DC servo motor

Turnau A.: 1998. Prototyping of conventional and intelligent controllers. EAEEIE 98 Lisbon – Enhancement of Education in Electrical and Information Engineering through Industry Co-operation and Research, May 1998, Lisbon, Portugal, pp. 93-98.

Grega W. (1995): A study on the development of integrated environment for small DC servo motor control and simulation, in: Methods and Models in Automatics and Robotics, ed. S.Banka, Wyd. Politechniki Szczeciñskiej, 1995, pp. 371-375.

Hypiusova, M., Osusky, J. and Kajan, S. : Robust Controller Design Using Edge Theorem for Modular Servo System. In: Technical Computing Prague 2007 ,15th Annual Conference Proceedings, Prague, Czech Republic, November 14, ISBN 978-80-7080-658-6.

Ramiro S. Barbosa _, J.A. Tenreiro Machado, Isabel S. Jesus: Effect of fractional orders in the velocity control of a servo system, Computers and Mathematics with Applications 59 (2010) 1679_1686

Dragan Antić, Marko Milojković, Saša Nikolić: Fuzzy sliding mode control with additional fuzzyControl component, Facta Universitatis:Series: Automatic Control and Robotics Vol. 8, No 1, 2009, pp. 25 – 34

Ramiro S. Barbosa, J. A. Tenreiro Machado, Isabel S. Jesus: On the Fractional PID Control of a Laboratory Servo System, Proceedings of the 17th World Congress The International Federation of Automatic Control, Seoul, Korea, July 6-11, 2008

Preitl S.,  Precup R.E., Preitl Z.: Aspects Concerning the Tuning of 2-dof Fuzzy Controllers, Facta Universitatis, Series: Automatic Control and Robotics Vol. 9, No 1, 2010, pp. 1 – 18

Antić D.,  Milojković M., Jovanović Z.,  Nikolić S.: Optimal Design of the Fuzzy Sliding Mode Control for a DC Servo Drive, Strojniški vestnik – Journal of Mechanical Engineering 56(2010)7-8, 455-463 Paper received: 16.01.2009

Horla D., Simulation vs. Experimental Results of Pole-placement Controller with Full Adaptation, Proceedings of the 2013 International Conference on Systems, Control and Informatics, s. 27-33, Venice 2013.

Horla D., Minimum Variance Adaptive Control of A Servo Drive with Unknown Structure and Parameters. Asian Journal of Control, Vol. 15, s. 120.131. doi: 10.1002/asjc.479, 2013.

Horla D., Robust Performance of Sampled-data Adaptive Control of a Servo Drive. From Simulation to Experimental Results, Journal of Automation, Mobile Robotics and Intelligent Systems, Vol. 9(2), 2015, s. 3-8.

Magnetic Levitation

Turnau A., Kolek K.: 1998. Time-optimal and PID variable structure controller. Proceedings of the Mediterranean Conference on Electronics and Automatic Control MCEA’98, Marrakech, 17-19 September, Maroc, pp. 476-479.

A. Rachid,: 1998. A Maglev system for control engineering. Proceedings of the Mediterranean Conference on Electronics and Automatic Control MCEA’98, Marrakech, 17-19 September, Maroc, pp. 488-492.

Piłat A.K.: 2010, Features and Limitations of 2D Active Magnetic Levitation Systems Modeling in COMSOL Multiphysics,  Proceedings of the COMSOL Conference 2010 Paris.

Venayagamoorthy G. K. , Anene E. C.: 2010: PSO Tuned Flatness Based Control of a Magnetic Levitation System. Proceedings of the IEEE Industry Applications Society Annual Meeting, 2010. IAS ’10, Institute of Electrical and Electronics Engineers (IEEE), Oct 2010.
The definitive version is available at

Dragos C.A., Preitl S., Precup R.E., Petriu E.M. : 2011, Points of View on Magnetcic Levitation System Laboratory-Based Control Education. In Z.S.Hippe at al.(Eds): Human – Computer Systems Interaction.  AISC 99, Part II, pp. 261-275, Springer Verlag.

Piłat A.K.: 2012: The Programmable Analog Controller. Static and DynamicConfiguration, as exemplified for Active Magnetic Levitation. PRZEGLĄD ELEKTROTECHNICZNY (Electrical Review), ISSN 0033-2097, R. 88 NR 4b/2012

Balko P., Rosinova D.: 2017.  Modeling of magnetic levitation system,  21st International Conference on Process Control (PC), 6-9 June 2017.

Czerwiński K., Ławryńczuk M.: 2017:  Identification of Discrete-Time Model of Active Magnetic Levitation System. In Mitkowski W. at al.(Eds): Trends in Advanced Intelligent Control, Optimisation an Automation. pp. 599 -610, Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 577) , Springer AG 2017.

Tepljakov A.: 2017. Fractional-order Modeling and Control of Dynamic Systems, ISSN 2190-5053 Springer Theses, ISSN 2190-5061 (electronic), pp. 143-153,  Springer International Publishing 2017.

 Bojan-Dragos C.-A.,  Precup R.-E.,  Tomescu M.L., Preitl S.,  Tanasoiu O.-M.,  Hergane S.,: 2017: Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System, INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL ISSN 1841-9836, 12(5), 599-611, October 2017.

Two Rotor Aerodynamical System

Gorczyca P., Hajduk K.: Tracking Control Algorithms for Laboratory Aerodynamical System, International Journal of Applied Mathematics and Computer Science ; ISSN 1641-876X. —2004 vol. 14

Rahideh A., Shaheed M. H.: Mathematical dynamic modeling of a twin-rotor multiple-input multiple-output system. In: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2007, Vol. 221, February 1, p. 89 – 101.

Madoński R., Herman P.: An Experimental Verification of ADRC Robustness on a Cross-coupled Aerodynamical System, Industrial Electronics (ISIE), 2011 IEEE International Symposium on, pp. 859 – 863, 27-30 June 2011, Gdansk

Petko H. Petkov, Nicolai D. Christov,Mihail M. Konstantinov: Robust Real-Time Control of a Two-Rotor Aerodynamic System, Proceedings of the 17th World Congress The International Federation of Automatic Control, Seoul, Korea, July 6-11, 2008

Harlanova E., Yordanova S., Ivanov Z., Dimitrov L.: Multivariable Fuzzy Logic Control of Aerodynamic Plant, Proceedings of the 1st International Conference on Manufacturing Engineering, Quality and Production Systems (Volume II), ISBN: 978-960-474-122-9

Czajkowski A., Patan K.: Designing nonlinear model of the Two Rotor Aero-dynamical System using state space neural networks with delays, Conference: Methods and Models in Automation and Robotics – MMAR 2013 : 18th international conference; ISBN: 978-1-4673-5507-0

Czajkowski A.: Robust Control with Disturbance Estimation Using Echo State Networks for the Twin Rotor Aero-Dynamical System Application. Preprints of the 19th World Congress The International Federation of Automatic Control, Cape Town, South Africa. August 24-29, 2014

Butt S., Aschemann H.: Multivariable Integral Sliding Mode Control of a Two Degrees of Freedom
Helicopter, IFAC-PapersOnLine 48-1 (2015) 802–807.

Woźnica P., Analysis of the fuzzy control properties of a two-rotor aerodynamic system (in Polish), „Pomiary Automatyka Robotyka”, R. 19, Nr 2/2015, 13-18, DOI: 10.14313/PAR_216/13.

Pendulum – cart system

Szymkat M., Korytowski A., Turnau A.: Computation of time optimal controls by gradient matching Proc. IEEE Int. Conf. on Control Applications, August 22-27, 1999, Kohala Coast-Island of Hawai’i, USA, TuP1-2, pp. 363-368.

Turnau A.: Fuzzy and rule-based controller for cart-pole system on finite rail. Proc. of the European Conference on Modeling and Simulation, Barcelona , June 1-3, 1994, 460-464.

Turnau A.: From a rule-based to a time-suboptimal controller. Proc. 14th IASTED International Conference: Modelling, Identification and Control, February 20-22, 1995, Igls, Austria, 246-249.

Turnau A., and Korytowski A.: Time optimal control of pendulum-cart system. Sivasundaram S. (edt.) Proc. 1st International Conference on Nonlinear Problems in Aviation & Aerospace, May 9-11 1996, Embry_Riddle Aeronautical University Press, Daytona Beach, Florida, USA, pp. 649-654.

Turnau A., Korytowski A.: Synthesis of time optimal controller for a laboratory pendulum-cart model. Proc. of 16th IASTED International Conference Modeling, Identification and Control, Innsbruck, February 17-19, 1997, 366-371

Turnau A., Korytowski A., Szymkat M.: Time optimal control for the pendulum cart system in real-time Proc. IEEE Int. Conf. on Control Applications, August 22-27, 1999, Kohala Coast-Island of Hawai’i, USA, TuP6-3, pp. 1249-1254.

Grega W., Turnau A.: Time-optimal control of nonlinear systems: new methods and applications. Proc. of Summer School 99, Integrated Control Systems and Intelligent Control, Rzeszów University of Technology, Wetlina, 8-12 June, 1999, pp. 49-75.

Grega W., Turnau A.: 1998. Development of intelligent control algorithms in open architecture environment. Proc. of Summer School ’98 Kraków 7-11 July 1998.

Kempa A., Kolek K., Korytowski A., Tabakowski P, Turnau A.: 1997. Neural time-optimal real-time controller, Proc. 16th IASTED International Conference: Modelling, Identification and Control, February 17-19, Innsbruck, Austria, pp. 214-219.

Turnau A., Korytowski A.: 1996. Time optimal control of pendulum-cart system. Proc. 1st International Conference on Nonlinear Problems in Aviation & Aerospace, May 9-11, Aeronautical University Press, Daytona Beach, Florida 32114, USA, pp. 649-654.

Kolek K., Tabakowski P., Turnau A. 1993. Control of nonlinear system, simulation, real-time control, application of neural networks. Proceedings of the Int. Conference on Modelling Identification and Control. Innsbruck. Austria. pp. 126-127

Mills A., Wills A., Brett N. 2009. Nonlinear Model Predictive Control of an Inverted Pendulum
2009 American Control Conference, Hyatt Regency Riverfront, St. Louis, MO, USA
June 10-12, 2009.

Perev K.: 2011.  Inverted Pendulum Control: an Overview,  Information Technologies and Control ,1.2011, pp.34-41.


Weitian Chen, Mehrdad Saif: Output Feedback Controller Design for a Class of MIMO Nonlinear Systems Using High-Order Sliding-Mode Differentiators With Application to a Laboratory 3-D Crane, IEEE Transactions on Industrial Electronics, vol. 55, no. 11, November 2008, 3985

Rigoberto Toxqui, Wen Yu, Xiaoou Li: Anti-swing control for overhead crane with neural compensation, 2006 International Joint Conference on Neural Networks, Vancouver, BC, Canada, July 16-21, 2006

Rigoberto Toxqui , Wen Yu, Xiaoou Li: PD Control of Overhead Crane with Velocity Estimation and Uncertainties Compensation, Proceeding of the 6th World Congress on Control and Automation, June 21 – 23, 2006, Dalian, China

Rigoberto Toxqui Toxqui1, Wen Yu1, and Xiaoou Li: PD Control of Overhead Crane Systems with Neural Compensation, In J. Wang et al. (Eds.): ISNN 2006, LNCS 3972, pp. 1110–1115, Springer-Verlag Berlin Heidelberg 2006

Mariusz Pauluk, Adam Korytowski, Andrzej Turnau, Maciej Szymkat : Time optimal control of 3d crane, MMAR 2001 : Proceedings of the 7th IEEE international conference on Methods and Models in Automation and Robotics : Międzyzdroje 28–31 August 2001. Vol. 2

Masahiro SATO, Shun-ichi AZUMA and Toshiharu SUGIE: Modeling of 3D Crane and Gain Scheduling Control (in Japanese), Proceeding of the 50th Annual Conference of the Institute of Systems, Control and Information Engineers (ISCIE) Kyoto, May 10-12, 2006

Chang-Sei Kim, Keum-Shik Hong: Boundary Control of Container Cranes from the Perspective of Controlling an Axially Moving String System, International Journal of Control, Automation, and Systems (2009) 7(3):437-445, DOI 10.1007/s12555-009-0313-6,

Hahn Park, Dongkyoung Chwa, Keum-Shik Hong: A Feedback Linearization Control of Container Cranes: Varying Rope Length, International Journal of Control, Automation, and Systems, vol. 5, no. 4, pp. 379-387, August 2007

Hahn Park, Keum-Shik Hong: Sway Control of Container Cranes as an Axially Moving Nonlinear String, ICCAS2005, June 2-5, KINTEX, Gyeonggi-Do, Korea

Rigoberto Toxqui and Wen Yu,: Systems with velocity estimation and uncertainties compensation, Int. J. Automation and Control, Vol. 1, No. 4, 2007

Floriberto Ortiz Rodriguez, Wen Yu, Marco A. Moreno-Armendariz: Anti-swing control with hierarchical fuzzy CMAC compensation for an overhead crane, 22nd IEEE International Symposium on Intelligent Control Part of IEEE Multi-conference on Systems and Control Singapore, 1-3 October 2007

Zoran Jovanović, Dragan Antić, Zoran Stajić, Miloš Milošević, Saša Nikolić, Staniša Perić: Genetic Algorithms Applied In Parameters Determination Of The 3d Crane Model, Facta Universitatis: Series: Automatic Control and Robotics Vol. 10, No 1, 2011, pp. 19 – 27

Nguyen, Q.T., Veselý, V.: Robust Decentralized PID Controller Design for the 3D Crane Process, Editors: Fikar, M.,Kvasnica, M., In Proceedings of the 18th International Conference on Process Control, Tatranská Lomnica, Slovakia, pp. 485–489, 2011.

Stefan Preitl, Radu-Emil Precup and Zsuzsa Preitl: Case Studies in Teaching Fuzzy and Advanced Control Strategies, Magyar Kutatók 8. Nemzetközi Szimpóziuma 8th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics 457

Tower Crane

Altaf F. 2010; Modeling and Event-Triggered Control of Multiple 3D Tower Cranes over WSNs, Master’s Degree Project Stockholm,  Sweden October 2010,

Pauluk M.,  Marchewka D. 2011: 3D Tower Crane as a mechatronic tool for education, 1-st Slovak-Austrian International Conference on Robotics in Education 2011, Bratislava Slovakia,

BREUNING P. 2015: Linear Model Predictive Control of a 3D Tower Crane for Educational Use, Double Master’s Thesis 2015,

Thein Moe Win, Hesketh T.,  Raymond Eaton R. 2016: Robotic Tower Crane Modeling and Control (RTCMC) with LQR-DRO and LQR-LEIC for Linear and Nonlinear Payload Swing Minimization,  International Review of Automatic Control, Vol 9, No 2 (2016),

ABS system

Dragan Antić, Vlastimir Nikolić, Darko Mitić, Marko Milojković, Staniša Perić: Sliding Mode Control Of Anti-Lock Braking System: An Overview, Facta Universitatis: Series: Automatic Control and Robotics Vol. 9, No 1, 2010, pp. 41 – 58

Oniz Y., Kayacan E., Kaynak O.: A dynamic method to forecast the wheel slip for antilock braking system and its experimental evaluation. IEEE Trans. Syst. Man Cybern. B Cybern. 39(2), 551–560 (2009)

Precup R.E., Spătaru S.V., Rădac M.B., et al.: Model-based fuzzy control solutions for a laboratory antilock braking system. In: Proc. 3rd Int. Conf. On Human System Interaction, Rzeszow, Poland, pp. 133–138 (2010)

Andon V.Topalov, YesimOniz, ErdalKayacan, OkyayKaynak: Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm, Neurocomputing 74 (2011) 1883–1893

Samuel J., Jimoh O.P.: Neural Network-Based Adaptive Feedback Linearization Control of Antilock Braking System, ISSN 0974-0635 International Journal of Artificial Intelligence, Spring (March) 2013, Volume 10, Number S13

MultiTank system

Saša Nikolić, Bratislav Danković, Dragan Antić, Zoran Jovanović: On Identification Of Discrete Systems, FACTA UNIVERSITATIS: Series: Automatic Control and Robotics Vol. 9, No 1, 2010, pp. 59 – 67

Zoran Jovanović, Dragan Antić, Marko Milojković,Saša Nikolić: A New Laboratory Framework for Practical Work in Process Control, Tempus JEP – 41112 – 2006, Workshop I “New Master curricula and EU practice” September 17 – 19, 2008, Niš, Serbia

Mircea-Bogdan Rădac1, Bogdan-Alexandru Bigher1, Radu-Emil Precup1, Emil M. Petriu2, Claudia-Adina Dragoş1, Stefan Preitl1 and Alexandra-Iulia Stînean: 2012, Data-based Tuning of PI Controllers for Vertical Three-Tank Systems, In: Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics(ICINCO-2012), pages 31-39ISBN: 978-989-8565-21-1Copyrightc©2012 SCITEPRESS (Science and Technology Publications, Lda.)

Mariusz Buciakowski, Marcin Witczak, and Marcel Luzar: Robust Fault-tolerant Control for a Multi-tank System, 11th International Conference on Diagnostics of Processes and Systems , September 2013 , Poland

Danica Rosinová, Peter Balko, Teofana Puleva: 2016, Teaching multiloop control of nonlinear system: three tanks case study, IFAC-PapersOnLine 49-6 (2016) 360–365,

Tepljakov A.: 2017. Fractional-order Modeling and Control of Dynamic Systems, ISSN 2190-5053 Springer Theses, ISSN 2190-5061 (electronic), pp. 131-138,  Springer International Publishing 2017.

Pazera M., Klimkowicz K., Wrzesińska B., Witczak M.: 2017,  A Process Fault-Tolerant Control for Non-linear Dynamic Systems ,  in Advanced Solutions in Diagnostics and Fault Tolerant Control (edited by Kościelny J.M., Syfert M., Sztyber A.:), pp. 34 – 44

Miodrag Spasić1,2, Dragan Antić1, Nikola Danković1, Staniša Perić1, Saša S. Nikolić :2018, DIGITAL MODEL PREDICTIVE CONTROL OF THE THREE TANK SYSTEM BASED ON LAGUERRE FUNCTIONS, IN: FACTA UNIVERSITATIS, Series: Automatic Control and Robotics Vol. 17, No 3, 2018, pp. 153 – 164,

Zi-Jiang Yang , H. Sugiura: 2019, Robust nonlinear control of a three-tank system using finite-time disturbance observers, Control Engineering Practice, Vol. 84, 63/71 (2019)

Mohamed Omar, Ahmed Hatem Hamouda, Ayman A. El-Badawy: 2019,  Command-Filtered Backstepping Control of Multitank System, Conference: 2019 7th International Conference on Control, Mechatronics and Automation (ICCMA),

Corresponding to RT-CON

GregaW., Kolek K., Turnau A. (1998): „Rapid Prototyping Environment for Real-Time Control Education” Proceedings of the Real-Time Systems Education III, pp. 85-92

Grega W., Kolek K., Turnau A. (1998): „Real-time kernel dedicated to fast mechatronic systems” in: Proceedings of the Mediterranean Conference on Electronics and Automatic Control, pp.480-483