| Adaptive Control J Mikleš, M Fikar, J Mikleš, M Fikar Process Modelling, Identification, and Control, 445-463, 2007 | 368* | 2007 |
| Optimal operation of alternating activated sludge processes M Fikar, B Chachuat, MA Latifi Control engineering practice 13 (7), 853-861, 2005 | 110 | 2005 |
| A flexible and configurable architecture for automatic control remote laboratories M Kaluz, J Garcia-Zubia, M Fikar, Ľ Čirka IEEE Transactions on Learning Technologies 8 (3), 299-310, 2015 | 101 | 2015 |
| Clipping-based complexity reduction in explicit MPC M Kvasnica, M Fikar IEEE Transactions on Automatic Control 57 (7), 1878-1883, 2011 | 91 | 2011 |
| ArPi Lab: A low-cost remote laboratory for control education M Kalúz, Ľ Čirka, R Valo, M Fikar IFAC Proceedings Volumes 47 (3), 9057-9062, 2014 | 81 | 2014 |
| Stabilizing polynomial approximation of explicit MPC M Kvasnica, J Löfberg, M Fikar Automatica 47 (10), 2292-2297, 2011 | 59 | 2011 |
| Complexity reduction of explicit model predictive control via separation M Kvasnica, J Hledík, I Rauová, M Fikar Automatica 49 (6), 1776-1781, 2013 | 55 | 2013 |
| Dynamic optimization of batch diafiltration processes M Fikar, Z Kovács, P Czermak Journal of Membrane Science 355 (1-2), 168-174, 2010 | 53 | 2010 |
| Matlab dynamic optimisation code dynopt. user’s guide M Cizniar, M Fikar, MA Latifi Ver. 2005a, Bratislava, Slovak Republic, 2006 | 52* | 2006 |
| Optimal feeding strategy of diafiltration buffer in batch membrane processes R Paulen, M Fikar, G Foley, Z Kovács, P Czermak Journal of Membrane Science 411, 160-172, 2012 | 45 | 2012 |
| A MATLAB package for orthogonal collocations on finite elements in dynamic optimisation M Cizniar, D Salhi, M Fikar, MA Latifi Proceedings of the 15th international conference process control 5, 058f, 2005 | 43 | 2005 |
| Automatic code generation for real-time implementation of model predictive control M Kvasnica, I Rauová, M Fikar 2010 IEEE International Symposium on Computer-Aided Control System Design …, 2010 | 40 | 2010 |
| Generalized predictive control based on neural networks A Rusnak, M Fikar, K Najim, A Mészáros Neural Processing Letters 4, 107-112, 1996 | 36 | 1996 |
| Explicit stochastic MPC approach to building temperature control J Drgoňa, M Kvasnica, M Klaučo, M Fikar 52nd IEEE conference on decision and control, 6440-6445, 2013 | 35 | 2013 |
| Minimizing the process time for ultrafiltration/diafiltration under gel polarization conditions R Paulen, G Foley, M Fikar, Z Kovács, P Czermak Journal of Membrane Science 380 (1-2), 148-154, 2011 | 34 | 2011 |
| Constrained long-range predictive control based on artificial neural networks K Najim, A Rusnak, A Mészáros, M Fikar International journal of systems science 28 (12), 1211-1226, 1997 | 34 | 1997 |
| User’s guide for FORTRAN dynamic optimisation code DYNO M Fikar, MA Latifi LSGC CNRS, Nancy, France, 2002 | 33 | 2002 |
| Comparison of inverted pendulum stabilization with PID, LQ, and MPC control P Bakaráč, M Klaučo, M Fikar 2018 Cybernetics & Informatics (K&I), 1-6, 2018 | 31 | 2018 |
| System identification M Fikar, J Mikleš STU Bratislava, 1999 | 31 | 1999 |
| MPC-based reference governor control of a continuous stirred-tank reactor J Holaza, M Klaučo, J Drgoňa, J Oravec, M Kvasnica, M Fikar Computers & Chemical Engineering 108, 289-299, 2018 | 30 | 2018 |
Michal KvasnicaFull Professor at Slovak University of Technology in BratislavaVerified email at stuba.sk
