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Published online by Cambridge University Press: 28 April 2022
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- Data-Driven Identification of Networks of Dynamic Systems , pp. 257 - 265Publisher: Cambridge University PressPrint publication year: 2022
References
Baldi, P. and Hornik, K. Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 2(1):53–58, 1989.Google Scholar
Bamieh, B. and Filo, M. An input-output approach to structured stochastic uncertainty. Technical Report arXiv:1806.07473v1, University of California, Santa Barbara, 2018.Google Scholar
Bamieh, B. and Voulgaris, P. G. A convex characterization of distributed control problems in spatially invariant systems with communication constraints. Systems & Control Letters, 54(6):575–583, 2005.CrossRefGoogle Scholar
Bellman, R. and Astrom, K. J. On structural identifiability. Mathematical Biosciences, 7(3):329–339, 1970.Google Scholar
Beylkin, G. and Mohlenkamp, M. Algorithms for numerical analysis in high dimensions. SIAM Journal on Scientific Computing, 26(6):2133–2159, 2005.CrossRefGoogle Scholar
Boersma, S., Doekemeijer, B., Vali, M., Meyers, J. and van Wingerden, J.-W. A control-oriented dynamic wind farm model: WFSim. Wind Energy Science, 3(1):75–95, 2018.CrossRefGoogle Scholar
Borelli, F. and Keviczky, T. Distributed LQR design for identically dynamically decoupled systems. IEEE Transactions on Automatic Control, 53(8):1901–1912, 2008.Google Scholar
Boumal, N., Mishra, B., Absil, P. A. and Sepulchre, R. Manopt, a Matlab toolbox for optimization on manifolds. Journal of Machine Learning Research, 15(1):1455–1459, 2013.Google Scholar
Boussé, M. Explicit and Implicit Tensor Decomposition-Based Algorithms and Applications. PhD thesis, KU Leuven, 2019.Google Scholar
Bruls, J., Chou, C., Haverkamp, B. and Verhaegen, M. Linear and non-linear system identification using separable least-squares. European Journal of Control, 5(1):116–128, 1999.CrossRefGoogle Scholar
Carroll, J. D. and Chang, J.-J. Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Young decomposition. Psychometrika, 35(3):283–319, 1970.CrossRefGoogle Scholar
Castagnotto, A., Cruz Varona, M., Jeschek, L. and Lohmann, B. sss & sssMOR: Analysis and reduction of large-scale dynamic systems in Matlab. at-Automatisierungstechnik, 65(2):134–150, February 2017.CrossRefGoogle Scholar
Chen, T., Ohlsson, H. and Ljung, L. On the estimation of transfer functions, regularizations and Gaussian processes revisited. Automatica, 48(8):1525–1535, 2012.CrossRefGoogle Scholar
Chiuso, A. The role of vector autoregressive modeling in predictor-based subspace identification. Automatica, 43(6):1034–1048, 2007.CrossRefGoogle Scholar
Chiuso, A. and Pillonetto, G. A Bayesian approach to sparse dynamic network identification. Automatica, 48(8):1553–1565, 2012.CrossRefGoogle Scholar
Choudhary, S. and Mitra, U. Identifiability scaling laws in bilinear inverse problems. arXiv preprint arXiv:1402.2637, 2014.CrossRefGoogle Scholar
Correia, C., Conan, J.-M., Kulcsár, C., Raynaud, H.-F. and Petit, C. Adapting optimal LQG methods to ELT-sized AO systems. Paper presented at the first Adaptive Optics for Extremely Large Telescopes conference (1st AO4ELT), 2010.CrossRefGoogle Scholar
Cuthill, E. and McKee, J. Reducing the bandwidth of sparse symmetric matrices. In Proceedings of the 1969 24th National Conference of ACM, pp. 157–172, New York, 1969.CrossRefGoogle Scholar
Dahlhaus, R. and Eichler, M. Causality and graphical models in time series analysis. In Green, P., Hjort, N. and Richardson, S., eds. Highly Structured Stochastic Systems, pp. 115–137. Oxford University Press, Oxford, 2003.CrossRefGoogle Scholar
D’Andrea, R. Software for modeling, analysis, and control design for multidimensional systems. In Proceedings of the 1999 IEEE International Symposium on Computer Aided Control System Design (Cat. No. 99TH8404), pp. 24–27, IEEE, 1999.Google Scholar
D’Andrea, R. and Dullerud, G. Distributed control for spatially interconnected systems. IEEE Transactions on Automatic Control, 48(9):1478–1495, 2003.CrossRefGoogle Scholar
De Moor, B. Mathematical Concepts and Techniques for Modelling of Static and Dynamic Systems. PhD thesis, KU Leuven, 1988.Google Scholar
Dempster, A., Laird, N. and Rubin, D. Maximum Likelihood from Incomplete Data via EM Algorithm. Prentice Hall, Englewood Cliffs, NJ, 1977.CrossRefGoogle Scholar
de Silva, V. and Lim, L. Tensor rank and the ill-posedness of the best low-rank approximation problem. SIAM Journal on Matrix Analysis and Applications, 30(3):1084–1127, 2008.CrossRefGoogle Scholar
Dewilde, P. and van der Veen, A. Time-Varying Systems and Computations. Kluwer Academic Publisher, Norwell, MA, 1998.CrossRefGoogle Scholar
Ding, F. and Chen, T. Iterative least-squares solutions of coupled Sylvester matrix equations. Systems & Control Letters, 54(2):95–107, 2005.CrossRefGoogle Scholar
Doekemeijer, B. M., Boersma, S., Pao, L. Y., Knudsen, T. and van Wingerden, J.-W. Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control. Wind Energy Science, 3(2):749–765, 2018.CrossRefGoogle Scholar
Doelman, R. and Verhaegen, M. Sequential convex relaxation for convex optimization with bilinear matrix equalities. In 2016 European Control Conference (ECC), pp. 1946–1951, 2016.Google Scholar
Dullerud, G. and D’Andrea, R. Distributed control of heterogeneous systems. IEEE Transactions on Automatic Control, 49(12):2113–2128, 2004.CrossRefGoogle Scholar
Eckart, C. and Young, G. The approximation of one matrix by another of lower rank. Psychometrika, 1(3):211–218, 1936.CrossRefGoogle Scholar
Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. Least angle regression. Annals of Statistics, 2(32):407–499, 2004.Google Scholar
Ellerbroek, B. The TMT adaptive optics program. Paper presented at the second Adaptive Optics for Extremely Large Telescopes international conference (2nd AO4ELT), 2011.Google Scholar
Epperlein, J. P. and Bamieh, B. Spatially invariant embeddings of systems with boundaries. Paper presented at the American Control Conference, 2016.CrossRefGoogle Scholar
Fax, J. and Murray, R. Information flow and cooperative control of vehicle formations. IEEE Transactions on Automatic Control, 49(9):1465–1476, 2004.Google Scholar
Fazel, M., Hindi, H. and Boyd, S. P. Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices. Paper presented at the American Control Conference, 2003.Google Scholar
Fazel, M., Pong, T. K., Sun, D. and Tseng, P. Hankel matrix rank minimization with applications to system identification and realization. Siam Journal on Matrix Analysis and Applications, 34(3):946–977, 2012.CrossRefGoogle Scholar
Fraanje, R., Massioni, P. and Verhaegen, M. A decomposition approach to distributed control of dynamic deformable mirrors. International Journal of Optomechatronics, 4(3):269–284, 2010.CrossRefGoogle Scholar
Fritzson, P. Introduction to Modeling and Simulation of Technical and Physical Systems with Modelica. Wiley-IEEE, New Jersey, 2011.CrossRefGoogle Scholar
Gebraad, P. Data-driven wind plant control. PhD thesis, Delft Center for Systems and Control, Delft University of Technology, 2014.Google Scholar
Gendron, E., Morris, T., Basden, A., et al. Final two-stage MOAO on-sky demonstration with CANARY. In Marchetti, E., Close, L. M., and Véran, J.-P., eds., Adaptive Optics Systems V, vol. 9909, pp. 126–142. International Society for Optics and Photonics, SPIE, 2016.Google Scholar
Gevers, M., Bazanella, A. and Pimentel, G. Identifiability of dynamical networks with singular noise spectra. IEEE Transactions on Automatic Control, 64(6):2473–2479, 2019.CrossRefGoogle Scholar
Giraldi, L., Nouy, A. and Legrain, G. Low-rank approximate inverse for preconditioning tensor-structured linear systems. SIAM Journal on Scientific Computing, 36(4):A1850–A1870, 2014.Google Scholar
Golub, G. H. and Van Loan, C. F. Matrix Computations. The Johns Hopkins University Press, third edition, 1996.Google Scholar
Golub, G., Kolda, T., Nagy, J. and Loan, C. V. Workshop on tensor decompositions. SIAM J. Matrix Analysis and Applications, 21(2):1253–1278, 2000.Google Scholar
Gonçalves, J. and Warnick, S. Necessary and sufficient conditions for dynamical structure reconstruction of lti networks. IEEE Transactions on Automatic Control, 53(7):1670–1674, 2008.CrossRefGoogle Scholar
Gragg, W. B. and Lindquist, A. On the partial realization problem. Linear Algebra and its Applications, 50:277–319, 1983.Google Scholar
Guyon, O. Extreme adaptive optics. Annual Review of Astronomy and Astrophysics, 56(1):315–355, 2018.CrossRefGoogle Scholar
Halko, N., Martinsson, P. G. and Tropp, J. A. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53(2):217–288, 2011.CrossRefGoogle Scholar
Harshman, R. A. Foundations of the PARAFAC procedure: Models and conditions for an explanatory multimodal factor analysis. UCLA working papers in phonetics, 16:1–84, (University Microfilms, Ann Arbor, Michigan, No. 10,085), 1970.Google Scholar
Harshman, R. A. The problem and nature of degenerate solutions or decompositions of 3-way arrays. Paper presented at the American Institute of Mathematics Tensor Decomposition Workshop, Palo Alto, CA, 2004.Google Scholar
Håstad, J. Tensor rank is NP-complete. Journal of Algorithms, 11(4):644–654, 1990.CrossRefGoogle Scholar
Henrion, D. and Šebek, M. Polynomial and matrix fraction description. 2000. Available at https://homepages.laas.fr/henrion/Papers/6-43-13-5.pdf.Google Scholar
Hinnen, K., Verhaegen, M. and Doelman, N. Exploiting the spatiotemporal correlation in adaptive optics using data-driven H2-optimal control. Journal of the Optical Society of America A, 24(6):1714–1725, 2007.Google Scholar
Hinnen, K., Verhaegen, M. and Doelman, N. A data-driven H2-optimal control approach for adaptive optics. IEEE Transactions on Control Systems Technology, 16(3):381–395, 2008.Google Scholar
Hippe, P. and Deutscher, J. Design of Observer-based Compensators: From the Time to the Frequency Domain. Springer-Verlag, London, 2009.CrossRefGoogle Scholar
Hitchcock, F. L. The expression of a tensor or a polyadic as a sum of products. Journal of Mathematics and Physics, 6(1–4):164–189, 1927.Google Scholar
Ho, B. and Kalman, R. Effective construction of linear state-variable models from input/output data. Regelungstechnik, 14:545–548, 1966.Google Scholar
Hoff, P. D. Multilinear tensor regression for longitudinal relational data. Annals of Applied Statistics, 9(3):1169–1193, 2015.CrossRefGoogle ScholarPubMed
Houtzager, I., van Wingerden, J. and Verhaegen, M. VARMAX-based closed-loop subspace model identification. In Proceedings of the 48th IEEE Conference on Decision and Control, 2009a.Google Scholar
Houtzager, I., van Wingerden, J.-W. and Verhaegen, M. Fast-array recursive closed-loop subspace model identification. In 15th IFAC Symposium on System Identification, volume 42, pp. 96–101, 2009b.Google Scholar
Inigo, G. Estimation and control of noise amplifier flows using data-based approaches. PhD thesis, Fluids mechanics [physics.class-ph], Ecole Polytechnique, France, 2015.Google Scholar
Jansson, M. Subspace identification and ARX modeling, IFAC Proceedings Volumes, 36(16), 1585–1590, 2003, https://doi.org/10.1016/S1474-6670(17)34986-8.Google Scholar
Jovanović, M. and Bamieh, B. Lyapunov-based distributed control of systems on lattices. IEEE Transactions on Automatic Control, 50(4):422–433, 2005.Google Scholar
Juang, J., Chen, C. and Phan, M. Estimation of Kalman filter gain from output residuals. Journal of Guidance, Control, and Dynamics, 16(5):903–6908, 1993.CrossRefGoogle Scholar
Kailath, T. Linear Systems. Prentice-Hall Information and System Sciences Series. Prentice Hall, 1980.Google Scholar
Kailath, T., Sayed, A. and Hassibi, B. Linear Estimation. Prentice Hall, New Jersey, 2000.Google Scholar
Kibangou, A. Y., Garin, F. and Gracy, S. Input and state observability of network systems with a single unknown input. IFAC-PapersOnLine, 49(22):37–42, 2016. Paper presented at the 6th IFAC Workshop on Distributed Estimation and Control in Networked Systems NECSYS 2016. www.sciencedirect.com/science/article/pii/S2405896316319565.CrossRefGoogle Scholar
Kim, J. and Bewley, T. R. A linear systems approach to flow control. Annual Review of Fluid Mechanics, 39(1):383–417, 2007.Google Scholar
Knudsen, T. Consistency analysis of subspace identification methods based on a linear regression approach. Automatica, 37(1):81–89, 2001.CrossRefGoogle Scholar
Kolda, T. Multilinear operators for higher-order decompositions. Technical Report SAND2006-2081, Sandia National Laboratories, 2006.CrossRefGoogle Scholar
Kolda, T. and Bader, B. Tensor decompositions and applications. SIAM Review, 51(3):455–500, 2009.Google Scholar
Kolmogorov, A. The local structure of turbulence in incompressible viscous fluid for very large Reynolds’ numbers. Akademiia Nauk SSSR Doklady, 30:301–305, 1941.Google Scholar
Kulcsár, C., Raynaud, H.-F., Petit, C. and Conan, J.-M. Minimum variance prediction and control for adaptive optics. Automatica, 48(9):1939–1954, 2012.CrossRefGoogle Scholar
Langbort, C., Chandra, R. and D’Andrea, R. Distributed control design for systems interconnected over an arbitrary graph. IEEE Transactions on Automatic Control, 49(9):1502–1519, 2004.Google Scholar
Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C. and Ghahramani, Z. Kronecker graphs: An approach to modeling networks. Journal of Machine Learning Research, 11:985–1042, 2010.Google Scholar
Li, N., Kindermann, S. and Navasca, C. Some convergence results on the regularized alternating least-squares method for tensor decomposition. Linear Algebra and its Applications, 438(2):796–812, 2013.Google Scholar
Li, G., Wen, C. and Zhang, A. Fixed point iteration in identifying bilinear models. Systems & Control Letters, 83:28–37, 2015.Google Scholar
Lipp, T. and Boyd, S. Variations and extension of the convex cconcave procedure. Optimizationa & Engineering, 17(2):263–287, 2016.Google Scholar
Liu, Q. Modeling, Distributed Identification and Control of Spatially-Distributed Systems with Application to an Actuated Beam. PhD thesis, Institute of Control Systems, Hamburg University of Technology, 2015.Google Scholar
Liu, Z., Hansson, A. and Vandenberghe, L. Nuclear norm system identification with missing inputs and outputs. Systems and Control Letters, 62(8):605–612, 2013.CrossRefGoogle Scholar
Ljung, L. System Identification – Theory for the User. Prentice Hall, Upper Saddle River, NJ, 2nd edition, 1999.Google Scholar
Ljung, L. and Parrilo, P. Initialization of physical parameter estimates. Paper presented at the 13th IFAC Symposium on System Identification SYSID, 2003.CrossRefGoogle Scholar
Ljung, L., Singh, R. and Chen, T. Regularization features in the system identification tool-box. IFAC-PapersOnLine, 48(28):745–750, 2015. www.sciencedirect.com/science/article/pii/S2405896315028438CrossRefGoogle Scholar
Loan, C. V. and Pitsianis, N. Approximation with Kronecker products. In Moonen, M. and Golub, G., eds., Linear Algebra for Large Scale and Real Time Applications, pp. 293–314. Kluwer Publications, Dordrecht, 1992.Google Scholar
Massioni, P. Distributed control α-heterogeneous dynamically coupled systems. Systems and Control Letters, 72:30–35, 2014.CrossRefGoogle Scholar
Massioni, P. Decomposition Methods for Distributed Control and Identification. PhD thesis, Delft Center for Systems and Control, Delft University of Technology, 2015.Google Scholar
Massioni, P. and Verhaegen, M. Distributed control for identical dynamically coupled systems: A decomposition approach. IEEE Transactions on Automatic Control, 54(1):124–135, 2009.CrossRefGoogle Scholar
Massioni, P., Kulcsár, C., Raynaud, H.-F. and Conan, J.-M. Fast computation of an optimal controller for large-scale adaptive optics. Journal of the Optical Society of America A, 28(11):2298–2309, 2011.CrossRefGoogle ScholarPubMed
Massioni, P., Raynaud, H.-F., Kulcsár, C. and Conan, J.-M. An approximation of the Riccati equation in large-scale systems with application to adaptive optics. IEEE Transactions on Control Systems Technology, 2(23):479–487, 2015.CrossRefGoogle Scholar
Mercère, G., Markovsky, I. and Ramos, J. A. Innovation-based subspace identification in open- and closed-loop. Paper presented at the 2016 IEEE 55th Conference on Decision and Control, 2016.CrossRefGoogle Scholar
Mohan, K. and Fazel, M. Reweighted nuclear norm minimization with application to system identification. In Proceedings of the 2010 American Control Conference, pp. 2953–2959. IEEE, 2010.Google Scholar
Mohlenkamp, M. J. Musings on multilinear fitting. Linear Algebra and its Applications, 438(2):834–852, 2013.Google Scholar
Moonen, M., Moor, B. D., Vandenberghe, L. and Vandewalle, J. On- and off-line identification of linear state-space models. International Journal of Control, 49(1):219–232, 1989.CrossRefGoogle Scholar
Motee, N. and Jadbabaie, J. Optimal control of spatially distributed systems. IEEE Transactions on Automatic Control, 53(7):1616–1629, 2008.CrossRefGoogle Scholar
Neichel, B., Fusco, T., Sauvage, J.-F., et al. The adaptive optics modes for HARMONI: from classical to laser assisted tomographic AO. In Proceedings of SPIE 9909, Adaptive Optics Systems V, 990909 (26 July 2016); https://doi.org/10.1117/12.2231681Google Scholar
Nocedal, J. and Wright, S. J. Numerical Optimization, 2nd ed. Springer, New York, 2nd edition, 2006.Google Scholar
Noll, R. J. Zernike polynomials and atmospheric turbulence*. Journal of the Optical Society of America, 66(3):207–211, 1976.CrossRefGoogle Scholar
Ogata, K. Modern Control Engineering 4th ed. Prentice Hall, New Jersey, 4th edition, 2002.Google Scholar
Osborn, J. Characterising atmospheric turbulence using scidar techniques. In Imaging and Applied Optics 2018, p. JW5I.5. Optical Society of America, 2018.Google Scholar
Overschee, P. V. and Moor, B. D. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30(1):75–93, 1994.Google Scholar
Overton, M. L. and Womersley, R. S. Optimality conditions and duality theory for minimizing sums of the largest eigenvalues of symmetric matrices. Mathematical Programming, 62(1–3):321–357, 1993.CrossRefGoogle Scholar
Peternell, K., Scherrer, W. and Deistler, M. Statistical analysis of novel subspace identification methods. Signal Processing, 52(2):161–177, 1996.CrossRefGoogle Scholar
Petit, C., Conan, J.-M., Kulcsár, C., Raynaud, H.-F. and Fusco, T. First laboratory validation of vibration filtering with LQG control law for adaptive optics. Optics Express, 16(1):87–97, 2008.CrossRefGoogle ScholarPubMed
Pillonetto, G., Dinuzzo, F., Chen, T., Nicolao, G. D. and Ljung, L. Kernel methods in system identification, machine learning and function estimation: A survey. Automatica, 50(3):657–682, 2014.Google Scholar
Pircher, M. and Zawadzki, R. J. Review of adaptive optics OCT (AO-OCT): Principles and applications for retinal imaging. Biomedical Optics Express, 8(5):2536–2562, 2017.Google Scholar
Piscaer, P. Sparse VARX Model Identification for Large-Scale Adaptive Optics. MSc thesis, Delft Center for Systems and Control, Delft University of Technology, 2016.Google Scholar
Poyneer, L. A., Macintosh, B. A. and Véran, J.-P. Fourier transform wavefront control with adaptive prediction of the atmosphere. Journal of the Optical Society of America A, 24(9):2645–2660, 2007.CrossRefGoogle ScholarPubMed
Poyneer, L. A., Palmer, D. W., Macintosh, B., et al. Performance of the Gemini planet imager adaptive optics system. Applied Optics, 55(2):323–340, 2016.CrossRefGoogle ScholarPubMed
Pozzi, P., Quintavalla, M., Wong, A. B., Borst, J. G. G., Bonora, S. and Verhaegen, M. Plug- and-play adaptive optics for commercial laser scanning fluorescence microscopes based on an adaptive lens. Optics Letters, 45(13):3585–3588, 2020. doi: 10.1364/OL.396998.Google Scholar
Qi, L. and Womersley, R. S. On extreme singular values of matrix valued functions. Journal of Convex Analysis, 3:153–166, 1996.Google Scholar
Qiu, Y. Multilevel sequential separable (MSSS) matrix computation toolbox version 0.7. http://ta.twi.tudelft.nl/nw/users/yueqiu/software.html, 2013.Google Scholar
Qiu, Y. Preconditioning Optimal Flow Control Problems Using Multilevel Sequentially Semiseparable Matrix Computations. PhD thesis, Delft Center for Systems and Control, Delft University of Technology, 2015.Google Scholar
Recht, B. and D’Andrea, R. Distributed control of systems over discrete groups. IEEE Transactions on Automatic Control, 49(9):1270–1283, 2009.Google Scholar
Recht, B., Fazel, M. and Parrilo, P. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Review, 52(3):471–501, 2010.CrossRefGoogle Scholar
Rice, J. and Verhaegen, M. Distributed control: A sequentially semi-separable approach for spatially heterogeneous linear systems. IEEE Transactions on Automatic Control, 54(6):1270–1283, 2009.Google Scholar
Rice, J. and Verhaegen, M. Efficient system identification of heterogeneous distributed systems via a structure exploiting extended Kalman filter. IEEE Transactions on Automatic Control, 56(7):1713–1718, 2011.Google Scholar
Roesser, R. A discrete state-space model for linear image processing. IEEE Transactions on Automatic Control, 20(1):1–10, 1975.Google Scholar
Rojo, O. A new algebra of Toeplitz-plus-Hankel matrices and applications. Computers & Mathematics with Applications, 55(12):2856–2869, 2008.CrossRefGoogle Scholar
Roux, B. L., Conan, J.-M., Kulcsár, C., Raynaud, H.-F., Mugnier, L. M. and Fusco, T. Optimal control law for classical and multiconjugate adaptive optics. Journal of the Optical Society of America A, 21(7):1261–1276, 2004.Google Scholar
Rudin, W. Real and Complex Analysis. McGraw-Hill International Editions, New York, 1986.Google Scholar
Sayed, A. H. and Kailath, T. A state-space approach to adaptive RLS filtering. IEEE Signal Processing Magazine, 11(3):18–60, 1994.CrossRefGoogle Scholar
Scobee, D., Ratliff, L., Dong, R., Ohlsson, H., Verhaegen, M. and Sastry, S. S. Nuclear norm minimization for blind subspace identification (N2BSID). In Decision and Control (CDC), 2015 IEEE 54th Annual Conference on, pp. 2127–2132. IEEE, 2015.Google Scholar
Shamma, J. and Arslan, G. A decomposition approach to distributed control of systems on lattices. IEEE Transactions on Automatic Control, 51(4):701–707, 2006.CrossRefGoogle Scholar
Shen, X., Diamond, S., Udell, M., Gu, Y. and Boyd, S. Disciplined multi-convex programming. Technical Report arXiv:1609.03285, Stanford University, 2016.Google Scholar
Sinquin, B. Structured Matrices for Predictive Control of Large and Multi-dimensional Systems. PhD thesis, Delft University of Technology, 2019.Google Scholar
Sinquin, B. and Verhaegen, M. A subspace like identification method for large-scale LTI dynamical systems. Paper presented at the 2017 Signal Processing Symposium (SPSympo), 2017.CrossRefGoogle Scholar
Sinquin, B. and Verhaegen, M. Tensor-based predictive control for extremely large-scale single conjugate adaptive optics. Journal of Optical Society of America A, 36(9):1612–1626, 2018.CrossRefGoogle Scholar
Sinquin, B. and Verhaegen, M. K4SID: Large-scale subspace identification with Kronecker modeling. IEEE Transactions on Automatic Control, 64(3):960–975, 2019a.CrossRefGoogle Scholar
Sinquin, B. and Verhaegen, M. QUARKS: Identification of large-scale Kronecker vector- autoregressive models. IEEE Transactions on Automatic Control, 64(2):448–463, 2019b.Google Scholar
Sinquin, B., Varnai, P., Monchen, G. and Verhaegen, M. Tensor toolbox for identifyin multi-dimensional systems. Available at https://bitbucket.org/csi-dcsc/t4sid, 2018.Google Scholar
Sinquin, B., Prengère, L., Kulcsár, C., et al. On-sky results for adaptive optics control with data- driven models on low-order modes. Submitted to Monthly Notices of Royal Astronomical Society, 2020.Google Scholar
Sivo, G., Kulcsár, C., Conan, J.-M., et al. First on-sky SCAO validation of full LQG control with vibration mitigation on the CANARY pathfinder. Optical Express, 22(19):23565–23591, 2014.CrossRefGoogle ScholarPubMed
Songsiri, J. and Vandenberghe, L. Topology selection in graphical models of autoregressive processes. Journal of Machine Learning Research, 11:2010, 2010.Google Scholar
Southwell, R. Relaxation Methods in Theoretical Physics. Oxford University Press, Oxford, 1946.Google Scholar
Strang, G. Fast transforms: Banded matrices with banded inverses. Proceedings of the National Academy of Sciences of the United States of America, 107(28):12413–12416, 2009.Google Scholar
Sundaram, S. Fault-tolerant and secure control systems. University of Waterloo, Lecture Notes, 2012.Google Scholar
Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1):267–288, 1996.CrossRefGoogle Scholar
Timoshenko, S. and Woinowsky-Krieger, S. Theory of Plates and Shells. McGraw-Hill, New York, 1959.Google Scholar
Tsiligkaridis, T. and Hero, A. O. Covariance estimation in high dimensions via Kronecker product expansions. IEEE Transactions on Signal Processing, 61(21):5347–5360, 2013.Google Scholar
Udell, M., Horn, C., Zadeh, R. and Boyd, S. Generalized low-rank models. Foundations and Trends in Machine Learning, 9:1–118, 2016.Google Scholar
van den Hof, J. Structural identifiability of linear compartemental systems. IEEE Transactions on Automatic Control, 43(6):800–818, 2002.CrossRefGoogle Scholar
Van Overschee, P. and De Moor, B. N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica, 30(1):75–93, 1994.CrossRefGoogle Scholar
Van Overschee, P. and De Moor, B. Identification for Linear Systems: Theory - Implementation - Applications. Springer-Verlag, 2011.Google Scholar
Varnai, P. Exploiting Kronecker Structures, with Applications to Optimization Problems Arising in the Field of Adaptive Optics. MSc thesis, Delft Center for Systems and Control, Delft University of Technology, 2017.Google Scholar
Verhaegen, M. Subspace techniques in system identification. In Baillieul, J. and Samad, T., eds., Encyclopedia of Systems and Control. Springer-Verlag, London, 2015.Google Scholar
Verhaegen, M. The identification of network connected systems. In Baillieul, J. and Samad, T., eds., Encyclopedia of Systems and Control, pp. 1–11. Springer London, London, 2019. doi: 10.1007/978-1-4471-5102-9_100088-1.Google Scholar
Verhaegen, M. and Hansson, A. N2SID: Nuclear norm subspace identification of innovation models. Automatica, 72(5):57–63, 2016.Google Scholar
Verhaegen, M. and Verdult, V. Filtering and System Identification: A Least Squares Approach. Cambridge University Press, Cambridge, 1st edition, 2007.CrossRefGoogle Scholar
Vervliet, N., Debals, O., Sorber, L., Barel, M. V. and Lathauwer, L. D. Tensorlab 3.0. Technical report, KU Leuven, 2016. Available at www.esat.kuleuven.be/stadius/tensorlab/.Google Scholar
Wan, Y., Keviczky, T., Verhaegen, M. and Gustafsson, F. Data-driven robust receding horizon fault estimation. Automatica, 71:210–221, 2016.Google Scholar
Weerts, H. Identifiability and Identification Methods for Dynamic Networks. PhD thesis, Eindhoven: Technische Universiteit Eindhoven, 2018.Google Scholar
Wen, F., Chu, L., Liu, P. and Qiu, R. C. A survey on nonconvex regularization-based sparse and low-rank recovery in signal processing, statistics, and machine learning. IEEE Access, 6:69883–69906, 2018.Google Scholar
Wills, A., Yu, C., Ljung, L. and Verhaegen, M. Affinely parametrized state-space models: Ways to maximize the likelihood function. Paper presented at the 18th IFAC Symposium on System Identification (SYSID 2018), 2018.CrossRefGoogle Scholar
Woodbury, N. Representation and Reconstruction of Linear Time-Invariant Networks. PhD thesis, Brigham Young University, 2019.Google Scholar
Xu, Y. and Yin, W. A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on Imaging Sciences, 6(3):1758–1789, 2013.Google Scholar
Yeung, E., Gonçalves, J., Sandberg, H. and Warnick, S. Representing structure in linear interconnected dynamical systems. Paper presented at the 49th IEEE Conference on Decision and Control (CDC), 2010.CrossRefGoogle Scholar
Yu, C. and Verhaegen, M. Local subspace identification of distributed homogeneous systems with general interconnection patterns. Paper presented at the 17th IFAC Symposium on System Identification (SYSID 2015), 2015.Google Scholar
Yu, C. and Verhaegen, M. Blind multivariable ARMA subspace identification. Automatica, 66:3–14, 2016.Google Scholar
Yu, C. and Verhaegen, M. Subspace identification of distributed clusters of homogeneous systems. IEEE Transactions on Automatic Control, 62(1):463–468, 2017.CrossRefGoogle Scholar
Yu, C. and Verhaegen, M.Subspace identification of individual systems operating in a network (si2on). IEEE Transactions on Automatic Control, 63(4):1120–1125, 2018.Google Scholar
Yu, C., Chen, J. and Verhaegen, M. Subspace identification of individual systems in a large-scale heterogeneous network. Automatica, 109:108517, 2019.CrossRefGoogle Scholar
Yu, C., Ljung, L. and Verhaegen, M. Identification of structured state-space models. Automatica, 90:54–61, 2018a.CrossRefGoogle Scholar
Yu, C., Verhaegen, M. and Hansson, A. Subspace identification of local systems in one-dimensional homogeneous networks. IEEE Transactions on Automatic Control, 63(4):1126–1131, 2018b.Google Scholar
Yu, C., Chen, J., Ljung, L. and Verhaegen, M. Subspace identification of continuous-time models using generalized orthodnormal bases. Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), pp. 5280–5285, 2017, doi: 10.1109/CDC.2017.8264440.2017.Google Scholar
Yu, C., Ljung, L., Wills, A. and Verhaegen, M. Constrained subspace method for the identification of structured state-space models (COSMOS). IEEE Transactions on Automatic Control, 65(10):4201–4214, 2020. doi: 10.1109/TAC.2019.2957703.CrossRefGoogle Scholar
Yue, Z., Thunberg, J., Ljung, L. and Gonçalves, J. A state-space approach to sparse dynamic network reconstruction. Technical Report arXiv:1811.08677v1, Université du Luxembourg, 2018.Google Scholar