Systems Theory and Automatic Control

Publications

Books and Volumes

[15] A. Savchenko. Efficient Set-based Process Monitoring and Fault Diagnosis. Number 7 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, 2017.
[14] J.P. Zometa. Code generation for model predictive control of embedded systems. Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2017.
[13] K.J. Kazim. Towards a unified approach for path-following and force-feedback using nonlinear model predictive control. Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2016.
[12] L. Carius. Control and Model-Based Analysis of Microaerobic Processes using Rhodospirillum rubrum as Model Organism. Number 6 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, 2016.
[11] B. Huang, R. Findeisen, and B. Guay, M. Gopaluni. 9th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), volume 48. IFAC-PapersOnLine, Whistler, Canada, June 2015.
[10] P. Rumschinski. Verification of system properties of polynomial systems using discrete-time approximations and set-based analysis. Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2015.
[9] P. Varutti. Model Predictive Control for Nonlinear Networked Control Systems, A Model-based Compensation Approach for Nondeterministic Communication Networks. Number 5 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, June 2014.
[8] P. Benner, R. Findeisen, D. Flockerzi, U. Reichl, and K. Sundmacher. Large-Scale Networks in Engineering and Life Sciences. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, 2014.
[7] S. Borchers. Set-membership Estimation, Analysis, and Design of Experiments for Biological Processes. Number 4 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, August 2013.
[6] T. Faulwasser. Optimization-Based Solutions to Constrained Trajectory-Tracking and Path-Following Problems. Number 3 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, February 2013.
[5] L. Grüne, F. Allgöwer, R. Findeisen, J. Fischer, D. Groß, U.D. Hanebeck, M. Müller, J. Pannek, M. Reble, O. Stursberg, P. Varutti, K. Worthmann, and B. Kern. Distributed and Networked Model Predictive Control in Control Theory of Digitally Networked Systems. Springer-Verlag, 2013.
[4] S. Maldonado. Force-induced Bone Adaptation: A Systems Biology Perspective Towards Therapy Design. Number 2 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, February 2012.
[3] S. Streif. Understanding Phototaxis of Halobacterium salinarum: A Systems Biology Approach. Number 1 in Contributions in Systems Theory and Automatic Control, Otto-von-Guericke Universität Magdeburg. Shaker Verlag, May 2011. [ DOI ]
[2] R. Findeisen, L.B. Biegler, and F. Allgöwer, editors. Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences. Springer-Verlag, Berlin, 2008.
[1] R. Findeisen. Nonlinear Model Predictive Control: A Sampled-Data Feedback Perspective. Fortschr.-Ber. VDI Reihe 8 Nr. 1087, VDI Verlag, Düsseldorf, 2006.

Journal Articles and Book Chapters (all peer reviewed)

[88] T. Faulwasser, T. Weber, P. Zometa, and R. Findeisen. Implementation of nonlinear model predictive path-following control for an industrial robot. IEEE Transactions on Control Systems Technology, 25(4):1505--1511, 2017.
[87] A. Šatrauskienė, R. Navickas, A. Laucevičius, and H. Huber. Identifying differential mir and gene consensus patterns in peripheral blood of patients with cardiovascular diseases from literature data. BMC cardiovascular disorders, 17(1):173, 2017.
[86] D. Veltman, T. Laeremans, E. Passante, and H. Huber. Signal transduction analysis of the nlrp3-inflammasome pathway after cellular damage and its paracrine regulation. Journal of theoretical biology, 415:125--136, 2017.
[85] C. Kankeu, K.e Clarke, E. Passante, and H. Huber. Doxorubicin-induced chronic dilated cardiomyopathy—the apoptosis hypothesis revisited. Journal of Molecular Medicine, pages 1--10, 2017.
[84] M. Kishida, M. Kögel, and R. Findeisen. Combined event- and self-triggered control approach with guaranteed finite-gain L2 stability for uncertain linear systems. IET Control Theory Applications, 11(11):1674--1683, 2017.
[83] J. Matschek, E. Bullinger, F. von Haeseler, M. Skalej, and R. Findeisen. Mathematical 3D modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. Mathematical Biosciences, 284:51--60, 2017. [ DOI ]
[82] N. Rudolph, S. Streif, and R. Findeisen. Set-based experiment design for model discrimination using bilevel optimization. IFAC-PapersOnLine, 49(26):295--299, 2016.
[81] S. Koulchitsky, C. Delairesse, T. Beeken, A. Monteforte, J. Dethier, E. Quertemont, R. Findeisen, E. Bullinger, and V. Seutin. Activation of D2 autoreceptors alters cocaine-induced locomotion and slows down local field oscillations in the rat ventral tegmental area. Neuropharmacology, 108:120--127, 2016. [ DOI ]
[80] S. Lucia, M. Kögel, P. Zometa, D.E. Quevedo, and R. Findeisen. Predictive control, embedded cyberphysical systems and systems of systems- a perspective. Annual Reviews in Control, pages 193--207, 2016. [ DOI ]
[79] M. Schliemann-Bullinger, D. Fey, T. Bastogne, R. Findeisen, P. Scheurich, and E. Bullinger. The experimental side of parameter estimation. In L. Geris and D. Gomez-Cabrero, editors, Uncertainty in Biology - A Computational Modeling Approach, volume 17 of Studies in Mechanobiology, Tissue Engineering and Biomaterials. Springer, 1 edition, 2016. [ DOI ]
[78] S. Streif, K. Kim, P. Rumschinski, M. Kishida, D.E. Shen, R. Findeisen, and R. D. Braatz. Robustness analysis, prediction and estimation for uncertain biochemical networks: An overview. Journal of Process Control, 42:14--34, 2016.
[77] D. Hast, S. Streif, and R. Findeisen. Detection and isolation of parametric faults in hydraulic pumps using a set-based approach and quantitative-qualitative fault specifications. 40:61--70, 2015.
[76] J.-B. Tylcz, T. Bastogne, H. Benachour, D. Bechet, E. Bullinger, H. Garnier, and M. Barberi-Heyob. A model-based pharmacokinetics characterization method of engineered nanoparticles for pilot studies. IEEE Transactions on NanoBioscience, 2015. [ DOI ]
[75] S. Blacher, C. Erpicum, B. Lenoir, J. Paupert, G. Moraes, S. Ormenese, E. Bullinger, and A. Noël. Cell invasion in the spheroid sprouting assay: A spatial organisation analysis adaptable to cell behaviour. PLoS ONE, 9(5):e97019, 05 2014. [ DOI ]
[74] L. Carius, P. Rumschinski, T. Faulwasser, D. Flockerzi, H. Grammel, and R. Findeisen. Model-based derivation, analysis and control of unstable microaerobic steady-states - considering Rhodospirillum rubrum as an example. Biotechnology and Bioengineering, 111(4):734--747, 2014.
[73] T. Faulwasser, V. Hagenmeyer, and R. Findeisen. Constrained reachability and trajectory generation for flat systems. Automatica, 50(4):1151--1159, 2014.
[72] M. Kishida, P. Rumschinski, R. Findeisen, and R. Braatz. Efficient polynomial-time outer bounds on state trajectories for uncertain polynomial systems using skewed structured singular values. IEEE Transactions on Automatic Control, 59(11):3063 -- 3068, 2014. art. no. 6808491. [ DOI ]
[71] M. Rausch, R. Klein, S. Streif, and R. Findeisen. Modellbasierte Zustandsschätzung für Lithium-Ionen-Batterien (engl. Model-based state estimation for lithium-ion batteries). AT- Automatisierungstechnik, 62(4):296--311, 2014.
[70] J. K. Scott, R. Findeisen, R. Braatz, and D. Raimondo. Input design for guaranteed fault diagnosis using zonotopes. Automatica, 50(6):1580--1589, 2014.
[69] I. Alvarado, R. Findeisen, P. Kühl, F. Allgöwer, and D. Limón. Iteratively improving moving horizon observers for repetitive processes. In F. Lamnabhi-Lagarrigue, S. Laghrouche, A. Loria, and E. Panteley, editors, Taming Heterogeneity and Complexity of Embedded Control, pages 39--54. John Wiley & Sons, Inc., 2013.
[68] S. Borchers, S. Freund, A. Rath, S. Streif, U. Reichl, and R. Findeisen. Identification of growth phases and influencing factors in cultivation of AGE1.HN cells using set-based methods. Plos One, 8(8):11, 2013.
[67] L. Carius, A.B. Carius, M. McIntosh, and H. Grammel. Quorum sensing influences growth and photosynthetic membrane production in high-cell-density cultivations of Rhodospirillum rubrum. BMC Microbiology, 13:189, 2013.
[66] L. Carius, O. Hädicke, and H. Grammel. Stepwise reduction of the culture redoxpotential allows the analysis of microaerobic metabolism and photosynthetic membrane synthesis in Rhodospirillum rubrum. Biotechnology and Bioengineering, 110(2):573--585, 2013.
[65] R. Klein, N.A. Chaturvedi, J. Christensen, J. Ahmed, R. Findeisen, and A. Kojić. Electrochemical model based observer design for a lithium-ion battery. IEEE J. Cont. Syst. Techn., 21(2):289--301, 2013.
[64] M.C. Readman, M. Schliemann, D. Kalamatianos, and E. Bullinger. A feedback control perspective on models of apoptosis signal transduction. Chaos, Solitons & Fractals, 50:93--99, 2013. Special Issue Functionality and Dynamics in Biological Systems. [ DOI ]
[63] M. Cannon, Cheng. Q, B. Kouvaritakis, and S.V. Raković. Stochastic tube MPC with state estimation. Automatica, 48(3):536--541, 2012.
[62] M. Krauss, S. Schaller, S. Borchers, R. Findeisen, J. Lippert, and L. Kuepfer. Integrating cellular metabolism into a multiscale whole-body model. PLOS Comp. Bio., 8(10):1--13, 2012.
[61] M. Kreysing, R. Pusch, D. Haverkate, M. Landsberger, J. Engelmann, J. Ruiter, C. Mora-Ferrer, E. Ulbricht, J. Grosche, K. Franze, S. Streif, S. Schumacher, F. Makarov, J. Kacza, J. Guck, H. Wolburg, J.K. Bowmaker, G. von der Emde, S. Schuster, H.-J. Wagner, A. Reichenbach, and M. Francke. Photonic crystal light collectors in fish retina improve vision in turbid water. Science, 336(6089):1700--1703, 2012. [ DOI ]
[60] M. Ma, H. Chen, R. Findeisen, and F. Allgöwer. Nonlinear receding horizon control of quadruple-tank system and real-time implementation. International Journal of Innovative Computing, Information and Control, 8(10(B)):7083--7093, 2012.
[59] S.V. Raković, B. Kouvaritakis, M. Cannon, C. Panon, and R. Findeisen. Parameterized tube model predictive control. IEEE Transactions on Automatic Control, 57(11):2746--2761, 2012.
[58] S.V. Raković, B. Kouvaritakis, R. Findeisen, and M. Cannon. Homothetic tube model predictive control. Automatica, 48(8):1631--1638, 2012. [ DOI ]
[57] P. Rumschinski, S. Streif, and R. Findeisen. Combining qualitative information and semi-quantitative data for guaranteed invalidation of biochemical network models. Int. J. of Robust and Nonlinear Control, 22(10):1157--1173, 2012. [ DOI ]
[56] S. Streif, A. Savchenko, P. Rumschinski, S. Borchers, and R. Findeisen. ADMIT: a toolbox for guaranteed model invalidation, estimation, and qualitative-quantitative modeling. Bioinformatics, 28(9):1290--1291, 2012. [ DOI ]
[55] L. Trotta, E. Bullinger, and R. Sepulchre. Global analysis of dynamical decision-making models through local computation around the hidden saddle. PLoS ONE, 7(3):e33110, 2012. [ DOI ]
[54] S. Waldherr, S. Streif, and F. Allgöwer. Design of biomolecular network modifications for adaptation. IET Syst Biol., 6(6):223--231, 2012.
[53] S. Borchers, S. Bosio, R. Findeisen, U. Haus, P. Rumschinski, and R. Weismantel. Graph problems arising from parameter identification of discrete dynamical systems. Mathematical Methods of Operations Research, 73(3):381--400, 2011.
[52] M. Cannon, B. Kouvaritakis, J. Buerger, and S.V. Raković. Robust tubes in nonlinear model predictive control. IEEE Transactions on Automatic Control, 56(8):1942--1947, 2011.
[51] M. Cannon, B. Kouvaritakis, S.V. Raković, and Q Cheng. Stochastic tubes in model predictive control with probabilistic constraints. IEEE Transactions on Automatic Control, 56(1):194--200, 2011.
[50] M. Schliemann, E. Bullinger, S. Borchers, F. Allgöwer, R. Findeisen, and P. Scheurich. Heterogeneity reduces sensitivity of cell death for TNF-stimuli. BMC Systems Biology, 5(204):28, 2011.
[49] Z. Artstein and S.V. Raković. Set invariance under output feedback: A set-dynamics approach. Int. J. of Systems Science, 42(4):539--555, 2010.
[48] C. Böhm, R. Findeisen, and F. Allgöwer. Robust control of constrained sector bounded Lur'e systems with applications to nonlinear model predictive control. J. on Dynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms, 17(6):935--958, 2010.
[47] N.A. Chaturvedi, R. Klein, J. Christensen, J. Ahmed, and A. Kojić. Algorithms for advanced battery-management systems. Control Systems Magazine, IEEE, 30(3):49--68, 2010.
[46] J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen. Guaranteed steady state bounds for uncertain (bio-)chemical processes using infeasibility certificates. J. Proc. Contr., 20(9):1076--1083, 2010.
[45] B. Kouvaritakis, M. Cannon, S.V. Raković, and Cheng. Q. Explicit use of probabilistic distributions in linear predictive control. Automatica, 46(10):1719--1724, 2010.
[44] P. Rumschinski, S. Borchers, S. Bosio, R. Weismantel, and R. Findeisen. Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks. BMC systems biology, 4:69, 2010.
[43] L. Zeiger and H. Grammel. Model-based high cell density cultivation of Rhodospirillum rubrum under respiratory dark conditions. Biotechnology and Bioengineering, 105(4):729--739, 2010.
[42] C. Böhm, F. Heß, R. Findeisen, and F. Allgöwer. An NMPC approach to avoid weakly observable trajectories. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Information Sciences LNCIS 384, pages 275--284. Springer, Berlin, Heidelberg, 2009.
[41] T. Faulwasser and R. Findeisen. Nonlinear model predictive path-following control. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Information Sciences LNCIS 384, pages 335--343. Springer, Berlin, Heidelberg, 2009.
[40] T. Faulwasser and R. Findeisen. Ein prädiktiver Ansatz zur Lösung nichtlinearer Pfadverfolgungsprobleme unter Beschränkungen. Automatisierungstechnik, 57(8):386--394, 2009.
[39] T. Faulwasser, B. Kern, P. Varutti, and R. Findeisen. Prädiktive Regelung nichtlinearer Systeme unter asynchronen Mess- und Stellsignalen. At-Automatisierungstechnik, 57(6):279--286, 2009.
[38] D. Fey, R. Findeisen, and E. Bullinger. Identification of biochemical reaction networks using a parameter-free coordinate system. Control Theory in Systems Biology, MIT press, pages 293--310, 2009.
[37] R. Findeisen and P. Varutti. Stabilizing nonlinear predictive control over nondeterministic communication networks. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Information Sciences LNCIS 384, pages 167--179. Springer, Berlin, Heidelberg, 2009.
[36] B. Kern, C. Böhm, R. Findeisen, and F. Allgöwer. Receding horizon control for linear periodic time-varying systems subject to input constraints. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Information Sciences LNCIS 384, pages 109--117. Springer, Berlin, Heidelberg, 2009.
[35] D. Mayne, S.V. Raković, R. Findeisen, and F. Allgöwer. Robust output feedback model predictive control of constrained linear systems: time-varying case. Automatica, 45(9):2082--2087, 2009.
[34] S.V. Raković. Set theoretic methods in model predictive control. In L. Magni, D. Raimundo, and F. Allgöwer, editors, Nonlinear Model Predictive Control: Towards New Challenging Applications, Lecture Notes in Control and Information Sciences LNCIS 384, pages 41--54. Springer, Berlin, Heidelberg, 2009.
[33] S.V. Raković and B. Miroslav. Local control Lyapunov functions for constrained linear discrete time systems: The Minkowski algebra approach. IEEE Transactions on Automatic Control, 54(11):2686--2692, 2009.
[32] S. Streif, S. Waldherr, F. Allgöwer, and R. Findeisen. Steady state sensitivity analysis of biochemical reaction networks: A brief review and new methods. In A. Jayaraman and J. Hahn, editors, Systems Analysis of Biological Networks, Methods in Bioengineering, pages 129--148. Artech House MIT Press, 2009.
[31] E. Bullinger, D. Fey, M. Farina, and R. Findeisen. Identifikation biochemischer Reaktionsnetzwerke: Ein beobachterbasierter Ansatz. AT-Automatisierungstechnik, 56(5):269--279, 2008.
[30] A. Graefe, C. Orwat, and T. Faulwasser. Der Umgang mit Barrieren bei der Einführung von Pervasive Computing - Ein Literaturüberblick. Technikfolgenabschätzung Theorie und Praxis, 1:13--19, 2008.
[29] M. Gurumurthy, C.H. Tan, R. Ng, L. Zeiger, J. Lau, J. Lee, A. Dey, R. Philp, Q. Li, T.M. Lim, D.H. Price, Lane D.P., and S.H. Chao. Nucleophosmin interacts with HEXIM1 and regulates RNA polymerase II transcription. Journal of Molecular Biology, 378(2):302--317, 2008.
[28] S. Maldonado, R. Findeisen, and F. Allgöwer. Understanding the process of force induced bone growth and adaptation trough mathematical modelling. Bone, 42(1):61--, 2008.
[27] S. Maldonado, R. Findeisen, and F. Allgöwer. Describing force-induced bone growth and adaptation by a mathematical model. J. of Musculoskeletal and Neuronal Interactions, 8(1):15--17, 2008.
[26] C. Orwat, A. Graefe, and T. Faulwasser. Towards pervasive computing in health care - a literature review. BMC Medical Informatics and Decision Making, 8, 2008. [ http ]
[25] G. Papavasiliou, P. Songprawat, V. Pérez-Luna, E. Hammes, M. Morris, Y.C. Chiu, and E. Brey. Three-dimensional pattering of poly (ethylene glycol) hydrogels through surface-initiated photopolymerization. Tissue Eng. Part C, Methods, 14(2):129--140, 2008.
[24] E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead. System and control theory furthers the understanding of biological signal transduction. In I. Queinnec, S. Tarbouriech, G. Garcia, and S-I. Niculescu, editors, Biology and Control Theory: Current Challenges, volume 357 of Lecture Notes in Control and Information Sciences, pages 123--135. Springer-Verlag, 2007.
[23] H. Chen, X. Gao, H. Wang, and R. Findeisen. On disturbance attenuation of nonlinear moving horizon control. In R. Findeisen, L. Biegler, and F. Allgöwer, editors, Assessment and Future Directions of Nonlinear Model Predictive Control, Lecture Notes in Control and Information Sciences, pages 283--294, Berlin, Germany, 2007. Springer-Verlag.
[22] M. Diehl, R. Findeisen, and F. Allgöwer. A stabilizing real-time implementation of nonlinear model predictive control. In L. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van Bloem Wanders, editors, Real-Time PDE Optimization, pages 25--48. SIAM, 2007.
[21] R. Findeisen, T. Raff, and F. Allgöwer. Sampled-data nonlinear model predictive control for constrained continuous time systems. In S. Tarbouriech, G. Garcia, and A.H. Glattfelder, editors, Advanced Strategies in Control Systems with Input and Output Constraints, Lecture Notes in Control and Information Sciences, pages 207--235. Springer, Berlin, 2007.
[20] R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z.K. Nagy, and F. Allgöwer. Optimization strategies for a MMA polymerization reactor. Comp. & Chem. Eng., 31(4):281--291, 2007.
[19] T. Raff, R. Findeisen, M. Herceg, and F. Allgöwer. Nonlinear model predictive control of a turbocharged diesel engine. In F. Allgöwer, L. Del Re, M. Diehl, and R. Scattolini, editors, Predictive Control of Combustion Engines. Tauner Verlag, Linz, 2007.
[18] D. Mayne, S.V. Raković, R. Findeisen, and F. Allgöwer. Robust output feedback model predictive control of constrained linear systems. Automatica, 1217-1222(42):7, 2006.
[17] T. Raff, C. Ebenbauer, R. Findeisen, and F. Allgöwer. Nonlinear model predictive control and sum of squares techniques. In M. Diehl and K. Mombauer, editors, Fast Motions in Biomechanics and Robotics, Lecture Notes in Control and Information Sciences, pages 325--344, Berlin, 2006. Springer-Verlag.
[16] M. Diehl, R. Findeisen, F. Allgöwer, H.G. Bock, and J.P. Schlöder. Nominal stability of the real-time iteration scheme for nonlinear model predictive control. IEE Control Theory Appl., 152(3):296--308, 2005.
[15] T. Raff, C. Ebenbauer, R. Findeisen, and F. Allgöwer. Remarks on moving horizon state estimation with guaranteed convergence. In T. Meurer, K. Graichen, and E.D. Gilles, editors, Control and Observer Design for Nonlinear Finite and Infinite Dimensional Systems, pages 67--80. Springer Lecture Notes in Control and Information Sciences, Springer Verlag, 2005.
[14] F. Allgöwer, R. Findeisen, and Z. Nagy. Nonlinear model predictive control: From theory to application. J. Chin. Inst. Chem. Engrs., 35(3):299--315, 2004.
[13] F. Allgöwer, R. Findeisen, and C. Ebenbauer. Nonlinear model predictive control. Encyclopedia for Life Support Systems (EOLSS) article contribution 6.43.16.2, 2003.
[12] M. Diehl, R. Findeisen, S. Schwarzkopf, I. Uslu, F. Allgöwer, H.G. Bock, and J.P. Schlöder. An efficient approach for nonlinear model predictive control of large-scale systems. Part II: Experimental evaluation considering the control of a distillation column. Automatisierungstechnik, 51(1):22--29, 2003.
[11] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Towards a sampled-data theory for nonlinear model predictive control. In C. Kang, M. Xiao, and W. Borges, editors, New Trends in Nonlinear Dynamics and Control, and their Applications, Lecture Notes in Control and Information Sciences, 295, pages 295--313, New York, USA, 2003. Springer-Verlag.
[10] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Output feedback stabilization for constrained systems with nonlinear predictive control. Int. J. of Robust and Nonlinear Control, 13(3-4):211--227, 2003.
[9] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. State and output feedback nonlinear model predictive control: An overview. Europ. J. Contr., 9(2-3):190--206, 2003.
[8] L. Imsland, R. Findeisen, F. Allgöwer, and B.A. Foss. Output feedback stabilization with nonlinear predictive control: Asymptotic properties. J. Modeling Identification and Control, 24(3):169--179, 2003.
[7] L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B.A. Foss. A note on stability, robustness and performance of output feedback nonlinear model predictive control. J. of Proc. Contr., 13(7):633--644, 2003.
[6] M. Diehl, H.G. Bock, J.P. Schlöder, R. Findeisen, Z. Nagy, and F. Allgöwer. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. J. Proc. Contr., 12(4):577--585, 2002.
[5] M. Diehl, R. Findeisen, S. Schwarzkopf, I. Uslu, F. Allgöwer, H. G. Bock, and J.P. Schlöder. An efficient approach for nonlinear model predictive control of large-scale systems part I: Description of the methodology. Automatisierungstechnik, 50(12):557--567, 2002.
[4] R. Findeisen and F. Allgöwer. The quasi-infinite horizon approach to nonlinear model predictive control. In A. Zinober and D. Owens, editors, Nonlinear and Adaptive Control, Lecture Notes in Control and Information Sciences, pages 89--105, Berlin, Germany, 2002. Springer-Verlag.
[3] M. Diehl, I. Uslu, R. Findeisen, S. Schwarzkopf, F. Allgöwer, H.G. Bock, T. Bürner, E.D. Gilles, A. Kienle, J.P. Schlöder, and E. Stein. Real-time optimization of large scale process models: Nonlinear model predictive control of a high purity distillation column. In M. Grötschel, S.O. Krumke, and J. Rambau, editors, Online Optimization of Large Scale Systems: State of the Art, pages 363--384. Springer-Verlag, 2001.
[2] R. Findeisen and F. Allgöwer. Nonlinear model predictive control for index-one DAE systems. In F. Allgöwer and A. Zheng, editors, Nonlinear Model Predictive Control, volume 26 of Progress in Systems and Control Theory, pages 145--162. Birkhäuser, 2000.
[1] R. Findeisen and F. Allgöwer. A nonlinear model predictive control scheme for the stabilization of setpoint families. Journal A, Benelux Quarterly Journal on Automatic Control, 41(1):37--45, 2000.

Submitted Journal Articles

[2] J. Matschek, T. Bäthge, T. Faulwasser, and R. Findeisen. Model Predictive Control for Path Following and Trajectory Tracking: Introduction and Application Perspectives. In S. V. Raković and William S. Levine, editors, Handbook of Model Predictive Control. Birkhäuser.
[1] H. Lindhorst, S. Lucia, R. Findeisen, and S. Waldherr. Modelling metabolic networks including gene expression and uncertainties.

Proceedings (peer reviewed)

[192] A. Savchenko, P. Andonov, P. Rumschinski, and R. Findeisen. Multi-objective complexity reduction for set-based fault diagnosis. In Advanced Control of Industrial Processes (AdCONIP), 2017 6th International Symposium on, pages 589--594, Taipei, Taiwan, 2017. IEEE.
[191] J. Matschek, J. Bethge, P. Zometa, and R. Findeisen. Force feedback and path following using predictive control: Concept and application to a lightweight robot. In Proc. 19th IFAC World Congress, pages 10243--10248, Toulouse, France, 2017.
[190] B. Morabito, R. Klein, and R. Findeisen. Real time feasibility and performance of moving horizon estimation for li-ion batteries based on first principles electrochemical models. In American Control Conference (ACC), 2017, pages 3457--3462. IEEE, 2017.
[189] M. Kögel and R. Findeisen. Low latency output feedback model predictive control for constrained linear systems. In Proc. 56th IEEE Conf. Decision Contr., CDC'17, Melbourne, Australia, 2017. To appear.
[188] M. Kögel and R. Findeisen. Robust output feedback MPC for uncertain linear systems with reduced conservatism. In Proc. IFAC World Congress 2017, pages 11172--11177, Toulouse, France, 2017.
[187] M. Kishida, M. Kögel, and R. Findeisen. Event-triggered actuator signal update using self-triggered sampled data for uncertain linear systems. In Proc. Amer. Contr. Conf. (ACC'17), pages 3035--3041, Seattle, Washington, USA., 2017.
[186] M. Kishida, M. Kögel, and R. Findeisen. Verifying Robust Forward Admissibility for Nonlinear Systems using (Skewed) Structured Singular Values. In Proc. 55th IEEE Conf. Decision Contr., CDC'16, pages 4065--4071, Las Vegas, Nevada, USA, 2016.
[185] M. Kögel and R. Findeisen. Sampled-data, output feedback predictive control of uncertain, nonlinear systems. In Proc. 10th IFAC Symposium on Nonlinear Control Systems (NOLCOS), pages 47--52, Monterey, California, USA., 2016.
[184] M. Kögel and R. Findeisen. Output feedback MPC with send-on-delta measurements for uncertain systems. In Proc. 6th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NECSYS), pages 145--150, Tokyo, Japan, 2016.
[183] T. Mühlpfordt, J.A. Paulson, R.D. Braatz, and R. Findeisen. Recursive estimation and output feedback stochastic model predictive control of linear systems with probabilistic uncertainties. In Proc. American Control Conference (ACC), 2016. To appear.
[182] T. Mühlpfordt, J.A. Paulson, R.D. Braatz, and R. Findeisen. Output feedback model predictive control with probabilistic uncertainties for linear systems. In Proc. Amer. Contr. Conf., ACC'16, 2016.
[181] S. Lucia, M. Schliemann-Bullinger, R. Findeisen, and E. Bullinger. A set-based optimal control approach for pharmacokinetic / pharmacodynamic drug dosage design. In Proceedings DYCOPS-CAB 2016, pages 797--802, Trondheim, 2016.
[180] C. Kallies, M. Schliemann-Bullinger, R. Findeisen, S. Lucia, and E. Bullinger. Monotonicity of kinetic proofreading. In IFAC-PapersOnLine - Proceedings of the 6th Foundations of Systems Biology in Engineering FOSBE 2016, 9-12 October, Magdeburg, Germany, volume 49, pages 306--311, 2016.
[179] T. Bäthge, S. Lucia, and R. Findeisen. Exploiting models of different granularity in robust predictive control. In IEEE 55th Conference on Decision and Control (CDC), pages 2763--2768, Las Vegas, NV, 2016.
[178] R. Findeisen, M. A. Grover, C. Wagner, M. Maiworm, R. Temirov, F. S. Tautz, M. V. Salapaka, S. Salapaka, R. D. Braatz, and S. O. R. Moheimani. Control on a molecular scale: a perspective. In Proc. Amer. Contr. Conf., ACC'16, pages 3069--3082, Boston, USA, 2016.
[177] T. Mühlpfordt, J.A. Paulson, R.D. Braatz, and R. Findeisen. Output feedback model predictive control with probabilistic uncertainties for linear systems. In Proc. Amer. Contr. Conf., ACC'16, 2016.
[176] G. Takács, P. Zometa, R. Findeisen, and B. Rohal-Ilkiv. Efficiency and performance of embedded model predictive control for active vibration attenuation. In Proc. of European Control Conference (ECC), pages 1334--1340, Aalborg, Denmark, 2016.
[175] P. Zometa, H. Heinemann, S. Lucia, M. Kögel, and R. Findeisen. Efficient stochastic model predictive control for embedded systems based on second-order cone programs. In Proc. of European Control Conference (ECC), pages 166--171, Aalborg, Denmark, 2016.
[174] J. Matschek, A. Himmel, F. von Haeseler, E. Bullinger, M. Skalej, and R. Findeisen. Mathematical modelling and sensitivity analysis of multipolar radiofrequency ablation in the spine. In 9th IFAC Symposium on Biological and Medical Systems (BMS), pages 243--248, Berlin, Germany, 2015.
[173] P. Andonov, A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Controller verification and parametrization subject to quantitative and qualitative requirements. In Proc. of International Symposium on Advanced Control of Chemical Processes (ADCHEM), pages 1174--1179, Whistler, Canada, 2015.
[172] R. Findeisen and B. Huang. Editorial ifac symposium on advanced control of chemical processes. In Proc. of International Symposium on Advanced Control of Chemical Processes (ADCHEM), volume 48, page V, Whistler, Canada, 2015.
[171] M. Kögel and R. Findeisen. Robust output feedback predictive control with self-triggered measurements. In Proc. of IEEE Conference on Decision and Control (CDC), pages 5487--5493, Osaka, Japan, 2015.
[170] M. Kögel and R. Findeisen. Discrete-time robust model predictive control for continuous-time nonlinear systems. In Proc. Amer. Contr. Conf., ACC'15, pages 924--930, Chicago, USA, 2015. [ DOI ]
[169] M. Kögel and R. Findeisen. Robust output feedback model predictive control using reduced order models. In Proc. of International Symposium on Advanced Control of Chemical Processes (ADCHEM), pages 1008--1014, Whistler, Canada, 2015. [ DOI ]
[168] M. Kishida and R. Findeisen. μ-based approaches to determining guaranteed consistent and inconsistent parameter sets. In Proc. 54th IEEE Conf. Decision Contr., CDC'15, pages 6603--6608, 2015. [ DOI ]
[167] M. Kopf, H.-G. Giesseler, P. Varutti, T. Faulwasser, and R. Findeisen. On the effect of enforcing stability in model predictive control for gust load alleviation. In Proc. Amer. Contr. Conf., ACC'14, pages 2329--2334, Chicago, USA, 2015.
[166] M. Kögel and R. Findeisen. Self-triggered, prediction-based control of Lipschitz nonlinear systems. In Proc. of European Control Conference (ECC), pages 2155--2160, Linz, Austria, 2015. [ DOI ]
[165] S. Lucia, M. Kögel, and R. Findeisen. Contract-based predictive control of distributed systems with plug and play capabilities. In Proc. IFAC Conference on Nonlinear Model Predictive Control (NMPC), pages 205--211, Seville, Spain, 2015. [ DOI ]
[164] S. Lucia, M. Kögel, P. Zometa, D.E. Quevedo, and R. Findeisen. Predictive control in the era of networked control and communication - a perspective. In Proc. IFAC Conference on Nonlinear Model Predictive Control (NMPC), pages 322-- 331, Seville, Spain, 2015. [ DOI ]
[163] S. Lucia, P. Rumschinski, A.J. Krener, and R. Findeisen. Improved design of nonlinear model predictive controllers. In Proceedings of the 5th IFAC Conference on Nonlinear Model Predictive Control (NMPC), pages 254--259, Seville, Spain, 2015.
[162] S. Lucia, P. Zometa, M. Kögel, and R. Findeisen. Efficient stochastic model predictive control based on polynomial chaos expansions for embedded applications. In Proc. of IEEE Conference on Decision and Control (CDC), pages 3006--3012, Osaka, Japan, 2015.
[161] M. Maiworm, T. Bäthge, and R. Findeisen. Scenario-based model predictive control: Recursive feasibility and stability. In Proc. of Int. Symp. on Ad. Control of Chem. Processes (ADCHEM'15), pages 50--56, Whistler, Canada, 2015.
[160] B. Morabito, M. Kögel, E. Bullinger, G. Pannocchia, and R. Findeisen. Simple and efficient moving horizon estimation based on the fast gradient method . In Proc. IFAC Conference on Nonlinear Model Predictive Control (NMPC), pages 428--433, Seville, Spain, 2015. [ DOI ]
[159] A. Patrascu, I. Necoara, and R. Findeisen. Rate of convergence analysis of a dual fast gradient method for general convex optimization. In Proc. 54th IEEE Conference on Decision and Control, pages 3311--3316, 2015.
[158] N. Rudolph, T. Meyer, K. Franzen, C. Garbers, S. Streif, F. Schaper, A. Dittrich, and R. Findeisen. A two-level approach for fusing early signaling events and long term biological responses. In Proc. of International Symposium on Advanced Control of Chemical Processes (ADCHEM), pages 1228--1233, Whistler, Canada, 2015.
[157] M. Schliemann-Bullinger, M.C. Readman, D. Kalamatianos, R. Findeisen, and E. Bullinger. Unraveling apoptosis signalling using linear control methods: Linking the loop gain to reverting the decision to undergo apoptosis. In Proc. of International Symposium on Advanced Control of Chemical Processes (ADCHEM), volume 48, pages 954--959, Whistler, Canada, 2015. [ DOI ]
[156] M. Kögel and R. Findeisen. On MPC based trajectory tracking. In Proceedings European Control Conference, pages 121--127, Strasbourg, France, 2014.
[155] M. Kögel and R. Findeisen. On self-triggered reduced-attention control for constrained systems. In Proceedings IEEE Conference on Decision and Control, pages 2795--2801, Los Angeles, USA, 2014.
[154] M. Kögel and R. Findeisen. Stabilization of inexact MPC schemes. In Proceedings IEEE Conference on Decision and Control, pages 5922--5928, Los Angeles, USA, 2014.
[153] S. Koulchitsky, T. Beeken, A. Monteforte, J. Dethier, E. Quertemont, R. Findeisen, E. Bullinger, and V. Seutin. Changed state - changed brain: shift of the dominant frequency of theta oscillations in the rat VTA during stereotypic locomotion. In Abstract Proceedings of Belgian Brain Council 2014 in Frontiers in Human Neuroscience, page 81, 2014. [ DOI | http ]
[152] J. Matschek, F. von Haeseler, and R. Findeisen. Mathematical modeling of mono- and multipolar radio frequency ablation in the spine - validated simulation of the temperature distribution. In Abstractband of 1st Conference on Image-Guided Interventions, pages 69--70, Magdeburg, Germany, 2014. [ .pdf ]
[151] A. Mesbah, S. Streif, R. Findeisen, and R.D. Braatz. Stochastic nonlinear model predictive control with probabilistic constraints. In Proc. Amer. Contr. Conf., ACC'14, pages 2413--2419, Portland, Oregon, 2014.
[150] A. Mesbah, S. Streif, R. Findeisen, and R.D. Braatz. Active fault diagnosis for nonlinear systems with probabilistic uncertainties. In Proc. 19th IFAC World Congress, pages 7079--7084, Cape Town, South Africa, 2014.
[149] J. A. Paulson, A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Fast stochastic model predictive control of high-dimensional systems. In Proc. 53rd IEEE Conf. on Decision and Control (CDC'14), pages 2802--2809, Los Angeles, CA, 2014.
[148] J. A. Paulson, D. M. Raimondo, R.D. Braatz, R. Findeisen, and S. Streif. Guaranteed active fault diagnosis for uncertain nonlinear systems. In Proc. European Control Conference (ECC), pages 926--931, Strasbourg, France, 2014.
[147] M. Rausch, R. Klein, S. Streif, C. Pankiewitz, and R. Findeisen. Set-based state of charge estimation for Lithium-ion batteries. In Proc. Amer. Contr. Conf., ACC'14, pages 1566--1571, Portland, Oregon, 2014.
[146] P. Rumschinski, R. Findeisen, and S. Streif. Finite-time output energy measure for polynomial systems with applications in observability analysis. In Proc. 19th IFAC World Congress, pages 2800--2805, Cape Town, South Africa, 2014.
[145] A. Savchenko, P. Andonov, S. Streif, and R. Findeisen. Guaranteed set-based controller parameter estimation for nonlinear systems - magnetic levitation platform as a case study. In Proc. 19th IFAC World Congress, pages 4650--4655, Cape Town, South Africa, 2014.
[144] S. Streif, D. Henrion, and R. Findeisen. Probabilistic and set-based model invalidation and estimation using LMIs. In Proc. 19th IFAC World Congress, pages 4110--4115, Cape Town, South Africa, 2014.
[143] S. Streif, M. Kögel, T. Bäthge, and R. Findeisen. Robust nonlinear model predictive control with constraint satisfaction: A relaxation-based approach. In Proc. 19th IFAC World Congress, pages 11073--11079, Cape Town, South Africa, 2014.
[142] S. Streif, F. Petzke, A. Mesbah, R. Findeisen, and R.D. Braatz. Optimal experimental design for probabilistic model discrimination using polynomial chaos. In Proc. 19th IFAC World Congress, pages 4103--4109, Cape Town, South Africa, 2014.
[141] T. Trenner, S. Streif, R. Findeisen, and J. Neidig. Einsatz cyber-physischer Systeme im Echtzeitkontext: Erhöhung der Autonomie durch Auswertung von Anlagenmodellen auf Zellebene. In Automation, 15. Branchentreff der Mess- und Automatisierungstechnik, VDI Bericht Vol. 2231,, pages 311--323, Baden-Baden, 2014.
[140] V. Bargsten, P. Zometa, and R. Findeisen. Modeling, parameter identification and model-based control of a lightweight robotic manipulator. In Proc. IEEE Conf. on Cont. Appl., CCA'13, pages 134--139, Hyderabad, India, 2013.
[139] S. Borchers and R. Findeisen. Outlier detection for polynomial systems using semidefinite relaxations. In IFAC Symposium on Nonlinear Control Systems, NOLCOS'13, pages 761--766, Toulouse, France, 2013.
[138] T. Faulwasser, J. Matschek, P. Zometa, and R. Findeisen. Predictive path-following control: Concept and implementation for an industrial robot. In Proc. IEEE Conf. on Cont. Appl., CCA'13, pages 128--133, Hyderabad, India, 2013.
[137] H.-G. Giesseler, M. Kopf, T. Faulwasser, P. Varutti, and R. Findeisen. Gust load alleviation based on model predictive control. In Proc. of Int. Forum on Aeroelasticity & Structural Dynamics, IFASD'13, Bristol, GB, pages 1--18, 2013.
[136] D. Hast, S. Streif, and R. Findeisen. Guaranteed diagnosability of parametric faults in nonlinear systems. In Proc. IEEE 52th Conference on Decision and Control CDC'13, pages 5662--5667, Florence, Italy, 2013.
[135] M. Kögel and R. Findeisen. On efficient predictive control of linear systems subject to quadratic constraints using condensed, structure-exploiting interior point methods. In Proc. European Control Conferencen, ECC'13, pages 27--34, Zurich, Switzerland, 2013.
[134] M. Kögel and R. Findeisen. Stability of NMPC with cyclic horizons. In IFAC Symposium on Nonlinear Control Systems, NOLCOS'13, pages 809--814, Toulouse, France, 2013.
[133] M. Kögel and R. Findeisen. Set-point tracking using distributed MPC. In Proc. 10th International Symposium on Dynamics and Control of Process Systems, DYCOPS'13, pages 57--62, Mumbai, India, 2013.
[132] M. Kögel and R. Findeisen. Distributed control of interconnected systems with lossy communication networks. In Proc. 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems NecSys 2013, pages 363--368, Koblenz, Germany, 2013.
[131] B. Kern and R. Findeisen. Analysis and constrained control of nonlinear interconnected systems exploiting positively invariant family of sets. In 52th IEEE Conf. on Decision and Control, pages 3806--3811, Firenze, Italy, 2013.
[130] M. Rausch, S. Streif, C. Pankiewitz, and R. Findeisen. Nonlinear observability and identifiability of single cells in battery packs. In Proc. IEEE Multi-conference on Systems and Control, MSC'13, pages 401--406, Hyderabad, India, 2013.
[129] A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Structural problem reduction for set-based fault diagnosis. In Proc. 10th International Symposium on Dynamics and Control of Process Systems, DYCOPS'13, pages 595--600, Mumbai, India, 2013.
[128] M. Schliemann, S. Livingstone, M.C. Readman, D. Kalamatianos, and E. Bullinger. Quantifying heterogeneity of cell death. In Computer Applications in Biotechnologie CAB2013, pages 181--186, Mumbai, India, 2013. [ DOI ]
[127] J. Scott, R. Findeisen, R. D. Braatz, and D. M. Raimondo. Design of active inputs for set-based fault diagnosis. In Proc. Amer. Contr. Conf. ACC13, pages 3561--3566, Washington, USA, 2013.
[126] S. Streif, D. Hast, R. D. Braatz, and R. Findeisen. Certifying robustness of separating inputs and outputs in active fault diagnosis for uncertain nonlinear systems. In Proc. 10th IFAC International Symposium on Dynamics and Control of Process Systems, DYCOPS'13, pages 837--842, Mumbai, India, 2013.
[125] S. Streif, M. Karl, and R. Findeisen. Outlier analysis in set-based estimation for nonlinear systems using linear relaxations. In Proc. European Control Conferencen, ECC'13, pages 2921--2926, Zurich, Switzerland, 2013.
[124] S. Streif, K. Kim, P. Rumschinski, M. Kishida, R. Findeisen, and R. D. Braatz. Robustness analysis, prediction and estimation for uncertain biochemical networks. In Proc. 10th IFAC International Symposium on Dynamics and Control of Process Systems, DYCOPS'13, pages 1--20, 2013.
[123] S. Streif, P. Rumschinski, D. Henrion, and R. Findeisen. Estimation of consistent parameter sets for continuous-time nonlinear systems using occupation measures and LMI relaxations. In 52th IEEE Conf. on Decision and Control, pages 6379--6384, Firenze, Italy, 2013.
[122] S. Streif, N. Strobel, and R. Findeisen. Inner approximations of consistent parameter sets via constraint inversion and mixed-integer linear programming. In Proc. 12th IFAC Symposium on Computer Applications in Biotechnology CAB, pages 326--331, Mumbai, India, 2013.
[121] L. Trotta, E. , and R.J. Sepulchre. Delayed-decision making in noisy bistable switches. In Computer Applications in Biotechnologie CAB2013, pages 84--88, Mumbai, India, 2013. [ DOI ]
[120] P. Zometa and R. Findeisen. muAO-MPC: A free code generation tool for embedded real-time linear model predictive control. In Proc. Amer. Contr. Conf. ACC13, pages 5340--5345, Washington, USA, 2013.
[119] H.-G. Giesseler, M. Kopf, P. Varutti, T. Faulwasser, and R. Findeisen. Model predictive control for gust load alleviation. In Proceedings of 4th IFAC Nonlinear Model Predictive Control Conference, Noordwijkerhout, NL., pages 27--32. International Federation of Automatic Control (IFAC), August 23-27 2012.
[118] T. Faulwasser and R. Findeisen. Predictive path following without terminal constraints. In 20th Int. Symposium on Mathematical Theory of Networks and Systems,Melbourne, Australia, 2012.
[117] D. Hast, M. Gottfried, and R. Findeisen. A method for the interpretation of parametric faults in model based condition monitoring. In 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. SAFEPROCESS, pages 618--623, Mexico City, Mexico, 2012.
[116] M. Kögel and R. Findeisen. Cooperative distributed MPC using the alternating direction multiplier method. In Proceedings of International Symposium on Advanced Control of Chemical Processes ADCHEM12, pages 445--450, Singapore, 2012.
[115] M. Kögel and R. Findeisen. Parallel solutions of model predictive control using the alternating direction method of multipliers. In Proceedings of 4th IFAC Conference on Nonlinear Model Predictive Control 2012 (NMPC'12), pages 369--374, Noordwijkerhout, the Netherlands, 2012.
[114] P. Rumschinski, D.S. Laila, and R. Findeisen. Discrete-to-continuous dynamics reconstruction for bilinear systems. In Proc. of the IEEE Conference on Decision and Control, pages 172--177, Maui, USA, 2012.
[113] A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Complete diagnosability of abrupt faults using set-based sensitivities. In Proc. 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes SAFEPROCESS, pages 860--865, Mexico City, Mexico, 2012.
[112] M. Schliemann, R. Findeisen, and E. Bullinger. Robustness-based model validation of an apoptosis signalling network model. In Proc. 16th IFAC Symposium on System Identification SYSID 2012, pages 930--935, Brussels, Belgium, 2012. [ DOI ]
[111] P. Varutti, B. Kern, and R. Findeisen. Dissipativity-based distributed nonlinear predictive control for cascaded systems. In Proceedings of International Symposium on Advanced Control of Chemical Processes ADCHEM12, pages 439--444, Singapour, 2012.
[110] P. Zometa, M. Kögel, T. Faulwasser, and R. Findeisen. Implementation aspects of model predictive control for embedded systems. In Proc. Amer. Control Conference ACC12, pages 1205--1210, Montreal, Canada, 2012.
[109] S. Borchers and R. Findeisen. Design of experiments for guaranteed parameter estimation in membership setting. In Proc. 50th Conference on Decision and Control, pages 2602--2607, Orlando, Fl, USA, 2011.
[108] S. Borchers, S.V. Raković, and R. Findeisen. Set membership parameter estimation and design of experiments using homothety. In Proc. 18th IFAC World Congress, pages 9034--9040, Milan, Italy, 2011.
[107] T. Faulwasser and R. Findeisen. A model predictive control approach to trajectory tracking problems via time-varying level sets of Lyapunov functions. In Proc. 50th IEEE Conf. on Decision and Control CDC, pages 3381--3386, Orlando, USA, 2011.
[106] T. Faulwasser, V. Hagenmeyer, and R. Findeisen. Optimal exact path-following for constrained differentially flat systems. In Proc. 18th IFAC World Congress, pages 9875--9880, Milan, Italy, 2011.
[105] R. Findeisen, L. Grüne, J. Pannek, and P. Varutti. Robustness of prediction based delay compensation for nonlinear systems. In Proc. 18th IFAC World Congress, pages 203--208, Milan, Italy, 2011.
[104] J. Fruth, R. Münder, H. Gruschinski, J. Dittmann, B. Karpuschewski, and R. Findeisen. Sensitising to security risks in manufacturing engineering: An exemplary vr prototype. In Second International Workshop on Digital Engineering (IWDE'11), pages 39--44, Magdeburg, Germany, 2011.
[103] M. Kishida, P. Rumschinski, R. Findeisen, and R. Braatz. Efficient polynomial-time outer bounds on state trajectories for uncertain polynomial systems using skewed structured singular values. In Proc. 2011 IEEE Multi-conference on Systems and Control, 2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD), pages 216--221, Denver, USA, 2011. [ DOI ]
[102] R. Klein, N.A. Chaturvedi, J. Christensen, J. Ahmed, R. Findeisen, and A. Kojić. Optimal charging strategies in Lithium-ion battery. In Proc. Amer. Contr. Conf. ACC11, pages 382--387, San Francisco , USA, 2011.
[101] M. Kögel, R. Blind, F. Allgöwer, and R. Findeisen. Optimal and optimal-linear control over lossy, distributed networks. In Proc. 18th IFAC World Congress, pages 13239--13244, Milan, Italy, 2011.
[100] M. Kögel and R. Findeisen. A fast gradient method for embedded linear predictive control. In Proc. 18th IFAC World Congress, pages 1362--1367, Milan, Italy, 2011.
[99] M. Kögel and R. Findeisen. Fast predictive control of linear, time-invariant systems using an algorithm based on the fast gradient method and augmented lagrange multipliers. In Proc. 2011 IEEE Multi-conference on Systems and Control, pages 780--785, Denver, USA, 2011.
[98] M. Kögel and R. Findeisen. Robust suboptimal control over lossy networks using extended input schemes. In Proc. 2011 IEEE Multi-conference on Systems and Control, pages 210--215, Denver, USA, 2011.
[97] M. Kögel and R. Findeisen. Fast predictive control of linear systems combining Nesterov’s gradient method and the method of multipliers. In Proc. IEEE Conference on Decision and Control and European Control Conference, pages 501--506, Orlando , USA, 2011.
[96] S.V. Raković, B. Kern, and R. Findeisen. Practical robust positive invariance for large-scale discrete time systems. In Proc. 18th IFAC World Congress, pages 6425--6430, Milan, Italy, 2011.
[95] S.V. Raković, B. Kouvaritakis, M. Cannon, C. Panon, and R. Findeisen. Fully parameterized tube MPC. In Proc. 18th IFAT World Congress, pages 197--202, Milan, Italy, 2011.
[94] P. Rumschinski, D.S. Laila, and R. Findeisen. Set-based parameter estimation for symmetric network motifs. In Proc. 18th IFAC World Congress, pages 10454--10459, Milan, Italy, 2011.
[93] A. Savchenko, P. Rumschinski, and R. Findeisen. Fault diagnosis for polynomial hybrid systems. In Proc. 18th IFAC World Congress, pages 2755--2760, Milan, Italy, 2011.
[92] P. Varutti and R. Findeisen. Event-based NMPC for networked control systems over UDP-like communication channels. In Proc. Amer. Contr. Conf. ACC11, pages 3166--3171, San Francisco, USA, 2011.
[91] M. Cannon, B. Kouvaritakis, J. Buerger, and S.V. Raković. Robust tubes in nonlinear model predictive control. In Proc. 8th. IFAC Symposium on Nonlinear Control Systems, NOLCOS'10, pages 208--213, Bologna, Italy, 2010.
[90] M. Cannon, B. Kouvaritakis, S.V. Raković, and Q Cheng. Stochastic tubes in model predictive control with probabilistic constraints. In Proc. Amer. Contr. Conf., ACC'10, pages 6274--6279, Baltimore, USA, 2010.
[89] M. Cannon, Cheng. Q, B. Kouvaritakis, and S.V. Raković. Stochastic tube MPC with state estimation. In Proc. 19th Int. Symposium on Mathematical Theory of Networks and Systems, MTNS'10, pages 11--16, Budapest, Hungary, 2010.
[88] N.A. Chaturvedi, R. Klein, J. Christensen, J. Ahmed, and A. Kojic. Modeling, estimation and control challenges for Lithium-ion batteries. In Proc. Amer. Contr. Conf., ACC'10, pages 1997--2002, Baltimore, USA, 2010.
[87] T. Faulwasser and R. Findeisen. Constrained output path-following for nonlinear systems using predictive control. In Proc. 8th. IFAC Symposium on Nonlinear Control Systems, NOLCOS'10, pages 753--758, Bologna, Italy, 2010.
[86] R. Klein, N.A. Chaturvedi, J. Christensen, R. Ahmed, J. Findeisen, and A. Kojic. State estimation of a reduced electrochemical model of a Lithium-ion battery. In Proc. Amer. Contr. Conf., ACC'10, pages 6618--6623, Baltimore, USA, 2010.
[85] B. Kouvaritakis, M. Cannon, S.V. Raković, and Cheng. Q. Explicit use of probabilistic distributions in linear predictive control. In Proc. International Control Control Conference, UKACC'10, Coventry, UK, 2010.
[84] M. Kögel, R. Blind, and F. Allgöwer. Optimal control over unreliable networks with uncertain loss rates. In Proc. Amer. Contr. Conf., ACC'10, pages 3672--3677, Baltimore, USA, 2010.
[83] S. Maldonado and R. Findeisen. Force-induced bone growth and adaptation: A system theoretical approach to understanding bone mechanotransduction. In IOP Conf. Ser.: Mater. Sci. Eng., Sydney, Australia, 2010.
[82] S. Maldonado, A. Savchenko, and R. Findeisen. Therapy discrimination via global sensitivity analysis of force-induced bone growth and adaptation. In Proc. of Computer-Aided Control System Design (CACSD'10), pages 499--505, Yokohama, Japan, 2010.
[81] S.V. Raković, B. Kern, and R. Findeisen. Practical set invariance for decentralized discrete time systems. In Proc. 49th IEEE Conf. on Decision and Control, Atlanta, USA, 2010.
[80] S.V. Raković, B. Kouvaritakis, R. Findeisen, and M. Cannon. Simple homothetic tube model predictive control. In Proc. 19th Int. Symposium on Mathematical Theory of Networks and Systems, MTNS'10, pages 1411--1418, Budapest, Hungary, 2010.
[79] P. Rumschinski, S. Borchers, A. Savchenko, and R. Findeisen. Advances in global parameter estimation approaches for biochemical reaction networks: An overview. In Proc. of Computer-Aided Control System Design (CACSD'10), Yokohama, Japan, 2010.
[78] P. Rumschinski, D. S. Laila, Steffen Borchers, and R. Findeisen. Influence of discretization errors on set-based parameter estimation. In Proc. IEEE Conf. on Dec. and Contr., CDC '10, pages 296--301, Atlanta, USA, 2010.
[77] P. Rumschinski, J. Richter, A. Savchenko, S. Borchers, J. Lunze, and R. Findeisen. Complete fault diagnosis of uncertain polynomial systems. In 9th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS-9, pages 127--132, Leuven, Belgium, 2010.
[76] P. Varutti, T. Faulwasser, B. Kern, M. Kögel, and R. Findeisen. Event-based reduced-attention predictive control for nonlinear uncertain systems. In Proc. of Computer-Aided Control System Design (CACSD'10), pages 1085--1090, Yokohama, Japan, 2010.
[75] P. Varutti, B. Kern, and R. Findeisen. Event-based control for networked control systems: An active compensation approach. In Proc. Entwurf komplexer Automatisierungssysteme, EKA'10, pages 29--38, Magdeburg, Germany, 2010.
[74] S. Borchers, P. Rumschinski, S. Bosio, R. Weismantel, and R. Findeisen. Model discrimination and parameter estimation via infeasibility certificates for dynamical biochemical reaction networks. In Proceedings of 16th IFAC Symposium on Identification and System Parameter Estimation SYSID, pages 245 -- 250, St. Malo, France, 2009.
[73] S. Borchers, P. Rumschinski, S. Bosio, R. Weismantel, and R. Findeisen. A set-based framework for coherent model invalidation and parameter estimation of discrete time nonlinear systems. In 48th IEEE Conf. on Decision and Control, pages 6786 -- 6792, Shanghai, China, 2009.
[72] S. Borchers, P. Rumschinski, S. Bosio, and R. Weismantel, R.and Findeisen. Model invalidation and system identification of biochemical reaction networks. In Proceedings of 7th MATHMOD conference, pages 787 -- 795, Vienna, Austria, 2009.
[71] C. Böhm, S. Yu, R. Findeisen, and F. Allgöwer. Predictive control for Lure systems subject to constraints using LMIs. In Proceedings of 10th European Control Conference ECC09, pages 3389 -- 3394, Budapest, Hungary, 2009.
[70] T. Faulwasser, B. Kern, and R. Findeisen. Model predictive path-following for constrained nonlinear systems. In Proc. 48th IEEE Conf. on Decision and Control, pages 8642--8647, Shanghai, China, 2009.
[69] J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer, and R. Findeisen. Guaranteed steady-state bounds for uncertain chemical processes. In Proceedings of International Symposium on Advanced Control of Chemical Processes ADCHEM09, Istanbul, Turkey, 2009.
[68] M. Kearney, S.V. Raković, and P.R. McAree. Control correction synthesis: A set-theoretic approach. In Proceedings of 10th European Control Conference ECC, pages 3130 -- 3135, Budapest, Hungary, 2009.
[67] S. Maldonado, F. Allgöwer, and R. Findeisen. Global sensitivity analysis of force induced bone growth and adaptation using semidefinite programming. In Proc. Foundations of Systems Biology in Engineerings FOSBE'09, pages 141 -- 144, Denver, USA, 2009.
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[61] C. Böhm, R. Findeisen, and F. Allgöwer. Avoidance of poorly observable trajectories: A predictive control perspective. In Proceedings of 17th IFAC World Congress, volume 17, pages 1952--1957, Seoul, South Korea, 2008.
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[59] N. Dmitruk, R. Findeisen, and F. Allgöwer. Optimal measurement feedback control of finite-time continous linear systems. In Proceedings of 17th IFAC World Congress, pages 15339--15344, Seoul, Korea, 2008.
[58] D. Fey, R. Findeisen, and E. Bullinger. Parameter estimation in kinetic reaction models using nonlinear observers facilitated by model extensions. In Proceedings of 17th IFAC World Congress, pages 313--318, Seoul, South Korea, 2008.
[57] D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay. Observability based parameter identifiability for biochemical reaction networks. In Proc. Amer. Contr. Conf. ACC, pages 2130--2135, Seattle, USA, 2008.
[56] S. Maldonado, R. Findeisen, and F. Allgöwer. Understanding the process of force induced bone growth and adaptation by a mathematical model. In Proc. of Comp. Modeling in Bone Mechanobiology, 8th Wolrd Congress on Computational Mechanics (WCCM8), 5th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008), Italy, 2008.
[55] S. Waldherr, R. Findeisen, and F. Allgöwer. Global sensitivity analysis of biochemical reaction networks via semidefinite programming. In Proceedings of 17th IFAC World Congress, volume 17, pages 9701--9706, Seoul, South Korea, 2008.
[54] M. Farina, E. Bullinger, R. Findeisen, and S. Bittanti. An observer based strategy for parameter identification in systems biology. In Proc. Foundations of Systems Biology in Engineering FOSBE'07, Stuttgart, Germany, pages 521--526, 2007.
[53] R. Findeisen, J. Sjoberg, and F. Allgöwer. Model predictive control of continuous time nonlinear differential algebraic systems. In Proc. Symposium on Nonlinear Control Systems, NOLCOS'07, pages 165--171, Pretoria, South Africa, 2007.
[52] D. Geffen, R. Findeisen, M. Schliemann, F. Allgöwer, and M. Guay. The question of parameter identifiability for biochemical reaction networks considering the NF-κ signal transduction pathway. In Proc. Foundations of Systems Biology in Engineering FOSBE, Stuttgart, Germany, pages 509--514, 2007.
[51] S. Maldonado, R. Findeisen, and F. Allgöwer. Phenomenological mathematical modeling and analysis of force-induced bone growth and adaptation. In Proc. Foundations of Systems Biology in Engineerings FOSBE'07, pages 147--152, Stuttgart, Germany, 2007.
[50] Z. Nagy, R. Klein, A. Kiss, and R. Findeisen. Advanced control of a reactive distillation column. Proceedings of the 17th European Symposium on Computer Process Engineering ESCAPE17, 24:805--810, 2007.
[49] S. Streif, R. Findeisen, and E. Bullinger. Sensitivity analysis of biochemical reaction networks by bilinear approximation. In Proc. Foundations of Systems Biology in Engineering (FOSBE) Conference, Stuttgart, Germany, pages 521--526, 2007.
[48] I. Alvarado, R. Findeisen, P. Kühl, D. Limón, and F. Allgöwer. Iteratively improving moving horizon observers for repetitive processes. In Proceedings of the joint CTS-HYCON workshop, Paris, France, 2006.
[47] S. Borchers, S. Maldonado, R. Findeisen, and F. Allgöwer. Modeling the bone remodeling cycle due to mechanical force. In Proc. 2nd European Modeling and Simulation Symposium (EMSS'06), pages 385--394, Barcelona, Spain, 2006.
[46] E. Bullinger, R. Findeisen, D. Kalamatianos, and P. Wellstead. System and control theory allows to further understanding of biological signal transduction. In Proceedings of Workshop CNRS-NSF Biology and control theory: Current challenges, Toulouse, France, 2006.
[45] M. Farina, R. Findeisen, E. Bullinger, S. Bittanti, F. Allgöwer, and P. Wellstead. Results towards identifiability properties of biochemical reaction networks. In Proc. 45th IEEE Conf. Decision Contr., CDC'06, pages 2104--2109, San Diego, USA, 2006.
[44] M. Herceg, T. Raff, R. Findeisen, and F. Allgöwer. Nonlinear model predictive control of a turbocharged diesel engine. In Proc. IEEE Conf. on Cont. Appl. CCA'06, pages 2766--2771, Munich, Germany, 2006.
[43] R. Lepore, A. Vande Wouwer, M. Remy, R. Findeisen, Z.K. Nagy, and F. Allgöwer. Scheduled optimization of an MMA polymerization process. In Proc. Int. Symp. Adv. Control of Chemical Processes, ADCHEM'06, pages 939--944, Gramado, Brazily, 2006.
[42] S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer. Mathematical modeling and analysis of force induced bone growth. In Proc. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS'06, pages 3154--3157, New York, USA, 2006.
[41] S. Maldonado, S. Borchers, R. Findeisen, and F. Allgöwer. Modeling bone adaptation and remodeling initiated by mechanical stimuli. In Proc. 2nd European Modeling and Simulation Symposium EMSS'06, pages 403--409, Barcelona, Spain, 2006.
[40] D. Mayne, S.V. Raković, R. Findeisen, and F. Allgöwer. Robust Output Feedback Model Predictive Control for Constrained Linear Systems under Uncertainty Based on Feed Forward and Positive Invariant Feedback Control. In Proc. IEEE Conf. on Dec. and Contr., CDC '06, pages 6618--6623, 2006.
[39] T. Raff, R. Findeisen, J.S. Kim, and F. Allgöwer. Control of nonlinear time-delay systems with guaranteed stability: A model predictive control perspective. In Proc. Symposium on Nonlinear Control Systems, NOLCOS'07, pages 134--139, Pretoria, South Africa, 2006.
[38] S.V. Raković, R. Findeisen, D. Mayne, and F. Allgöwer. Constrained linear systems under uncertainty based on feed forward and positive invariant feedback control. In Proc. 45th IEEE Conf. Decision Contr., CDC'06, pages 618--6623, San Diego, CA, 2006.
[37] S. Streif, R. Findeisen, and E. Bullinger. Relating cross Gramian and sensitivity analysis in systems biology. In Proc. 17th Int. Symposium on Mathematical Theory of Networks and Systems, MTNS'06, pages 437--442, Kyoto, Japan, 2006.
[36] I. Alvarado, R. Findeisen, P. Kühl, F. Allgöwer, and D. Limón. State estimation for repetitive processes using iteratively improving moving horizon observers. In Proc. of the joint 44th IEEE Conf. Decision Contr., CDC'05/9th European Control Conference, ECC'05, pages 7756--7761, Sevilla, Spain, 2005.
[35] R. Findeisen and F. Allgöwer. Robustness properties and output feedback of optimization based sampled-data open-loop feedback. In Proc. of the joint 44th IEEE Conf. Decision Contr., CDC'05/9th European Control Conference, ECC'05, pages 54--59, Sevilla, Spain, 2005.
[34] C. Ebenbauer, R. Findeisen, and F. Allgöwer. Nonlinear high-gain observer design via semidefinite programming. In Proc. 2nd IFAC Symposium on System, Structure and Control, SSSC'04, pages 751--756, Oaxaca, Mexico, 2004.
[33] R. Findeisen and F. Allgöwer. Computational delay in nonlinear model predictive control. In Proc. Int. Symp. Adv. Control of Chemical Processes, ADCHEM'03, pages 427--432, Hong Kong, PRC, 2004.
[32] R. Findeisen and F. Allgöwer. Stabilization using sampled-data open-loop feedback - a nonlinear model predictive control perspective. In Proc. 6th. IFAC Symposium on Nonlinear Control Systems, NOLCOS'04, pages 735--740, Stuttgart, Germany, 2004.
[31] R. Findeisen and F. Allgöwer. Min-max output feedback predictive control with guaranteed stability. In Proc. 16th Int. Symposium on Mathematical Theory of Networks and Systems, MTNS'04, pages ISBN 90--5682--517--8, CD--Rom, Katholieke Universiteit Leuven, Belgium, 2004.
[30] R. Lepore, R. Findeisen, Z.K. Nagy, F. Allgöwer, and A. Vande Wouwer. Optimal open- and closed-loop control for disturbance rejection in batch process control: a MMA polymerization example. In Proc. Symposium on Knowledge Driven Batch Processes, BatchPro, Poros, Greece, 2004.
[29] R. Lepore, R. Findeisen, A. Vande Wouwer, F. Allgöwer, and M. Remy. On open- and closed-loop control of an MMA polymerization reactor. In Proc. 23rd Benelux Meeting on Systems and Control, Helvoirt, The Netherlands, 2004.
[28] Z. Nagy, R. Findeisen, and F. Allgöwer. Hierarchical nonlinear model predictive control of an industrial batch reactor. In Proc. Symposium on Knowledge Driven Batch Processes, BatchPro, Poros, Greece, 2004.
[27] T. Raff, R. Findeisen, C. Ebenbauer, and F. Allgöwer. Model predictive control for discrete time polynomial control systems: A convex approach. In Proc. 2nd IFAC Symposium on System, Structure and Control, SSSC'04, pages 158--163, Oaxaca, Mexico, 2004.
[26] A. Yonchev, R. Findeisen, C. Ebenbauer, and F. Allgöwer. Model predictive control of linear continuous time singular systems subject to input constraints. In Proc. 43th IEEE Conf. Decision Contr., CDC'04, pages 2047--2052, Nassau, Bahamas, 2004.
[25] P. H. Menold, R. Findeisen, and F. Allgöwer. Finite time convergent observers for nonlinear systems. In Proc. 42th IEEE Conf. Decision Contr., CDC'03, pages 5673--5678, Maui, Hawaii, December 2003.
[24] M. Diehl, R. Findeisen, F. Allgöwer, J.P. Schlöder, and H.G. Bock. Stability of nonlinear model predictive control in the presence of errors due to numerical online optimization. In Proc. 42th IEEE Conf. Decision Contr., CDC'03, pages 1419--1424, Maui, Hawaii, 2003.
[23] R. Findeisen and F. Allgöwer. Theorie und Anwendung der nichtlinearen prädiktiven Regelung. In Proc. of GMA-Gesellschaft für Meß- und Automatisierungstechnik annual meeting, Baden-Baden, Germany, 2003.
[22] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Stability conditions for observer based output feedback stabilization with nonlinear model predictive control. In Proc. 42th IEEE Conf. Decision Contr., CDC'03, pages 1425--1430, Maui, Hawaii, 2003.
[21] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Output-feedback nonlinear model predictive control using high-gain observers in original coordinates. In 7th European Control Conference, ECC'2003, pages 2061--2066, Cambridge, UK, 2003.
[20] L. Imsland, R. Findeisen, F. Allgöwer, and B.A. Foss. Output feedback stabilization with nonlinear predictive control - asymptotic properties. In Proc. Amer. Contr. Conf., ACC'03, pages 4908--4913, Denver, CO, 2003.
[19] P. H. Menold, R. Findeisen, and F. Allgöwer. Finite time convergent observers for linear time-varying systems. In Proc. 11th Mediterranean Conference on Control and Automation, MED'03, Rhodos, Greece, 2003.
[18] F. Allgöwer, J.A. Rossitter, M. Cannon, B. Kouvaritakis, H.G. Bock, M. Diehl, R. Findeisen, and J.P. Schlöder. Tutorial workshop on computational efficiency in linear and non-linear predictive control. In 15th IFAC World Congress, Barcelona, Spain, 2002.
[17] R. Findeisen and F. Allgöwer. Nonlinear model predictive control: From theory to application. In Proc. Int. Symp. on Design, Operation and Control of Chemical Plants, PSE Asia'02, Taipei, Taiwan, 2002.
[16] R. Findeisen, M. Diehl, and T. Bürner. Efficient output feedback nonlinear model predictive control. In Proc. Amer. Contr. Conf., ACC'02, pages 4752--4757, Anchorage, AK, 2002.
[15] R. Findeisen, M. Diehl, I. Disli, S. Schwarzkopf, F. Allgöwer, H.G. Bock, J.P. Schlöder, and Gilles. Computation and performance assessment of nonlinear model predictive control. In Proc. of 41th IEEE Conf. Decision Contr., CDC'02, pages 4613--4618, Las Vegas, USA, 2002.
[14] R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Output feedback nonlinear predictive control - A separation principle approach. In Proceedings of 15th IFAC World Congress, Barcelona, Spain, 2002.
[13] Z. Nagy, S. Agachi, F. Allgöwer, R. Findeisen, M. Diehl, H.G. Bock, and J.P. Schlöder. The tradeoff between modelling complexity and real-time feasibility in nonlinear model predictive control. In Proc. 6th World Multiconference on Systemics, Cybernetics and Informatics, SCI 2002, pages 329--334, Orlando, Fl., 2002.
[12] F. Allgöwer and R. Findeisen. Nonlinear model predictive control of chemical processes. In B. Maschke and A. Van der Schaft, editors, Workshop on Geometrical Modeling and Control of Physical Systems, Ecole d'Etè d'Automatique de Grenoble, pages 3.1--3.75, 2001.
[11] E. Bullinger, R. Findeisen, and F. Allgöwer. Adaptive λ-tracking of nonlinear systems with higher relative degree using reduced-order high gain control. In Proc. Symposium on Nonlinear Control Systems, NOLCOS'01, pages 92--97, St. Petersburg, Russia, 2001.
[10] R. Findeisen and F. Allgöwer. An introduction to nonlinear model predictive control. In C.W. Scherer and J.M. Schumacher, editors, Summerschool on "The Impact of Optimization in Control", Dutch Institute of Systems and Control, DISC, pages 3.1--3.45, 2001.
[9] R. Findeisen, Z. Nagy, M. Diehl, F. Allgöwer, H.G. Bock, and J.P. Schlöder. Computational feasibility and performance of nonlinear model predictive control. In Proc. 6st European Control Conference, ECC'01, pages 957--961, Porto, Portugal, 2001.
[8] L. Imsland, R. Findeisen, E. Bullinger, F. Allgöwer, and B.A. Foss. On output feedback nonlinear model predictive control using high gain observers for a class of systems. In 6th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS-6, pages 91--96, Jejudo, Korea, 2001.
[7] Z. Nagy, S. Agachi, F. Allgöwer, and R. Findeisen. Nonlinear model predictive control of a high purity distillation column. In 14-th International Congress of Chemical and Process Engineering CHISA 2000, Praha, 2001. Paper ID P3.12 on CD-ROM.
[6] Z. Nagy, S. Agachi, F. Allgöwer, R. Findeisen, M. Diehl, H.G. Bock, and J.P. Schlöder. Using genetic algorithm in robust nonlinear model predictive control. In European Symposium on Computer Aided Process Engineering-11, ESCAPE-11, pages 711--716, Denmark, 2001.
[5] F. Allgöwer, R. Findeisen, Z. Nagy, M. Diehl, H.G. Bock, and J.P. Schlöder. Efficient nonlinear model predictive control for large scale constrained processes. In Proceedings of the Sixth International Conference on Methods and Models in Automation and Robotics, pages 43--54. Miedzyzdroje, Poland, 2000.
[4] E. Bullinger, R. Findeisen, F. Kraus, and F Allgöwer. Some further results on adaptive λ-tracking for linear systems with high relative degree. In Proc. Amer. Contr. Conf., ACC'00, pages 3655--3660, Chicago, USA, 2000.
[3] R. Findeisen, F. Allgöwer, M. Diehl, H.G. Bock, J.P. Schlöder, and Z. Nagy. Efficient nonlinear model predictive control. In 6th International Conference on Chemical Process Control, CPC VI, pages 454--460, 2000.
[2] R. Findeisen, H. Chen, and F. Allgöwer. Nonlinear predictive control for setpoint families. In Proc. Amer. Contr. Conf., ACC'00, pages 260--264, Chicago, USA, 2000.
[1] Z. Nagy, R. Findeisen, M. Diehl, F. Allgöwer, H.G. Bock, S. Agachi, J.P. Schlöder, and D. Leineweber. Real-time feasibility of nonlinear predictive control for large scale processes - a case study. In Proc. Amer. Contr. Conf., ACC'00, pages 4249--4253, Chicago, IL, 2000.

Submitted Proceedings (to be peer reviewed)

[3] K. Rinke, F. Jost, R. Findeisen, T. Fischer, R. Bartsch, E. Schalk, and S. Sager. Parameter estimation for leukocyte dynamics after chemotherapy. 2016.
[2] L. Carius, P. Rumschinski, and R. Findeisen. The impact of experimental data quality on computational systems biology and engineering. 2016.
[1] S. Yu, T. Qu, R. Findeisen, and H. Chen. Model predictive control for uncertain nonlinear systems subject to chance constraints. 2016.

Technical Report

[2] S.V. Raković, B. Kern, and R. Findeisen. Practical robust positive invariance for large-scale discrete time systems. Technical Report IFAT-SYS 1/2011, 2011. [ .pdf ]
[1] S.V. Raković, B. Kern, and R. Findeisen. Practical set invariance for decentralized discrete time systems. Technical Report IFAT-SYS 1/2010, 2010. [ .pdf ]