Control and System

Dual Decomposition in Subspace Predictive Control under Fault Condition

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  • 1. The 28th Research Institute, China Electronics Technology Group Corporation, Nanjing 210007, China
    2. College of Astronomy and Space Science, Nanjing University, Nanjing 210093, China

Received date: 2013-11-24

  Revised date: 2014-05-12

  Online published: 2014-05-12

Abstract

This paper studies design of controllers in subspace predictive control structure under faulty conditions.
Having derived the output predictive estimations, statistic distribution of the residual and the expression
are analyzed. To optimize the prediction controller with equalities and inequalities, dual decomposition is used
to convert the former constrained optimization into unconstrained optimization. The incremental proximal
method is applied to solve the primal-dual optimization problem. With a helicopter as an example, effectiveness
of the proposed control strategy is verified.

Cite this article

WANG Jian-hong1, XU Ying1, XIONG Zhao-hua1, XU Bo2 . Dual Decomposition in Subspace Predictive Control under Fault Condition[J]. Journal of Applied Sciences, 2014 , 32(6) : 652 -660 . DOI: 10.3969/j.issn.0255-8297.2014.06.016

References

[1]  CHIUSO A. The role of vector autoregressive modeling in predictor based subspace identification [J]. Automatica, 2007, 43(6): 1034-1048.

[2]  CHIUSO A. On the relation between CCA and predictor based subspace identification[J]. IEEE Transactions of Automatic Control, 2007, 52(10): 1795-1811.

[3] 王建宏.子空间预测控制算法在主动噪声振动中的应用[J]. 振动与冲击,2011,30(10): 129-135.

WANG Jianhong, WANG Daobo. Subspace predictive control applied to active noise and vibration control[J]. Journal of Vibration and Shock, 2011, 30(10): 129-135. (in Chinese)

[4] 王建宏.子空间预测控制中的椭球优化及其应用[J]. 应用科学学报,2010,28(4): 424-429.

WANG Jianhong, WANG Daobo. Application of the ellipsoid optimization algorithm in subspace predictive control[J]. Journal of Applied Science, 2010, 28(4): 424-429. (in Chinese)

[5] 王建宏.故障估计下子空间预测控制的快速梯度算法[J]. 上海交通大学学报:自然科学版,2013,47(7):1015-1021.

Wang Jianhong. Fast gradient algorithm in subspace          predictive control under fault estimation[J]. Journal of Shanghai Jiao Tong University: Natural Science Edition. 2013, 47(7): 1015-1021. (in Chinese)

[6] LJUNG L. System identification: theory for the user [M]. [S.l.]:  Prentice Hall, 1999.

[7] BOYD S, VANDENBERGHE L. Convex optimization [M]. UK:  Cambridge University Press, 2008.

[8] ZEILINGER Melanie. Real time suboptimal model predictive control using a combination of explicit MPC and online optimization [J]. IEEE Transactions of Automatic Control, 2011, 56(7): 1524-1534.

[9]  JAKOB K. A design algorithm using external perturbation to improve iterative feedback tuning convergence[J]. Automatica, 2011, 47(2): 2665-2670.

[10] OHLSSON Henrik. Identification of switched linear regression models using sum of norms regularization[J]. Automatica, 2013, 49(4): 1045-1050.

[11] Van Mulders Anne. Identification of systems with localised nonlinearity: from state space to block structured models[J]. Automatica, 2013, 49(5 ): 1392-1396.

[12] FELLER Christian. An improved algorithm for combinatorial multi-parameteric quadratic programming[J]. Automatica, 2013, 49(5 ): 1370-1376.

[13] FIOER Havard. Observers for interconnected nonlinear and linear systems[J]. Automatica, 2012, 48(7 ): 1339-1346.

[14] RAKO Lauret. Identification of switched linear systems via sparse optimization[J]. Automatica, 2011, 47(4 ): 668-677.

[15] Paul M J Vanden Hof. Identification of dynamic models in complex networks with prediction error methods [J].  Automatica, 2013, 49(10): 2994-3006.
 
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