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Dr.-Ing. Anton Savchenko

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Books and Volumes
[1] A. Savchenko. Efficient Set-based Process Monitoring and Fault Diagnosis. PhD thesis, Otto-von-Guericke University Magdeburg, Magdeburg, Germany, 2017.
Journals Articles and Book Chapters (all peer reviewed)
[2] J. Bethge, R. Findeisen, D. D. Le, M. Merkert, H. Rewald, S. Sager, A. Savchenko, and S. Sorgatz. Mathematical optimization and machine learning for efficient urban traffic. German Success Stories in Industrial Mathematics, 35:113--120, 2021.
[1] 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.
Proceedings (peer reviewed)
[19] J. Pohlodek, H. Alsmeier, B. Morabito, C. Schlauch, A. Savchenko, and R. Findeisen, Stochastic Model Predictive Control Utilizing Bayesian Neural Networks. In 2023 American Control Conference (ACC), San Diego, USA, 2023.
[18] B. Morabito, J. Pohlodek, J. Matschek, A. Savchenko, L. Carius, and R. Findeisen. Towards risk-aware machine learning supported model predictive control and open-loop optimization for repetitive processes. In 7th IFAC Conference on Nonlinear Model Predictive Control NMPC, Bratislava, Slovakia, 2021.
[17] T. Zieger, A. Savchenko, T. Oehlschlägel, and R. Findeisen. One-step safe neural network supported control. In 2021 American Control Conference (ACC), New Orleans, USA, 2021.
[16] P. Andonov, A. Savchenko, P. Rumschinski, T. Trenner, J. Neidig, and R. Findeisen. Monitoring and Verification of Event-driven Discrete Manufacturing Systems with Guarantees. In Conference on Control Technology and Applications (CCTA), pages 1029--1035, Montreal, Canada, 2020.
[15] H. H. Nguyen, J. Matschek, T. Zieger, A. Savchenko, N. Noroozi, and R. Findeisen. Towards nominal stability certification of deep learning-based controllers. In 2020 American Control Conference (ACC), pages 3886--3891, Denver, CO, USA, 2020.
[14] T. Zieger, A. Savchenko, T. Oehlschlägel, and R. Findeisen. Towards safe neural network supported model predictive control. In 2020 IFAC World Congress, pages 5320--5325, Berlin, Germany, 2020.
[13] P. Andonov, A. Savchenko, and R. Findeisen. Controller Parametrization for Offset-free Control Using Set-Based Feasibility Methods. In American Control Conference (ACC), pages 2795--2800, Philadelphia, USA, 2019.
[12] P. Andonov, B. Morabito, A. Savchenko, and R. Findeisen. Admissible Control Parametrization of Uncertain Finite-time Processes With Application to Li-ion Battery Management. In European Control Conference (ECC), pages 2338--2343, Limasol, Cyprus, 2018.
[11] H. H. Nguyen, A. Savchenko, S. Yu, and R. Findeisen. Improved robust predictive control for lure systems using set-based learning. In Proceedings of 6th Nonlinear Model Predictive Control Conference (NMPC), pages 487--492, Madison, United States, 2018.
[10] 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.
[9] P. Andonov, A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Controller Verification and Parametrization Subject to Quantitative and Qualitative Requirements. In International Symposium on Advanced Control of Chemical Processes (ADCHEM), pages 1174--1179, Whistler, Canada, 2015.
[8] 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 IFAC World Congress, pages 4650--4655, Cape Town, South Africa, 2014.
[7] A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Structural problem reduction for set-based fault diagnosis. In IFAC Symposium on Dynamics and Control of Process Systems (DyCoPS), pages 595--600, Mumbai, India, 2013.
[6] A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Complete diagnosability of abrupt faults using set-based sensitivities. In International Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), pages 595--600, Mexico City, Mexico, 2012.
[5] A. Savchenko, P. Rumschinski, and R. Findeisen. Fault diagnosis for polynomial hybrid systems. In IFAC World Congress, pages 2755--2760, Milan, Italy, 2011.
[4] 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.
[3] 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.
[2] 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.
[1] U.-U. Haus, D. Michaels, and A. Savchenko. Extended formulations for MINLP problems by value decompositions. In International Conference on Engineering Optimization, 2008.