Maciej Ławryńczuk

Publications

Books

2022

1. Ławryńczuk M.: Nonlinear predictive control using Wiener models: computationally efficient approaches for polynomial and neural structures, Studies in Systems, Decision and Control, vol. 389, Springer, Cham, 2022.

2014

2. Ławryńczuk M.: Computationally efficient model predictive control algorithms: a neural network approach, Studies in Systems, Decision and Control, vol. 3, Springer, Cham, 2014.

2012

3. Ławryńczuk M.: Neural networks in computationally efficient predictive control algorithms (in Polish), Scientific Works of Warsaw University of Technology, Electronics, no. 180, Publishing House of Warsaw University of Technology, Warsaw, 2012.

2010

4. Ławryńczuk M., Marusak P.: Artificial intelligence in automatic control (in Polish), Book available in electronic form, Warsaw, 2010.

2009

5. Ławryńczuk M.: Process control (in Polish), Book available in electronic form, Warsaw, 2009.

2007

6. Kręglewska U., Ławryńczuk M., Marusak P.: Control: laboratory excercises, Publishing House of Warsaw University of Technology, Warsaw, 2007.

2003

7. Kręglewska U., Sacha K., Gustowski J., Ławryńczuk M.: Fundamentals of process control: laboratory excercises (in Polish), Publishing House of Warsaw University of Technology, Warsaw, 2003.

Chapters in books

2024

1. Chaber P., Ławryńczuk M., Nebeluk R., Plamowski S., Suchorab J., Zarzycki K.,: Artificial intelligence methods for detecting attacks on OT networks (in Polish), Cybersecurity of cyber-physical systems and the applicability of artificial intelligence (in Polish), ed.: Niewiadomska-Szynkiewicz E., pp. 55-62, Główny Instytut Górnictwa - Państwowy Instytut Badawczy, Katowice, 2024.

2023

2. Zarzycki K., Ławryńczuk M.: Physics-informed hybrid neural network model for MPC: A fuzzy approach, Advanced, Proceedings of the XXI Polish Control Conference, Lecture Notes in Networks and Systems, vol. 708, ed.: Pawełczyk M., Bismor D., Ogonowski S., Kacprzyk J., pp. 183-192, Springer, Cham, 2023.
3. Nebeluk R., Ławryńczuk M.: On the choice of the cost function for nonlinear model predictive control: A multi-criteria evaluation, Advanced, Proceedings of the XXI Polish Control Conference, Lecture Notes in Networks and Systems, vol. 708, ed.: Pawełczyk M., Bismor D., Ogonowski S., Kacprzyk J., pp. 361–371, Springer, Cham, 2023.

2022

4. Domański P., Chen Y., Ławryńczuk M.: Outliers in control engineering-they exist, like it or not, Outliers in Control Engineering: Fractional Calculus Perspective, Fractional Calculus in Applied Sciences and Engineering, vol. 10, red.: Chen Y., Domański P., Ławryńczuk M., pp. 3-26, De Gruyter, Berlin, 2022.
5. Domański P., Ławryńczuk M.: Study on robustness of nonlinear model predictive control performance assessment, Outliers in Control Engineering: Fractional Calculus Perspective, Fractional Calculus in Applied Sciences and Engineering, vol. 10, red.: Chen Y., Domański P., Ławryńczuk M., pp. 115-132, De Gruyter, Berlin, 2022.

2021

6. Tatjewski P., Ławryńczuk M.: Nonlinear predictive control, Process Control, Robotics, and Information Processing, ed.: Kulczycki P., Korbicz J., Kacprzyk J., pp. 189-228, Springer, Cham, 2021.

2020

7. Tatjewski P., Ławryńczuk M.: Nonlinear predictive control (in Polish), Process Control, Robotics, and Information Processing, ed.: Kulczycki P., Korbicz J., Kacprzyk J., pp. 171-204, Wydawnictwo Naukowe PWN, Warsaw, 2020.
8. Okulski M., Ławryńczuk M.: Identification of linear models of a tandem-wing quadplane drone: preliminary results, Advanced, Contemporary Control, Advances in Intelligent Systems and Computing t. 1196, ed.: Bartoszewicz A., Kabziński J., Kacprzyk J., pp. 219-228, Springer, Cham, 2020.
9. Zarzycki K., Ławryńczuk M.: Development and modelling of a laboratory ball on plate process, Advanced, Contemporary Control, Advances in Intelligent Systems and Computing t. 1196, ed.: Bartoszewicz A., Kabziński J., Kacprzyk J., pp. 396–408, Springer, Cham, 2020.
10. Nebeluk R., Ławryńczuk M.: Tuning of nonlinear MPC algorithm for vehicle obstacle avoidance, Advanced, Contemporary Control, Advances in Intelligent Systems and Computing t. 1196, ed.: Bartoszewicz A., Kabziński J., Kacprzyk J., pp. 993-1005, Springer, Cham, 2020.
11. Wojtulewicz A., Ławryńczuk M.: Hardware accelerators for fast implementation of DMC and GPC control algorithms using FPGA and their applications to a servomotor, Advanced, Contemporary Control, Advances in Intelligent Systems and Computing t. 1196, ed.: Bartoszewicz A., Kabziński J., Kacprzyk J., pp. 1079-1091, Springer, Cham, 2020.
12. Sawulski J., Ławryńczuk M.: PC-based simulation environment for the engine control optimiser hardware-in-the-loop testing, Advanced, Contemporary Control, Advances in Intelligent Systems and Computing t. 1196, ed.: Bartoszewicz A., Kabziński J., Kacprzyk J., pp. 1297-1308, Springer, Cham, 2020.
13. Domański P., Ławryńczuk M.: Quality assessment of nonlinear model predictive control using fractal and entropy measures, Nonlinear Dynamics and Control, ed.: Lacarbonara W., Balachandran B., Ma J., Tenreiro Machado J., Stepan G., pp. 147–156, Springer, Cham, 2020.

2018

14. Okulski M., Ławryńczuk M.: A cascade PD controller for heavy self-balancing robot, Automation 2018, Advances in Intelligent Systems and Computing, vol. 743, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 183–192, Springer, Heidelberg, 2018.
15. Dziuba K., Góra R., Domanski P., Ławryńczuk M.: Multicriterial control quality assessment in amonia production process, I Scientific Conference Innovations in Chemical Industry, pp. 80–90, Polish Chamber of Chemical Industry, Warsaw, 2018.

2017

16. Chaber P., Ławryńczuk M.: Automatic code generation of MIMO model predictive control algorithms using transcompiler, Advances in Intelligent Systems and Computing, vol. 577, ed.: Mitkowski W., Kacprzyk J., Oprzędkiewicz K., Skruch P., pp. 315–324, Springer, Cham, 2017.
17. Chaber P., Ławryńczuk M.: Implementation of analytical generalized predictive controllers for very fast applications using microcontrollers: preliminary results, Advances in Intelligent Systems and Computing, vol. 577, ed.: Mitkowski W., Kacprzyk J., Oprzędkiewicz K., Skruch P., pp. 378–387, Springer, Cham, 2017.
18. Czerwiński K., Ławryńczuk M.: Identification of discrete-time model of active magnetic levitation system, Advances in Intelligent Systems and Computing, vol. 577, ed.: Mitkowski W., Kacprzyk J., Oprzędkiewicz K., Skruch P., pp. 599–608, Springer, Cham, 2017.

2016

19. Ławryńczuk M.: Neural modelling of a yeast fermentation process using extreme learning machines, Advances in Intelligent Systems and Computing, vol. 440, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 13–23, Springer, Heidelberg, 2016.
20. Wysocki A., Ławryńczuk M.: Two- and three-layer recurrent Elman neural networks as models of dynamic processes, Advances in Intelligent Systems and Computing, vol. 440, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 165–175, Springer, Heidelberg, 2016.

2015

21. Chaber P., Ławryńczuk M.: Recurrent polynomial and neural structures in modelling of a neutralisation process, Advances in Intelligent Systems and Computing, vol. 350, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 23–32, Springer, Heidelberg, 2015.
22. Wysocki A., Ławryńczuk M.: Predictive control of a multivariable neutralisation process using Elman neural networks, Advances in Intelligent Systems and Computing, vol. 350, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 335–344, Springer, Heidelberg, 2015.

2014

23. Chaber P., Ławryńczuk M.: Software system for predictive control (in Polish), Aktualne problemy automatyki i robotyki, ed. Malinowski K., Józefczyk J., Świątek J. pp. 309-318, Exit, 2014.
24. Ławryńczuk M.: Model predictive control algorithm with optimal linearisation (in Polish), Aktualne problemy automatyki i robotyki, ed. Malinowski K., Józefczyk J., Świątek J. pp. 109-119, Exit, 2014.
25. Wysocki A., Ławryńczuk M.: On choice of the sampling period and the horizons in Generalized Predictive Control, Advances in Intelligent Systems and Computing, vol. 267, ed.: Szewczyk R., Zieliński C., Kaliczyńska M., pp. 328–339, Springer, Heidelberg, 2014.

2013

26. Ławryńczuk M.: Nonlinear predictive control based on Least Squares Support Vector Machines Hammerstein models, Lecture Notes in Computer Science, vol. 7824, ed.: Tomassini M., Antonioni A., Daolio F., Buesser P., pp. 246–255, Springer, Heidelberg, 2013.
27. Ławryńczuk M.: Development of explicit neural predictive control algorithm using particle swarm optimisation, Lecture Notes in Artificial Intelligence, vol. 7894, ed.: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J, pp. 130–139, Springer, Heidelberg, 2013.

2012

28. Ławryńczuk M.: On-line trajectory-based linearisation of neural models for a computationally efficient predictive control algorithm, Lecture Notes in Computer Science, vol. 7267, ed.: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L. A., Zurada J., pp. 124-132, Springer, Heidelberg, 2012.

2011

29. Ławryńczuk M.: Nonlinear predictive control based on multivariable neural Wiener models, Lecture Notes in Computer Science, vol. 6593, ed.: Dobnikar A., Lotrič U., Šter B., pp. 31-40, Springer, Heidelberg, 2011.
30. Ławryńczuk M.: Robust nonlinear predictive control algorithm with quadratic programming (in Polish), Postępy automatyki i robotyki, ed. Malinowski K., Dindorf R., vol. 16, pp. 106-120, Politechnika Świętokrzyska, 2011.
31. Ławryńczuk M.: Predictive control of a distillation column using a control-oriented neural model, Lecture Notes in Computer Science, vol. 6593, ed.: Dobnikar A., Lotrič U., Šter B., pp. 230-239, Springer, Heidelberg, 2011.
32. Syfert M., Chrzanowski P., Fajdek B., Ławryńczuk M., Marusak P., Patan K., Rogala T., Stec A., Szulim R., Tomasik P., Wachla D., Witczak M.: Application of the DiaSter System, Modeling, diagnostics and process control. Implementation in the DiaSter system, ed.: Korbicz J., Kościelny J. M., pp. 295-368, Springer, London, 2011.
33. Tatjewski P., Trybus L., Ławryńczuk M., Marusak P., Świder Z., Stec A.: Supervisory control and optimization, Modeling, diagnostics and process control. Implementation in the DiaSter system, ed.: Korbicz J., Koscielny J. M., pp. 233-294, Springer, London, 2011.

2010

34. Gawkowski P., Grochowski K., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Testing fault robustness of model predictive control algorithms, Lecture Notes in Computer Science, vol. 6150, ed.: Giese H., pp. 109-124, Springer, Heidelberg, 2010.
35. Gawkowski P., Ławryńczuk M., Marusak P., Tatjewski P., Sosnowski J.: Dependability comparison of explicit and numerical GPC algorithms, Novel Algorithms and Techniques in Telecommunications, Automation and Industrial Electronics, ed.: Iskander M., Karim M. A., Kapila V., pp. 419-424, Springer, 2010.
36. Ławryńczuk M.: Computationally efficient nonlinear predictive control based on state-space neural models, Lecture Notes in Computer Science, vol. 6067, ed.: Wyrzykowski R., Dongarra J., Karczewski K., Wasniewski J., pp. 350-359, Springer, Heidelberg, 2010.
37. Ławryńczuk M.: Dynamic matrix control algorithm based on interpolated step response neural models, Lecture Notes in Artificial Intelligence, vol. 6114, ed.: Rutkowski L., Scherer R., Tadeusiewicz R., Zadeh L. A., Zurada J., pp. 297-304, Springer, Heidelberg, 2010.
38. Ławryńczuk M.: Explicit neural network-based nonlinear predictive control with low computational complexity, Lecture Notes in Artificial Intelligence, vol. 6086, ed.: Szczuka M., Kryszkiewicz M., Ramanna S., Jensen R., Hu Q., pp. 649-658, Springer, Heidelberg, 2010.
39. Ławryńczuk M.: Neural dynamic matrix control algorithm with disturbance compensation, Lecture Notes in Artificial Intelligence, vol. 6098, ed.: García-Pedrajas N., Herrera F., Fyfe C., Benítez J. M., Ali M., pp. 52-61, Springer, Heidelberg, 2010.
40. Ławryńczuk M.: Precise and computationally efficient nonlinear predictive control based on neural Wiener models, Lecture Notes in Artificial Intelligence, vol. 6804, ed.: Kryszkiewicz M., Rybński H., Skowron A., Raś Z. W., pp. 663?672, Springer, Heidelberg, 2010.
41. Ławryńczuk M., Marusak P., Tatjewski P.: Efficient predictive control algorithms based on soft computing approaches: application to glucose concentration stabilization, Novel Algorithms and Techniques in Telecommunications, Automation and Industrial Electronics, ed.: Iskander M., pp. 425-430, Springer, 2010.
42. Ławryńczuk M., Tatjewski P.: Approximate neural economic set-point optimisation for control systems, Lecture Notes in Artificial Intelligence, vol. 6114, ed.: Rutkowski L., Scherer R., Tadeusiewicz R., Zadeh L. A., Zurada J., pp. 305-312, Springer, Heidelberg, 2010.

2009

43. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Towards a fault-robust GPC implementation, Diagnosis of Processes and Systems, ed.: Kowalczuk Z., Pomorskie Wydawnictwo Naukowo-Techniczne, pp. 131-140, Gdańsk, 2009.
44. Ławryńczuk M.: A predictive control economic optimiser and constraint governor based on neural models, Lecture Notes in Computer Science, vol. 5495, ed.: Kolehmainen M., Toivanen P., Beliczyński B., pp. 79-88, Springer, Heidelberg, 2009.
45. Ławryńczuk M.: Computationally efficient nonlinear predictive control based on RBF neural multi-models, Lecture Notes In Computer Science, vol. 5495, ed.: Kolehmainen M., Toivanen P., Beliczyński B., pp. 89-98, Springer, Heidelberg, 2009.
46. Ławryńczuk M.: Neural networks in model predictive control, Studies in Computational Intelligence, vol. 252, ed.: Nguyen N. T., Szczerbicki E., pp. 31-63, Springer, Heidelberg, 2009.
47. Ławryńczuk M., Marusak P., Tatjewski P.: Predictive control and set-point optimisation in DiaSter system (in Polish), Systemy wykrywające, analizujące i tolerujące usterki, ed.: Kowalczuk Z., Pomorskie Wydawnictwo Naukowo-Techniczne, s. 19-30, Gdańsk, 2009.
48. Syfert M., Chrzanowski P., Fajdek B., Ławryńczuk M., Marusak P., Patan K.: Application of the DiaSter System, Modelling, diagnostic and supervisory control of processes. Implementation in the DiaSter System (in Polish), ed.: Korbicz J., Koscielny J. M., pp. 383-443, Wydawnictwa Naukowo - Techniczne, Warsaw, 2009.
49. Tatjewski P., Trybus L., Ławryńczuk M., Marusak P., Świder Z., Stec A.: Methods of supervisory control, Modelling, diagnostic and supervisory control of processes. Implementation in the DiaSter System (in Polish), ed.: Korbicz J., Koscielny J. M., pp. 317-374, Wydawnictwa Naukowo - Techniczne, Warsaw, 2009.

2008

50. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Improving dependability of an explicit DMC algorithm software implementation, Recent Advances in Control and Automation, ed.: Malinowski K., Rutkowski L., pp. 333-432, Academic Publishing House EXIT, 2008.
51. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Software implementation of explicit DMC algorithm with improved dependability, Novel Algorithms and Techniques in Telecommunications, Automation and Industrial Electronics, ed.: Sobh T., Elleithy K., Mahmood A., Karim M. A., pp. 214-219, Springer, 2008.
52. Ławryńczuk M.: Neural models training for predictive control, Recent Advances in Control and Automation, ed.: Malinowski K., Rutkowski L., pp. 232-241, Academic Publishing House EXIT, 2008.
53. Ławryńczuk M.: Optimising predictive control based on neural models, Lecture Notes in Artificial Intelligence, vol. 5253, ed.: Dochev D., Pistore M., Traverso P., pp. 118-129, Springer, Heidelberg, 2008.
54. Ławryńczuk M.: RBF neural models in steady-state target optimisation and predictive ontrol, Recent Advances in Control and Automation, ed. Malinowski K., Rutkowski L., pp. 353-362, Academic Publishing House EXIT, 2008.
55. Ławryńczuk M.: Suboptimal nonlinear predictive control based on neural Wiener models, Lecture Notes in Artificial Intelligence, vol. 5253, ed.: Dochev D., Pistore M., Traverso P., pp. 410-414, Springer, Heidelberg, 2008.
56. Ławryńczuk M.: Suboptimal nonlinear predictive control with MIMO neural Hammerstein models, Lecture Notes in Artificial Intelligence, vol. 5027, ed.: Nguyen N. T., Borzemski L., Grzech A., Ali M., pp. 225-234, Springer, Heidelberg, 2008.
57. Ławryńczuk M.: Suboptimal nonlinear predictive control with neural multi-models, Computational Intelligence: Methods and Applications, ed.: Rutkowski L., Tadeusiewicz R., Zadeh L. A., Zurada J., pp. 45-56, Academic Publishing Hous EXIT, 2008.
58. Ławryńczuk M., Marusak P., Tatjewski P.: Optymalizujące sterowniki predykcyjne w układach sterowania z ograniczeniami, Sterowanie i automatyzacja: aktualne problemy i ich rozwiązania, ed.: Malinowski K., Rutkowski L., pp. 437-446, Academic Publishing House EXIT, 2008.
59. Ławryńczuk M., Tatjewski P.: Efficient predictive control integrated with economic optimisation based on neural models, Lecture Notes in Artificial Intelligence, vol. 5097, ed.: Rutkowski L., Tadeusiewicz R., Zadeh L. A., Zurada J., pp. 111-122, Springer, Heidelberg, 2008.

2007

60. Ławryńczuk M.: Jet engine turbine and compressor characteristics approximation by means of artificial neural networks, Lecture Notes In Computer Science, vol. 4432, pp. 143-152, Springer, Heidelberg, 2007.
61. Ławryńczuk M.: Neural models in computationally efficient predictive control cooperating with economic optimisation, Lecture Notes In Computer Science, vol. 4669, ed.: de Sá J. M., Alexandre J. M., Duch W., Mandic D., pp. 650-659, Springer, Heidelberg, 2007.
62. Ławryńczuk M.: Suboptimal nonlinear predictive control with structured neural models, Lecture Notes In Computer Science, vol. 4669, ed.: de Sá J. M., Alexandre J. M., Duch W., Mandic D., pp. 630-639, Springer, Heidelberg, 2007.
63. Ławryńczuk M., Tatjewski P.: A computationaly efficient nonlinear predictive control algorithm with RBF neural models and its application, Lecture Notes in Artificial Intelligence, t 4585, ed.: Kryszkiewicz M., Peters J. F., Rybiński H., Skowron A., pp. 603-612, Springer, Heidelberg, 2007.
64. Ławryńczuk M., Tatjewski P.: Neural network steady-state modelling of a distillation column for economic optimisation, Fault Diagnosis and Fault Tolerant Control, ed.: Korbicz J., Patan K., Kowal M., pp. 319-328, Academic Publishing House EXIT, 2007.

2006

65. Ławryńczuk M., Tatjewski P.: An efficient nonlinear predictive control algorithm with neural models and its application to a high-purity distillation process, Lecture Notes in Artificial Intelligence, vol. 4029, ed.: Rutkowski L., Tadeusiewicz R., Zadeh L. A., Zurada J. M., pp. 76-85, Springer, Heidelberg, 2006.

Journal articles

2024

1. Ławryńczuk M.: Koopman operator-based multi-model for predictive control, Nonlinear Dynamics, vol. 112, pp. 9955-9982, 2024.
2. Plamowski S., Nebeluk R., Wojtulewicz A., Cabaj K., Chaber P., Ławryńczuk M., Marusak P., Zarzycki K.: Methodology for conducting a study of the vulnerability of PLC control algorithms to cyber attacks, IEEE Access, vol. 12, pp. 135551-135563, 2024.
3. Marusak P., Nebeluk R., Wojtulewicz A., Cabaj K., Chaber P., Ławryńczuk M., Plamowski S., Zarzycki K.: Efficient cyberattack detection methods in industrial control systems, Sensors, vol. 24, paper 3860, 2024.

2023

4. Ławryńczuk M., Nebeluk R.: Beyond the quadratic norm: Computationally efficient constrained nonlinear MPC using a custom cost function, ISA Transactions, vol. 134, pp. 336-356, 2023.
5. Nebeluk R., Ławryńczuk M.: Fast nonlinear predictive control using classical and parallel Wiener models: A Comparison for a neutralization reactor process, Sensors, vol. 23, paper 9539, 2023.
6. Nebeluk R., Ławryńczuk M.: Nonlinear model predictive control with L1 cost-function using neural networks for multivariable processes, IFAC-PapersOnLine, vol. 56, pp. 1591-1596, 2023.
7. Ławryńczuk M.: Fast training of neural affine models for model predictive control: An explicit solution, IFAC-PapersOnLine, vol. 56, pp. 1578-1583, 2023.
8. Zarzycki K., Ławryńczuk M.: Physics-informed hybrid GRU neural networks for MPC prediction, IFAC-PapersOnLine, vol. 56, pp. 8726-8731, 2023.
9. Zarzycki K., Ławryńczuk M.: Long Short-Term Memory neural networks for modeling dynamical processes and predictive control: A hybrid physics-informed approach, Sensors, vol. 23, paper 8898, 2023.
10. Zarzycki K., Chaber P., Cabaj K., Ławryńczuk M., Marusak P., Nebeluk R., Plamowski S., Wojtulewicz A.: GAN neural networks architectures for testing process control industrial network against cyber-attacks, IEEE Access, vol. 11, pp. 49587-49600, 2023.
11. Zarzycki K., Chaber P., Cabaj K., Ławryńczuk M., Marusak P., Nebeluk R., Plamowski S., Wojtulewicz A.: Forgery cyber-attack supported by LSTM neural network: An experimental case study, Sensors, vol. 23, paper 6778, 2023.

2022

12. Ławryńczuk M.: Special Issue “Model Predictive Control: Algorithms and Applications”: Foreward by the Guest Editor, Algorithms, vol. 15, paper 452, 2022.
13. Zarzycki K., Ławryńczuk M.: Advanced predictive control for GRU and LSTM networks, Information Sciences, vol. 616, pp. 229-259, 2022.
14. Ławryńczuk M.: Input convex neural networks in nonlinear predictive control: A multi-model approach, Neurocomputing, vol. 513, pp. 273-293, 2022.
15. Wojtulewicz A., Domański P. D., Czarnynoga M., Kutyła M., Ławryńczuk M., Nebeluk R., Plamowski S., Rosłon K., Zarzycki K.: Practical digital twins application to high energy systems: Thermal protection for multi-detector, Electronics, vol. 11, paper 2269, 2022.
16. Nebeluk R., Ławryńczuk M.: Fast Model predictive control of PEM fuel cell system using the L1 norm, Energies, vol. 15, paper 5157, 2022.
17. Ławryńczuk M., Marusak P. M., Chaber P., Seredyński D.: Initialisation of optimisation solvers for nonlinear model predictive control: Classical vs. hybrid methods, Energies, vol. 15, paper 2483, 2022.
18. Chaber P., Domański P. D., Ławryńczuk M., Nebeluk R., Plamowski S., Zarzycki K.: Digital twins in the practice of high-energy physics experiments: a gas System for the multipurpose detector , Sensors, vol. 22, paper 678, 2022.
19. Okulski M., Ławryńczuk M.: How much energy do we need to fly with greater agility? Energy consumption and performance of an attitude stabilization controller in a quadcopter drone: A modified MPC vs. PID , Energies, vol. 15, paper 1380, 2022.
20. Okulski M., Ławryńczuk M.: A small UAV optimized for efficient long-range and VTOL missions: An experimental tandem-wing quadplane drone, Applied Sciences, vol. 12, paper 7059, 2022.
21. The MPD Collaboration: Status and initial physics performance studies of the MPD experiment at NICA, The European Physical Journal A, vol. 58, paper 140, 2022.

2021

22. Okulska I., Ławryńczuk M.: Make a difference, open the door: The energy-efficient multi-layer thermal comfort control system based on a graph airflow model with doors and windows, Information Sciences, vol. 579, pp. 553-573, 2021.
23. Ławryńczuk M., Nebeluk R.: Computationally efficient nonlinear model predictive control using the L1 cost-function, Sensors, vol. 21, paper 5835, 2021.
24. Ocłoń P., Ławryńczuk M., Czamara M.: A new solar assisted heat pump system with underground energy storage: modelling and optimisation, Energies, vol. 14, paper 5137, 2021.
25. Zarzycki K., Ławryńczuk M.: LSTM and GRU neural networks as models of dynamical processes used in predictive control: a comparison of models developed for two chemical reactors, Sensors, vol. 21, paper 5625, 2021.
26. Zarzycki K., Ławryńczuk M.: Fast real-time model predictive control for a ball-on-plate process, Sensors, vol. 21, paper 3959, 2021.
27. Domański P., Ławryńczuk M.: Impact of MPC embedded performance index on control quality, IEEE Access, vol. 9, pp. 24787-24795, 2021.
28. Nebeluk R., Ławryńczuk M.: Computationally simple nonlinear MPC algorithm for vehicle obstacle avoidance with minimization of fuel utilization, IEEE Access, vol. 9, pp. 17296-17311, 2021.
29. Okulski M., Ławryńczuk M.: A novel neural network model applied to modeling of a tandem-wing quadplane drone, IEEE Access, vol. 9, pp. 14159-14178, 2021.
30. Nebeluk R., Ławryńczuk M.: Tuning of multivariable model predictive control for industrial tasks, Algorithms, vol. 14, paper 10, 2021.

2020

31. Dziuba K., Góra R., Domański P., Ławryńczuk M.: Multicriteria ammonia plant assessment for the advanced process control implementation, IEEE Access, vol. 8, pp. 207923-207937, 2020.
32. Tatjewski P., Ławryńczuk M.: Algorithms with state estimation in linear and nonlinear model predictive control, Computers and Chemical Engineering, vol. 143, pp. 107065, 2020.
33. Domański P., Ławryńczuk M.: Control quality assessment for processes with asymmetric properties and its application to pH reactor, IEEE Access, vol. 8, pp. 94535-94546, 2020.
34. Ławryńczuk M.: Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance, ISA Transactions, vol. 99, pp. 270-289, 2020.
35. Chaber P., Ławryńczuk M.: AutoMATiC: code generation of model predictive control algorithms for microcontrollers, IEEE Transactions on Industrial Informatics, vol. 16, pp. 4547-4556, 2020.
36. Ławryńczuk M.: Nonlinear model predictive control for processes with complex dynamics: a parameterisation approach using Laguerre functions, International Journal of Applied Mathematics and Computer Science, vol. 30, pp. 35-46, 2020.
37. Ławryńczuk M., Tatjewski P.: Offset-free state-space nonlinear predictive control for Wiener systems, Information Sciences, vol. 511, pp. 127-151, 2020.

2019

38. Ławryńczuk M.: Identification of Wiener models for dynamic and steady-state performance with application to solid oxide fuel cell, Asian Journal of Control, vol. 21, pp. 1836-1846, 2019.
39. Chaber P., Ławryńczuk M.: Fast analytical model predictive controllers and their implementation for STM32 ARM microcontroller, IEEE Transactions on Industrial Informatics, vol. 15, pp. 4580-4590, 2019.
40. Ławryńczuk M., Ocłoń P.: Model Predictive Control and energy optimisation in residential building with electric underfloor heating system, Energy, vol. 182, pp. 1028-1044, 2019.
41. Czerwiński K., Wojtulewicz A., Ławryńczuk M.: Fuzzy controller for laboratory levitation system: real-time experiments using programmable logic controller, International Journal of Control, Automation and Systems, vol. 17, pp. 1507-1514, 2019.
42. Sawulski J., Ławryńczuk M.: Optimization of control strategy for a low fuel consumption vehicle engine, Information Sciences, vol. 493, pp. 192-216, 2019.
43. Ławryńczuk M., Söffker D.: Wiener structures for modeling and nonlinear predictive control of proton exchange membrane fuel cell, Nonlinear Dynamics, vol. 95, pp. 1639-1660, 2019.

2018

44. Ławryńczuk M.: Perspectives of model predictive control in high-energy physics experiments, Acta Physica Polonica B Proceedings Supplement, vol. 11, pp. 677-680, 2018.
45. Ławryńczuk M.: Towards reduced-order models of solid oxide fuel cell, Complexity, Article ID 6021249, 2018.
46. Zhang J., Chin K.-S., Ławryńczuk M.: Nonlinear model predictive control based on piecewise linear Hammerstein models, Nonlinear Dynamics, vol. 92, pp. 1001-1021, 2018.
47. Chaber P., Ławryńczuk M.: Pruning of recurrent neural models: an optimal brain damage approach, Nonlinear Dynamics, vol. 92, pp. 763-780, 2018.
48. Ziętek p., Tatjewski P., Marusak P., Szumski M., Rubik M., Szumski M., Ławryńczuk M.: Influence of the control of electronic expansion valve and compressor efficiency on operation parameters of the air-to-water heat pump, Ciepłownictwo, ogrzewanie, wentylacja, vol. 49, pp. 404-410, 2018.

2017

49. Zhang J., Chin K.S., Ławryńczuk M.: Multilinear model decomposition and predictive control of MIMO two-block cascade systems, Industrial and Engineering Chemistry Research, vol. 67, pp. 14101-14114, 2017.
50. Domański P., Ławryńczuk M.: Assessment of predictive control performance using fractal measures, Nonlinear Dynamics, vol. 89, pp. 773-790, 2017.
51. Domański P., Ławryńczuk M.: Assessment of the GPC control quality using non-Gaussian statistical measures, International Journal of Mathematics and Computer Science, vol. 27, pp. 291-307, 2017.
52. Ławryńczuk M.: Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation, ISA Transactions, vol. 67, pp. 476-495, 2017.

2016

53. Ławryńczuk M.: Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models, Nonlinear Dynamics, vol. 86, pp. 1193-1214, 2016.
54. Ławryńczuk M.: Modelling and predictive control of a neutralisation reactor using sparse support vector machine Wiener models, Neurocomputing, vol. 205, pp. 311-328, 2016.
55. Wysocki A., Ławryńczuk M.: Elman neural network for modeling and predictive control of delayed dynamic systems, Archives of Control Sciences, vol. 26, pp. 117-142, 2016.

2015

56. Wojtulewicz A., Chaber P., Ławryńczuk M.: Laboratory stand for the study of multivariable control algorithms (in Polish), Pomiary Automatyka Robotyka, vol. 19, pp. 15-20, 2015.
57. Ławryńczuk M.: Nonlinear state-space predictive control with on-line linearisation and state estimation, International Journal of Applied Mathematics and Computer Science, vol. 25, pp. 833-847, 2015.
58. Ławryńczuk M.: Nonlinear predictive control for Hammerstein-Wiener systems, ISA Transactions, vol. 55, pp. 49-62, 2015.
59. Ławryńczuk M., Tatjewski P., Marusak P., Rubik M., Ziętek P., Szumski M., Szumski M.: Design and construction of the controller for air-water heat pump: modelling of control plant (in Polish), Pomiary Automatyka Robotyka, vol. 19, pp. 21-28, 2015.

2014

60. Ławryńczuk M.: Explicit nonlinear predictive control algorithms with neural approximation, Neurocomputing, vol. 129, pp. 570-584, 2014.

2013

61. Ławryńczuk M.: Practical nonlinear predictive control algorithms for neural Wiener models, Journal of Process Control, vol. 23, pp. 696–714, 2013.
62. Ławryńczuk M.: Predictive control and set-point optimisation algorithms (in Polish), Przegląd Komunalny, no. 6, pp. 60–63, 2013.

2011

63. Ławryńczuk M.: Accuracy and computational efficiency of suboptimal nonlinear predictive control based on neural models, Applied Soft Computing, vol. 11, pp. 2202-2215, 2011.
64. Ławryńczuk M.: On improving accuracy of computationally efficient nonlinear predictive control based on neural models, Chemical Engineering Science, vol. 66, pp. 5253-5267, 2011.
65. Ławryńczuk M.: On-line set-point optimisation and predictive control using neural Hammerstein models, Chemical Engineering Journal, vol. 166, pp. 269-287, 2011.
66. Ławryńczuk M.: Online set-point optimisation cooperating with predictive control of a yeast fermentation process: a neural network approach, Engineering Applications of Artificial Intelligence, vol. 24, pp. 968-982, 2011.

2010

67. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Fail-bounded implementations of the numerical model predictive control algorithms, Control and Cybernetics, vol. 39, pp. 1117-1134, 2010.
68. Ławryńczuk M.: Computationally efficient nonlinear predictive control based on neural Wiener models, Neurocomputing, vol. 74, pp. 401-417, 2010.
69. Ławryńczuk M.: Suboptimal nonlinear predictive control based on multivariable neural Hammerstein models, Applied Intelligence, vol. 32, pp. 173-192, 2010.
70. Ławryńczuk M.: Training of neural models for predictive control, Neurocomputing, vol. 73, pp. 1332-1343, 2010.
71. Ławryńczuk M., Marusak P., Tatjewski P.: Cooperation of predictive control and on-line set-point optimisation in multilayer control structures with Wiener models (in Polish), Pomiary Automatyka Robotyka, no. 2, pp. 470-480, 2010.
72. Ławryńczuk M., Tatjewski P.: Nonlinear predictive control based on neural multi-models, International Journal of Applied Mathematics and Computer Science, vol. 20, pp. 7-21, 2010.
73. Radomski D., Ławryńczuk M., Marusak P., Tatjewski P.: Modeling of glucose concentration dynamics for predictive control of insulin administration, Biocybernetics and Biomedical Engineering, vol. 30, pp. 41-53, 2010.

2009

74. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Fault sensitivity of explicit DMC and GPC algorithms, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 3, pp. 52-56, 2009.
75. Ławryńczuk M.: Explicit nonlinear predictive control algorithm with neural models (in Polish), Pomiary Automatyka Robotyka, no. 2, pp. 506-518, 2009.
76. Ławryńczuk M.: Efficient nonlinear predictive control based on structured neural models, International Journal of Applied Mathematics and Computer Science, vol. 19, pp. 233-246, 2009.
77. Ławryńczuk M.: Efficient nonlinear predictive control of a biochemical reactor using neural models, Bioprocess and Biosystems Engineering, vol. 32, pp. 301-312, 2009.
78. Ławryńczuk M.: Explicit nonlinear predictive control of a distillation column based on neural models, Chemical Engineering and Technology, vol. 10, pp. 1578-1587, 2009.

2008

79. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Dependability of the software implementation of the explicit DMC algorithm, IADIS International Journal on Computer Science and Information System, vol. 3, pp. 44-58, 2008.
80. Ławryńczuk M.: Identification of recurrent RBF neural models (in Polish), Pomiary Automatyka Robotyka, no. 2, pp. 558-570, 2008.
81. Ławryńczuk M.: Modelling and nonlinear predictive control of a yeast fermentation biochemical reactor using neural networks, Chemical Engineering Journal, vol. 145, pp. 290-307, 2008.
82. Ławryńczuk M.: Suboptimal non-linear predictive control based on MLP and RBF neural models with measured disturbance compensation, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 2, pp. 54-64, 2008.
83. Ławryńczuk M.: Suboptimal predictive control algorithm with FIR neural models (in Polish), Pomiary Automatyka Robotyka, no. 2, pp. 548-557, 2008.
84. Ławryńczuk M., Marusak P., Tatjewski P.: Cooperation of model predictive control with steady-state economic optimisation, Control and Cybernetics, vol. 37, pp. 133-158, 2008.
85. Ławryńczuk M., Tadej W.: A computationally efficient stable dual-mode type nonlinear predictive control algorithm, Control and Cybernetics, vol. 37, pp. 99-132, 2008.

2007

86. Ławryńczuk M.: A family of model predictive control algorithms with artificial neural networks, International Journal of Applied Mathematics and Computer Science, vol. 17, pp. 217-232, 2007.
87. Ławryńczuk M.: Advanced predictive control of a distillation column with neural models, Archives of Control Sciences, vol. 17, pp. 121-148, 2007.
88. Ławryńczuk M.: Recurrent neural networks in modelling nonlinear dynamic processes (in Polish), Pomiary Automatyka Robotyka, no. 2, CD-ROM, 2007.
89. Ławryńczuk M., Marusak P., Tatjewski P.: Structures and algorithms for cooperation between predictive control and on-line economic set-point optimisation (in Polish), Pomiary Automatyka Kontrola, vol. 53, pp. 55-61, 2007.

2006

90. Tatjewski P., Ławryńczuk M.: Soft computing in model-based predictive control, International Journal of Applied Mathematics Computer Science, vol. 16, pp. 101-120, 2006.

2004

91. Ławryńczuk M., Tatjewski P.: An infinite horizon predictive control algorithm based on multivariable input-output models, International Journal of Applied Mathematics and Computer Science, vol. 14, pp. 167-180, 2004..

2003

92. Ławryńczuk M., Tatjewski P.: Computationally efficient multivariable predictive control algorithm for input-output models (in Polish), Pomiary Automatyka Kontrola, vol. 49, pp. 10-14, 2003.
93. Ławryńczuk M., Tatjewski P.: Multivariable CRHPC (constrained receding-horizon predictive control) algorithm with improved numerical properties, Archives of Control Sciences, vol. 13, pp. 5-24, 2003.

1999

94. Ławryńczuk M.: Inferential measurements: the idea and industrial applications (in Polish), Pomiary Automatyka Robotyka, no. 6, pp. 7-10, 1999.

Conference proceedings

2023

1. Ławryńczuk M.: Fast training of neural affine models for model predictive control: An explicit solution, Proceedings of the 22nd World Congress IFAC, Yokohama, Japan, pp. 1724-1729, 2023.
2. Nebeluk R., Ławryńczuk M.: Nonlinear model predictive control with L1 cost-function using neural networks for multivariable processes, Proceedings of the 22nd World Congress IFAC, Yokohama, Japan, pp. 1737-1742, 2023.
3. Zarzycki K., Ławryńczuk M.: Physics-informed hybrid GRU neural networks for MPC prediction, Proceedings of the 22nd World Congress IFAC, Yokohama, Japan, pp. 9420-9425, 2023.

2022

4. Nebeluk R., Ławryńczuk M.: Fast nonlinear model predictive control using a custom cost-function: Preliminary results, Proceedings of the 30th Mediterranean Conference on Control and Automation, pp. 13-18, Vouliagmeni, Greece, 2022.
5. Zarzycki K., Ławryńczuk M.: Fast nonlinear model predictive control using LSTM networks: A model linearisation approach, Proceedings of the 30th Mediterranean Conference on Control and Automation, pp. 1-6, Vouliagmeni, Greece, 2022.

2020

6. Domański P., Ławryńczuk M.: Multi-criteria control performance assessment method for a multivariate MPC, Proceedings of the 2020 American Control Conference ACC 2020, pp. 1968-1973, Denver, USA, 2020.

2019

7. Domański P., Ławryńczuk M., Golonka S., Moszowski B., Matyja P.: Multi-criteria loop quality assessment: a large-scale industrial case study, Proceedings of the 24th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2019, pp. 99-104, Międzyzdroje, 2019.
8. Okulski M., Ławryńczuk M.: Development of a high-efficiency pitch/roll inertial measurement unit based on a low-cost accelerometer and gyroscope sensors, Proceedings of the 24th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2019, pp. 657-662, Międzyzdroje, 2019.
9. Sawulski J., Ławryńczuk M.: Real-time optimisation of control strategy for a low fuel consumption vehicle engine using STM32 microcontroller: preliminary results, Proceedings of the European Control Conference ECC 2019, pp. 4246-4251, Napoli, Italy, 2019.
10. Domański P., Ławryńczuk M.: Control quality assessment of nonlinear model predictive control using fractal and entropy measures, Proceedings of the First International Nonlinear Dynamics Conference NODYCON 2019, pp. 597-598, Rome, 2019.

2018

11. Sawulski J., Ławryńczuk M.: Optimisation-based tuning of dynamic matrix control algorithm for multiple-input multiple-output processes, Proceedings of the 23th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2018, pp. 160-165, Międzyzdroje, Poland, 2018.
12. Okulski M., Ławryńczuk M.: Development of a model predictive controller for an unstable heavy self-balancing robot, Proceedings of the 23th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2018, pp. 503-508, Międzyzdroje, Poland, 2018.
13. Wojtulewicz A., Ławryńczuk M.: Computationally efficient implementation of dynamic matrix control algorithm for very fast processes using programmable logic controller, Proceedings of the 23th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2018, pp. 579-584, Międzyzdroje, Poland, 2018.
14. Wojtulewicz A., Ławryńczuk M.: Implementation of multiple-input multiple-output dynamic matrix control algorithm for fast processes using field programmable gate array, 15th International Conference on Programmable Devices and Embedded Systems, PDeS 2018, Ostrava, Czech Republic, IFAC PapersOnLine, vol. 51-6, pp. 324-329, 2018.
15. Czerwiński K., Ławryńczuk M.: Dynamic matrix control algorithm implementation on ARM Cortex-R5 MCU: performance analysis, 15th International Conference on Programmable Devices and Embedded Systems, PDeS 2018, Ostrava, Czech Republic, IFAC PapersOnLine, vol. 51-6, pp. 330-335, 2018.

2016

16. Ławryńczuk M.: Nonlinear predictive control of temperature in long duct using specially designed neural model, Proceedings of the 3rd International Conference on Control and Fault-Tolerant Systems SysTol 2016, pp. 275-280, Barcelona, Spain, 2016.
17. Chaber P., Ławryńczuk M.: Effectiveness of PID and DMC control algorithms automatic code generation for microcontrollers: application to a thermal process, Proceedings of the 3rd International Conference on Control and Fault-Tolerant Systems SysTol 2016, pp. 603-608, Barcelona, Spain, 2016.
18. Chaber P., Ławryńczuk M.: Auto-generation of advanced control algorithms’ code for microcontrollers using transcompiler, Proceedings of the 21th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 454-459, Międzyzdroje, Poland, 2016.
19. Wojtulewicz A., Chaber P., Ławryńczuk M.: Multiple-input multiple-output laboratory stand for process control education, Proceedings of the 21th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 466-471, Międzyzdroje, Poland, 2016.
20. Ławryńczuk M.: An easily trained neural model of a distributed parameter system, Proceedings of the 21th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 674-679, Międzyzdroje, Poland, 2016.
21. Tatjewski P., Ławryńczuk M., Marusak P., Rubik M., Ziętek P., Szumski M., Szumski M.: Design and implementation of the air/water heat pump controller with increased coefficient of performance, Proceedings of the 21th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2016, pp. 959-964, Międzyzdroje, Poland, 2016.

2015

22. Wysocki A., Ławryńczuk M.: Jordan neural network for modelling and predictive control of dynamic systems, Proceedings of the 20th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2015, pp. 145-150, Międzyzdroje, Poland, 2015.
23. Ławryńczuk M.: Approximate state-space model predictive control, Proceedings of the 20th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2015, pp. 770-775, Międzyzdroje, Poland, 2015.
24. Chaber P., Ławryńczuk M.: RBF neural networks for modelling and predictive control: an application to a neutralisation process, Proceedings of the 20th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2015, pp. 776-781, Międzyzdroje, Poland, 2015.

2014

25. Ławryńczuk M.: Model predictive control with on-line optimal linearisation, Proceedings of the 2014 IEEE International Symposium on Intelligent Control, ISIC, part of the 2014 IEEE Multi-conference on Systems and Control, MSC 2014, pp. 2177-2182, Antibes, France, 2014.
26. Ławryńczuk M.: A predictive control algorithm with optimal linearisation (in Polish), Proceedings of the 18th National Control Conference, CD-ROM, Wrocław, Poland, 2014.
27. Ławryńczuk M., Chaber P.: A software system for predictive control (in Polish), Proceedings of the 18th National Control Conference, CD-ROM, Wrocław, Poland, 2014.
28. Tatjewski P., Ławryńczuk M., Marusak P., Rubik. M., Ziętek P., Szumski M.: Assumptions to the construction of the regulator for air/water heat pumps (in Polish), Proceedings of the 2014 Air, Heat & Energy Conference, pp. 487–492, Wrocław, Poland, 2014.

2013

29. Wysocki A., Ławryńczuk M.: An investment strategy for the stock exchange using neural networks, Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Fedcis 2013, pp. 183–190, Kraków, Poland, 2013.

2009

30. Gawkowski P., Ławryńczuk M., Marusak P., Tatjewski P., Sosnowski J.: On improving dependability of the numerical GPC algorithm, Proceedings of the European Control Conference ECC 2009, pp. 1377-1382, Budapest, Hungary, 2009.
31. Ławryńczuk M., Marusak P., Tatjewski P.: On cooperation of set-point optimisation and predictive control based on Hammerstein models, Proceedings of the 7th Workshop on Advanced Control and Diagnosis, CD-ROM, paper no. 88, Zielona Góra, Poland, 2009.
32. Tatjewski P., Ławryńczuk M., Marusak P.: Integrated predictive optimiser and constraint supervisor for processes with basic feedback control, Proceedings of the European Control Conference ECC 2009, pp. 3359-3364, Budapest, Hungary, 2009.

2008

33. Ławryńczuk M., Marusak P., Tatjewski P.: Piecewise linear steady-state target optimization for control systems with MPC: a case study, Proceedings of the 17th World Congress IFAC, pp. 13169-13174, 2008.

2007

34. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Dependability of explicit DMC and GPC algorithms, Proceedings of the II International Multiconference on Computer Science and Information Technology, pp. 903-912, 2007.
35. Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.: Dependability of the explicit DMC algorithm for a rectification process, Proceedings of the II International Multiconference on Computer Science and Information Technology, pp. 779-788, 2007.
36. Ławryńczuk M.: A suboptimal nonlinear predictive control algorithm based on neural models with measured disturbance compensation, Proceedings of the 13th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 509-516, Międzyzdroje, Poland, 2007.
37. Ławryńczuk M.: An efficient nonlinear predictive control algorithm with neural models based on multipoint on-line linearisation, Proceedings of the International Conference on Computer as a Tool EUROCON 2007, CD-ROM, pp. 777-784, Warsaw, Poland, 2007.
38. Ławryńczuk M.: Computational efficiency of suboptimal nonlinear predictive control with neural models, Proceedings of the II International Multiconference on Computer Science and Information Technology, pp. 257-266, 2007.
39. Ławryńczuk M.: Suboptimal nonlinear predictive control with structured RBF neural models, Proceedings of the 13th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 527-534, Międzyzdroje, Poland, 2007.
40. Ławryńczuk M.: Two nonlinear predictive control algorithms based on structured neural models, Proceedings of the 3rd IFAC Workshop on Advanced Fuzzy and Neural Control, CD-ROM, Valenciennes, France, 2007.
41. Ławryńczuk M., Marusak P., Tatjewski P.: An efficient MPC algorithm integrated with economic optimisation for MIMO systems, Proceedings of the 13th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 295-302, Międzyzdroje, Poland, 2007.
42. Ławryńczuk M., Marusak P., Tatjewski P.: An integrated structure with economic optimisation and constrained model predictive control: an application to a MIMO pH reactor, Proceedings of the International Conference on Computer as a Tool EUROCON 2007, CD-ROM, pp. 785-792, Warszawa, 2007.
43. Ławryńczuk M., Marusak P., Tatjewski P.: Economic efficacy of multilayer constrained predictive control structures: an application to a MIMO neutralisation reactor, Proceedings of the 11th IFAC IFORS IMACS IFIP Symposium on Large Scale Systems Theory and Applications, CD-ROM, paper 93, Gdańsk, Poland, 2007.
44. Ławryńczuk M., Marusak P., Tatjewski P.: Efficient model predictive control integrated with economic optimisation, Proceedings of the 15th Mediterranean Conference on Control and Automation, CD-ROM, paper T27-001, Athens, Greece, 2007.
45. Ławryńczuk M., Marusak P., Tatjewski P.: Multilayer and Integrated Structures for Predictive Control and Economic Optimisation, Proceedings of the 11th 11th IFAC IFORS IMACS IFIP Symposium on Large Scale Systems Theory and Applications, CD-ROM, paper 60, Gdańsk, Poland, 2007.
46. Ławryńczuk M., Marusak P., Tatjewski P.: Set-point optimisation and predictive constrained control for fast feedback controlled processes, Proceedings of the 13th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 357-362, Międzyzdroje, Poland, 2007.

2006

47. Ławryńczuk M., Marusak P., Tatjewski P.: Integrating predictive control with steady-state optimisation, Proceedings of the 12th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2006, pp. 445-452, Międzyzdroje, Poland, 2006.
48. Tatjewski P., Ławryńczuk M., Marusak P.: Linking nonlinear steady-state and target set-point optimisation for model predictive control, Proceedings of the IEE International Control Conference ICC 2006, CD-ROM, Glasgow, United Kingdom, 2006.

2005

49. Ławryńczuk M., Marusak P., Tatjewski P.: On-line set-point optimisation for processed controlled by predictive control algorithms (in Polish), Proceedings of the 15th National Control Conference, s. 291-296, Warsaw, Poland, 2005.
50. Ławryńczuk M., Marusak P., Tatjewski P.: Optimising predictive range control for a distillation process, Proceedings of the 11th IEEE International Conference on Methods and Models in Automation and Robotics, pp. 379-384, Międzyzdroje, Poland, 2005.

2004

51. Ławryńczuk M., Tatjewski P.: A stable dual-mode type nonlinear predictive control algorithm based on on-line linearisation and quadratic programming, Proceedings of the 10th IEEE International Conference on Methods and Robotics, pp. 503-510, Międzyzdroje, Poland, 2004.

2003

52. Ławryńczuk M., Marusak P., Tatjewski P.: REGZA - software package for developing advanced control systems (in Polish), Proceedings of the Scientific and Technical Conference AUTOMATION 2003, Automatisation - Novelties and Perspectives, pp. 317-326, Warsaw, Poland, 2003.
53. Ławryńczuk M., Tatjewski P.: An iterative nonlinear predictive control algorithm based on linearisation and neural models, Proceedings of the European Control Conference ECC 2003, CD-ROM, Cambridge, United Kingdom, 2003.

2002

54. Ławryńczuk M.: Alternative cost-functions in nonlinear model predictive control algorithms (in Polish), Proceedings of the 14th National Control Conference, pp. 159-164, Zielona Góra, Poland, 2002.
55. Ławryńczuk M., Marusak P., Tatjewski P.: Software package for developing advanced control systems (in Polish), Proceedings of the 14th National Control Conference, pp. 483-488, Zielona Góra, Poland, 2002.
56. Ławryńczuk M., Tatjewski P.: A computationally efficient nonlinear predictive control algorithm based on neural models, Proceedings of the 8th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2002, pp. 781-786, Szczecin, Poland, 2002.
57. Tatjewski P., Ławryńczuk M., Marusak P.: Selected predictive control algorithms for nonlinear processes (in Polish), Proceedings of the 14th National Control Conference, pp. 145-152, Zielona Góra, Poland, 2002.

2001

58. Ławryńczuk M., Tatjewski P.: A multivariable neural predictive control algorithm, Proceedings of the 1st IFAC Workshop on Advanced Fuzzy-Neural Control AFNC 2001, pp. 191-196, Valencia, Spain, 2001.
59. Ławryńczuk M., Tatjewski P.: A nonlinear predictive control algorithm for processes modelled by means of neural networks, Proceedings of the 7th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2001, s. 489-494, Międzyzdroje, Poland, 2001.
60. Ławryńczuk M., Tatjewski P.: Multiregional neural models of dynamic processes - structure and identification algorithms (in Polish), Proceedings of the 5th National Conference Diagnostics of Industrial Processes DPP 01, pp. 145-148, Łagów Lubuski, Poland, 2001.

2000

61. Ławryńczuk M.: Applications of neural netwoks for modelling of nonlinear processes Wykorzystanie sieci neuronowych do modelowania silnie nieliniowych procesów (in Polish), Proceedings of the Scientific and Technical Conference AUTOMATION 2000, Automatisation - Novelties and Perspectives, pp. 293-300, Warsaw, Poland, 2000.
62. Ławryńczuk M., Tatjewski P.: Neural inverse modelling for disturbance compensation in a nonlinear plant control, Proceedings of the 6th IEEE IFAC International Conference on Methods and Models in Automation and Robotics, pp. 721-726, Międzyzdroje, Poland, 2000.

1999

63. Ławryńczuk M., Marusak P.: Illustration of some methods of solving constrained optimisation problems by the evolutionary strategy (in Polish), Proceedings of the 3rd National Conference on Evolutionary Algorithms and Global Optimisation, pp. 203-210, Potok Złoty, Poland, 1999.
64. Ławryńczuk M., Pułaczewski J.: Inferential measurements: the idea and industrial applications (in Polish), Proceedings of the Scientific and Technical Conference AUTOMATION 99, Automatisation - Novelties and Perspectives, s. 413-420, Warsaw, Poland, 1999.
65. Ławryńczuk M., Pułaczewski J.: Distillation column control based on the Wiener model (in Polish), Proceedings of the 13th National Control Conference, pp. 35-40, Opole, Poland, 1999.

Reports

2013

1. Ławryńczuk M., Winiarski T.: Simulation-based development of a control algorithm for “Sarkofag” robot (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 13-22, 2006.

2006

2. Ławryńczuk M., Marusak P.: Models of reference processes for investigation of predictive control structures with economic optimisation (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 06-03, 2006.

2004

3. Ławryńczuk M., Marusak P., Tatjewski P.: Optimising predictive control methods (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 04-16, 2004.
4. Ławryńczuk M., Tatjewski P., Marusak P.: Optimising predictive control algorithms (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no 04-04, 2004.

2003

5. Ławryńczuk M., Marusak P.: REGZA - software package for developing advanced control systems - manual for the user (ver. 3.0) (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, 2003.

2001

6. Ławryńczuk M., Marusak P.: REGZA - software package for developing advanced control systems - manual for the user (ver. 2.0) (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 01-14, 2001.

2000

7. Ławryńczuk M.: Models with separate steady-state part - identification and application in control, (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology no 00-03, 2000.
8. Ławryńczuk M., Marusak P.: REGZA - software package for developing advanced control systems - manual for the user (ver. 1.0) (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 00-11, 2000.

1998

9. Ławryńczuk M.: Modelling and simulation of a two-component distillation process for control system development (in Polish), Report of the Institute of Control and Computation Engineering, Warsaw University of Technology, no. 98-17, 1998.
10. Weigl Z., Oracz P., Dunalewicz A., Ławryńczuk M., Poświata A., Ilmurzyńska J., Plesnar M.: Development of engineering simulation system of chemical processes (in Polish), Report of the Institute of Industrial Chemistry and the Institute of Control and Computation Engineering, Warsaw University of Technology, 1998.
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