pp. 1371-1373, 1994.
 +
# W. C. Durham, "Computationally efficient control allocation", Journal of Guidance Control and Dynamics, vol. 24, no. 3,2001, pp. 519-524.
 +
# J. M. Buffington, D. F. Enns, "Lyapunov Stability Analysis of Daisy Chain Control Allocation", Journal of Guidance Control and Dynamics, vol. 19, no. 6,1996, pp. 1226-1230.
 +
# TANG Shengyonga, ZHANG Shijiea, ZHANG Yulin, "A Modified Direct Allocation Algorithm with Application to Redundant Actuators", Chinese Journal of Aeronautics, vol. 24, pp. 299-308, 2011.
 +
# J. H. Holland, "Adaptation in natural and artificial systems".
 +
# M. S. Arumugam, M. V.C. Rao, Palaniappan, "New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems", Applied Soft Computing Journal, vol. 6, no. 1,2005, pp. 38-52.
 +
# K Atashkari, N Nariman-Zadeh, A Pilechi, A Jamali, X. Yao, "Thermodynamic Pareto optimization of turbojet engines using multiobjective genetic algorithms", International Journal of Thermal Sciences, vol. 44, no. 11,2005, pp. 1061-1071.
 
{{#ifanon:|
 
{{#ifanon:|
  

Latest revision as of 14:04, 13 July 2019

Subject Headings: Pseudo-Inverse Algorithm; Weighted Pseudo-Inverse Algorithm, Modified Weighted Pseudo-Inverse Algorithm.

Notes

Cited By

Quotes

Author Keywords

Abstract

Control allocation is used to distribute the control command into multiple effectors without exceeding the saturations. A powerful control allocation algorithm could enhance the performance of the control system. This paper proposes a new control allocation methodology for the two desired moment problem. The proposed strategy is based on the weighted pseudo-inverse. It has high computational efficiency and easy structure, but could not give the optimal result. Genetic algorithm is used to optimize the weighted matrix for better allocation result. Furthermore, the plane of desired moment is divided into several parts and each part will be bound with one weighted matrix chosen by genetic algorithm. Multiple weighted matrices could guarantee better performance in each part than one constant matrix. To demonstrate the effectiveness of the algorithm, direct allocation and the standard weighted pseudo-inverse are used for comparison. Simulation results show that the newly proposed algorithm could obtain allocation results approximate to or identical with the optimal solutions.

References

  1. M. Bodson, "Evaluation of optimization methods for control allocation", Journal of Guidance Control and Dynamics, vol. 25, no. 4, pp. 703-711, Jul.-Aug. 2002.
  2. J. A.M. Petersen, M. Bodson, "Constrained quadratic programming techniques for control allocation", IEEE Trans. Control Systems Technology, vol. 14, no. 1, pp. 91-98, Jan. 2006.
  3. W. C. Durham, "Constrained control allocation", Journal of Guidance Control and Dynamics, vol. 17, no. 4, pp. 713-725, 1993.
  4. J. Jin, "Modified pseudoinverse redistribution methodsfor redundant controls allocation", Journal of Guidance Control and Dynamics, vol. 28, no. 5,2005, pp. 1076-1079.
  5. W. C. Durham, "Attainable moments for the constrained control allocation problem", Journal of Guidance Control and Dynamics, vol. 17, no. 6, pp. 1371-1373, 1994.
  6. W. C. Durham, "Computationally efficient control allocation", Journal of Guidance Control and Dynamics, vol. 24, no. 3,2001, pp. 519-524.
  7. J. M. Buffington, D. F. Enns, "Lyapunov Stability Analysis of Daisy Chain Control Allocation", Journal of Guidance Control and Dynamics, vol. 19, no. 6,1996, pp. 1226-1230.
  8. TANG Shengyonga, ZHANG Shijiea, ZHANG Yulin, "A Modified Direct Allocation Algorithm with Application to Redundant Actuators", Chinese Journal of Aeronautics, vol. 24, pp. 299-308, 2011.
  9. J. H. Holland, "Adaptation in natural and artificial systems".
  10. M. S. Arumugam, M. V.C. Rao, Palaniappan, "New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems", Applied Soft Computing Journal, vol. 6, no. 1,2005, pp. 38-52.
  11. K Atashkari, N Nariman-Zadeh, A Pilechi, A Jamali, X. Yao, "Thermodynamic Pareto optimization of turbojet engines using multiobjective genetic algorithms", International Journal of Thermal Sciences, vol. 44, no. 11,2005, pp. 1061-1071.;


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 AModifiedWeightedPseudoInverseCXingyue Shao
Zixuan Liang
Bai Chen
Cunjia Liu
A Modified Weighted Pseudo-inverse Control Allocation Using Genetic Algorithm10.1109/ChiCC.2015.72605072015
AuthorXingyue Shao +, Zixuan Liang +, Bai Chen + and Cunjia Liu +
doi10.1109/ChiCC.2015.7260507 +
titleA Modified Weighted Pseudo-inverse Control Allocation Using Genetic Algorithm +
year2015 +