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Auxiliary signal design for failure detection in differentialalgebraic equations
1.  Department of Mathematics, North Carolina State University, Raleigh, North Carolina, 276958205, United States, United States 
References:
[1] 
I. Andjelkovic, K. A. Sweetingham and S. L. Campbell, Active fault detection in nonlinear systems using auxiliary signals,, in American Control Conference, (2008), 2142. Google Scholar 
[2] 
I. Andjelkovic and S. L. Campbell, Direct optimization determination of auxiliary test signals for linear problems with model uncertainty,, in 50th IEEE CDCECC, (2011), 909. Google Scholar 
[3] 
R. E. Bellman, Dynamic Programming,, Princeton University Press, (1957). Google Scholar 
[4] 
G. Besançon, I. RubioScola and D. Georges, Input selection in observer design for nonuniformly observable systems,, in 9th IFAC Symposium on Nonlinear Control Systems, (2013). Google Scholar 
[5] 
K. Brenan, S. L. Campbell and L. R. Petzold, Numerical Solution of Initial Value Problems in DifferentialAlgebraic Equations,, SIAM, (1996). Google Scholar 
[6] 
A. E. Bryson and Y. C. Ho, Applied Optimal Control,, Hemisphere, (1975). Google Scholar 
[7] 
S. L. Campbell and R. Nikoukhah, Auxiliary Signal Design for Failure Detection,, Princeton University Press, (2004). Google Scholar 
[8] 
S. L. Campbell, Least squares completions for nonlinear differential algebraic equations,, Numerical Mathematics, 65 (1993), 77. doi: 10.1007/BF01385741. Google Scholar 
[9] 
D. Choe, S. L. Campbell and R. Nikoukhah, A comparison of optimal and suboptimal auxiliary signal design approaches,, in IEEE Conference on Control Applications, (2005). Google Scholar 
[10] 
D. Garg, M. A. Patterson, W. W. Hager, A. V. Rao, D. A. Benson and G. T. Huntington, A unified framework for the numerical solution of optimal control problems using pseudospectral methods,, Automatica, 46 (2010), 1843. doi: 10.1016/j.automatica.2010.06.048. Google Scholar 
[11] 
D. Garg, W. W. Hager and A. V. Rao, Pseudospectral methods for solving infinitehorizon optimal control problems,, Automatica, 47 (2011), 829. doi: 10.1016/j.automatica.2011.01.085. Google Scholar 
[12] 
D. Garg, M. A. Patterson, C. L. Darby, C. Francolin, G. T. Huntington, W. W. Hager and A. V. Rao, Direct trajectory optimization and costate estimation of finitehorizon and infinitehorizon optimal control problems via a radau pseudospectral method,, Computational Optimization and Applications, 49 (2011), 335. doi: 10.1007/s1058900992910. Google Scholar 
[13] 
M. Gerdin, T. Glad and L. Ljung, Parameter estimation in linear differentialalgebraic equations,, in 13th IFAC Symposium on System Identification, (2003). Google Scholar 
[14] 
M. Gerdts, Parameter identification in higher DAE systems,, Technical Report, (2005). Google Scholar 
[15] 
R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance,, Springer, (2006). Google Scholar 
[16] 
R. Kircheis and S. Körkel, Parameter estimation for DAE models in a multiple experiment context,, 82nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, 11 (2011), 715. Google Scholar 
[17] 
H. H. Niemann, Active fault diagnosis in closedloop uncertain systems,, in 6th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, (2006), 587. Google Scholar 
[18] 
H. H. Niemann, A setup for active fault diagnosis,, IEEE Transactions on Automatic Control, 51 (2006), 1572. doi: 10.1109/TAC.2006.878724. Google Scholar 
[19] 
M. A. Patterson and A. V. Rao, Exploiting sparsity in direct collocation pseudospectral methods for solving continuoustime optimal control problems,, Journal of Spacecraft and Rockets, 49 (2012), 364. Google Scholar 
[20] 
R. J. Patton, P. M. Frank and R. N. Clark, Issues of Fault Diagnosis for Dynamic Systems,, Springer, (2000). Google Scholar 
[21] 
N. K. Poulsen and H. H. Niemann, Active fault diagnosisa stochastic approach,, in 7th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, (2009). Google Scholar 
[22] 
I. Okay, S. L. Campbell and P. Kunkel, Completions of implicitly defined time varying vector fields,, Linear Algebra and its Applications, 431 (2009), 1422. doi: 10.1016/j.laa.2009.05.006. Google Scholar 
[23] 
I. RubioScola, G. Besançon and D. Georges, Online observability optimization for state affine systems with output injection and observer design,, in 21st IEEE Mediterranean Conference on Control and Automation, (2013). Google Scholar 
[24] 
I. RubioScola, G. Besançon and D. Georges, Input optimization for observability of state affine systems,, in 5th IFAC Symposium on System Structure and Control, (2013). Google Scholar 
show all references
References:
[1] 
I. Andjelkovic, K. A. Sweetingham and S. L. Campbell, Active fault detection in nonlinear systems using auxiliary signals,, in American Control Conference, (2008), 2142. Google Scholar 
[2] 
I. Andjelkovic and S. L. Campbell, Direct optimization determination of auxiliary test signals for linear problems with model uncertainty,, in 50th IEEE CDCECC, (2011), 909. Google Scholar 
[3] 
R. E. Bellman, Dynamic Programming,, Princeton University Press, (1957). Google Scholar 
[4] 
G. Besançon, I. RubioScola and D. Georges, Input selection in observer design for nonuniformly observable systems,, in 9th IFAC Symposium on Nonlinear Control Systems, (2013). Google Scholar 
[5] 
K. Brenan, S. L. Campbell and L. R. Petzold, Numerical Solution of Initial Value Problems in DifferentialAlgebraic Equations,, SIAM, (1996). Google Scholar 
[6] 
A. E. Bryson and Y. C. Ho, Applied Optimal Control,, Hemisphere, (1975). Google Scholar 
[7] 
S. L. Campbell and R. Nikoukhah, Auxiliary Signal Design for Failure Detection,, Princeton University Press, (2004). Google Scholar 
[8] 
S. L. Campbell, Least squares completions for nonlinear differential algebraic equations,, Numerical Mathematics, 65 (1993), 77. doi: 10.1007/BF01385741. Google Scholar 
[9] 
D. Choe, S. L. Campbell and R. Nikoukhah, A comparison of optimal and suboptimal auxiliary signal design approaches,, in IEEE Conference on Control Applications, (2005). Google Scholar 
[10] 
D. Garg, M. A. Patterson, W. W. Hager, A. V. Rao, D. A. Benson and G. T. Huntington, A unified framework for the numerical solution of optimal control problems using pseudospectral methods,, Automatica, 46 (2010), 1843. doi: 10.1016/j.automatica.2010.06.048. Google Scholar 
[11] 
D. Garg, W. W. Hager and A. V. Rao, Pseudospectral methods for solving infinitehorizon optimal control problems,, Automatica, 47 (2011), 829. doi: 10.1016/j.automatica.2011.01.085. Google Scholar 
[12] 
D. Garg, M. A. Patterson, C. L. Darby, C. Francolin, G. T. Huntington, W. W. Hager and A. V. Rao, Direct trajectory optimization and costate estimation of finitehorizon and infinitehorizon optimal control problems via a radau pseudospectral method,, Computational Optimization and Applications, 49 (2011), 335. doi: 10.1007/s1058900992910. Google Scholar 
[13] 
M. Gerdin, T. Glad and L. Ljung, Parameter estimation in linear differentialalgebraic equations,, in 13th IFAC Symposium on System Identification, (2003). Google Scholar 
[14] 
M. Gerdts, Parameter identification in higher DAE systems,, Technical Report, (2005). Google Scholar 
[15] 
R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance,, Springer, (2006). Google Scholar 
[16] 
R. Kircheis and S. Körkel, Parameter estimation for DAE models in a multiple experiment context,, 82nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, 11 (2011), 715. Google Scholar 
[17] 
H. H. Niemann, Active fault diagnosis in closedloop uncertain systems,, in 6th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, (2006), 587. Google Scholar 
[18] 
H. H. Niemann, A setup for active fault diagnosis,, IEEE Transactions on Automatic Control, 51 (2006), 1572. doi: 10.1109/TAC.2006.878724. Google Scholar 
[19] 
M. A. Patterson and A. V. Rao, Exploiting sparsity in direct collocation pseudospectral methods for solving continuoustime optimal control problems,, Journal of Spacecraft and Rockets, 49 (2012), 364. Google Scholar 
[20] 
R. J. Patton, P. M. Frank and R. N. Clark, Issues of Fault Diagnosis for Dynamic Systems,, Springer, (2000). Google Scholar 
[21] 
N. K. Poulsen and H. H. Niemann, Active fault diagnosisa stochastic approach,, in 7th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, (2009). Google Scholar 
[22] 
I. Okay, S. L. Campbell and P. Kunkel, Completions of implicitly defined time varying vector fields,, Linear Algebra and its Applications, 431 (2009), 1422. doi: 10.1016/j.laa.2009.05.006. Google Scholar 
[23] 
I. RubioScola, G. Besançon and D. Georges, Online observability optimization for state affine systems with output injection and observer design,, in 21st IEEE Mediterranean Conference on Control and Automation, (2013). Google Scholar 
[24] 
I. RubioScola, G. Besançon and D. Georges, Input optimization for observability of state affine systems,, in 5th IFAC Symposium on System Structure and Control, (2013). Google Scholar 
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