Center of Technical and Medical Diagnostics Zaawansowane systemy wytwarzania i materiały
Center of Technical and Medical Diagnostics was found in 2015 at the University of Zielona Góra. The experience of the center research group is well recognized in the scientific community due to its successive development during the last 25 years. The activity of the center is oriented towards commercialization of research results as well as conducting research and development projects in the area of practical applications of technical and medical diagnostics.
Center of Technical and Medical Diagnostics is focused on a cooperation within the projects oriented towards:
• advanced control and monitoring techniques;
• fault diagnosis and fault-tolerant control;
• processing and analysis of medical images;
• pattern recognition and medical diagnostics.
Areas of special interest of the center are:
• application of analytical and soft computing techniques to technical diagnostics;
• intelligent control systems and fault-tolerant control;
• control of production systems with modern agile manufacturing techniques;
• analysis and recognition of medical images;
• medical diagnostics based on analytical and soft computing approaches;
• development of intelligent software platforms for technical and medical diagnostics.
Technical and Medical Diagnostics Center
Tel. +48 68 3282422
Contact person: Marcin Witczak
Institute of Control and Computation Engineering
University of Zielona Góra
ul. Podgórna 50
65-246 Zielona Góra
POLAND
e-mail: M.Witczak@issi.uz.zgora.pl
(Polski) 1. Projekty
- Hybrydowe sterowanie tolerujące uszkodzenia dla systemów nieliniowych z zastosowaniem metod analitycznych i technik obliczeń inteligentnych, Narodowe Centrum Nauki, Kierownik: prof. dr hab. inż. Józef Korbicz, 2014-2017, UMO-2013/11/B/ST7/01110.
- Sztuczne sieci neuronowe w odpornej diagnostyce uszkodzeń i sterowaniu układów nieliniowych, Narodowe Centrum Nauki, Kierownik: dr inż. Marcel Luzar, 2015-2018, UMO-2014/15/N/ST7/00749.
- Zaawansowane metody sterowania i diagnostyki z wykorzystaniem technik sztucznej inteligencji w działaniu procesów przemysłowych o szybkiej dynamice, Narodowe Centrum Nauki, Kierownik: dr inż. Andrzej Czajkowski, 2012-2015, UMO-2012/07/N/ST7/03316.
- Predykcyjne sterowanie tolerujące uszkodzenia w nieliniowych układach automatyki. Narodowe Centrum Nauki, Kierownik: prof. dr hab. inż. Józef Korbicz, 2011-2014, NN514678440.
2. Publikcacje
Monografie
- Marcin Witczak, Fault diagnosis and fault-tolerant control strategies for non-linear systems, Switzerland 2014, 229 s., Springer International Publishing, DOI: 10.1007/978-3-319-03014-2.
- Marcin Mrugalski, Advanced neural-network based computational schemes for robust fault diagnosis, Berlin 2014, 182 s., Springer-Verlag, ISBN: 9783319015460
- Marcel Luzar, Dynamic artificial neural networks in designing robust fault diagnosis systems, Zielona Góra 2016, 174 s., University of Zielona Góra Press, ISBN: 978837842282
- Andrzej Czajkowski, Fault tolerant control system design using dynamic neural networks, Zielona Góra 2016, 140 s., University of Zielona Góra Press, ISBN: 978837842263
Pozostałe publikacje
- Buciakowski, M., Witczak, M., Puig, V., Rotondo, D., Nejjari, F., Korbicz, J.: A bounded-error approach to simultaneous state and actuator fault estimation for a class of nonlinear systems, Journal of Process Control, 2017, Vol. 52, pp.14-25
- Witczak, P., Patan, K., Witczak, M., Mrugalski, M.: A neural network approach to simultaneous state and actuator fault estimation under unknown input decoupling, Neurocomputing, 2017, pp.1-11, DOI:10.1016/j.neucom.2016.10.076
- Buciakowski, M., Witczak, M., Mrugalski, M., Theilliol, D.: A quadratic boundedness approach to robust DC motor fault estimation, Control Engineering Practice, 2017, Vol. 66, pp.181-194
- Witczak, M., Rotondo, D., Puig, V., Nejjari, F., Pazera, M.: Fault estimation of wind turbines using combined adaptive and parameter estimation schemes, International Journal of Adaptive Control and Signal Processing, 2017, pp.1-19, DOI: 10.1002/acs.2792
- Witczak, M. Buciakowski, V. Puig, D. Rotondo, F. Nejjari: An LMI approach to robust fault estimation for a class of nonlinear systems, International Journal of Robust and Nonlinear Control, 2015, DOI:10.1002/rnc.3365
- Rotondo, V. Puig, F. Nejjari, M. Witczak: Automated generation and comparison of Takagi–Sugeno and polytopic quasi-LPV models, Fuzzy Sets and Systems, Vol. 277, No.1, pp. 44-64, 2015, DOI: 10.1016/j.fss.2015.02.002
- Witczak, D. Rotondo, V. Puig, P. Witczak: A practical test for assessing the reachability of discrete-time Takagi-Sugeno fuzzy systems, Journal of the Franklin Institute, Vol. 352, No. 12, pp. 5936-5951, 2015, DOI:10.1016/j.jfranklin.2015.10.006
- Witczak, M. Buciakowski, Ch. Aubrun: Predictive actuator fault-tolerant control under ellipsoidal bounding, International Journal of Adaptive Control and Signal Processing, 2015, DOI: 10.1002/acs.2567
- Witczak, M. Mrugalski, J. Korbicz:Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace, Neural Processing Letters, Vol. 42, No. 1, pp.71-87, 2015, DOI: 10.1007/s11063-014-9387-0
- Seybold, M. Witczak, P. Majdzik, R. Stetter: Towards robust predictive fault-tolerant for a battery assembly system, International Journal of Applied Mathematics and Computer Science, Vol. 25, No. 4, pp. 849-862, 2015, DOI: 10.2478/amcs-2015-0061
- Witczak, M. Witczak, J. Korbicz, Ch. Aubrun: A robust predictive actuator fault-tolerant control scheme for Takagi-Sugeno fuzzy systems, Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 4, pp. 977-987, 2015, DOI: 10.1515/bpasts-2015-0111
- Mrugalski, M. Luzar, M. Pazera, M. Witczak, C. Aubrun: Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system, ISA Transactions, Vol. 61, pp. 318-328, 2016, DOI: 10.1016/j.isatra.2016.01.002.
- Patan, Two stage neural network modelling for robust model predictive control, ISA Transactions 2017, s. 1—10, DOI: 10.1016/j.isatra.2017.10.011
- Patan, Neural network-based model predictive control: fault tolerance and stability, IEEE Transactions on Control Systems Technology .- 2015, Vol. 23, no. 3, s. 1147—115, DOI: 10.1109/TCST.2014.2354981
- Czajkowski, K. Patan, M. Szymański, Application of the state space neural network to the fault tolerant control system of the PLC-controlled laboratory stand, Engineering Applications of Artificial Intelligence .- 2014, Vol. 30, s. 168–178 DOI: 10.1016/j.engappai.2014.01.017.
