Fuzzy adaptation in a state space controller applied for a two-mass system

• Autorzy: Żychlewicz M. , Kamiński M.

Identyfikatory

DOI

10.5277/PED170111

• Języki publikacji: EN

Abstrakty

EN

Application of a state space controller for two-mass system has been examined. However, the classical version of the controller was modified in order to improve properties of the whole system. For this purpose fuzzy model was implemented as an adaptation element for the parameters. The theoretical description of the control structure, numerical tests and experimental results (using dSPACE1103 card) have been presented.

Słowa kluczowe

EN

fuzzy gain scheduling; state space controller; gains adaptation; two-mass drive

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2017
• Tom: Vol. 2 (37), No. 1
• Strony: 135--150
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Żychlewicz M.

• Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

autor

Kamiński M.

• Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• [1] WANG F., DAVARI S.A., CHEN Z., ZHANG Z., KHABURI D.A., RODRIGUEZ J., KENNEL R., Finite control set model predictive torque control of induction machine with a robust adaptive observer, IEEE Trans. Ind. Electron., 2017, 64(4), 2631-2641.
• [2] WU T.-Y., LAI C.-Y, CHEN S., An adaptive neural network compensator for decoupling of dynamic effects of a macro-mini manipulator, IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2015, 1427-1432.
• [3] CHEN T.-C., SHEU, T.-T., Model reference neural network controller for induction motor speed control, IEEE Trans. Energy Conv, 2002, 17(2), 157-163.
• [4] HUREZEANU A., NICOLA M., SACERDOTIANU D., Sensorless control using the model reference adaptive control estimator in electric drives with high dynamic, International Conference on Applied and Theoretical Electricity (ICATE), Romania, 2016.
• [5] YING-SHIEH K., MING-SHYAN W., CHUNG-CHUN H., Digital Hardware Implementation of Adaptive Fuzzy Controller for AC Motor Drive, Annual Conference of the IEEE Industrial Electronics Society (IECON), Taiwan, 2007, 1208-1213.
• [6] KOZAK S., State-of-the-art in control engineering, J. Electr. Syst. Inf. Technol., 2014, 1(1), 1-9.
• [7] SZABAT K., Direct and indirect adaptive control of a two-mass drive system. A comparison, Proc. IEEE International Symposium on Industrial Electronics, Cambridge, UK, 2008, 564-569.
• [8] CHANG I.-P., HUNG Y.-C., HWANG J.-C., LIN F.-J., TSAI M.-T., Digital signal processor-based probabilistic fuzzy neural network control of in-wheel motor drive for light electric vehicle, IET Electric Power Applications, 2012, 6(2), 47-61.
• [9] DWIVEDI S., SRIVASTAVA S.P., TAJNE S.K., Comparative Performance Analysis of Vector Controlled Induction Motor Drive for Neural Controller and DSP Implemented PI Controller, International Conference on Communication Systems and Network Technologies (CSNT), India, 2012, 274-281.
• [10] CAO J., NGUANG S.K., SALCIC Z., A Floating-Point FPGA-Based Self-Tuning Regulator, IEEE Trans. Ind. Electron., 2006, 53(2), 693-704.
• [11] KAMINSKI M., ORLOWSKA-KOWALSKA T., FPGA implementation of ADALINE-based speed controller in a two-mass system, IEEE Trans. Ind. Electr., 2013, 9(3), 1301-1311.
• [12] DOSTALEK P., DOLINAY Y., VASEK V., PEKAR L., Self-tuning digital PID controller implemented on 8-bit freescale microcontroller, Int. J. Math. Models Meth. Appl. Sci., 2010, 4(4), 274-281.
• [13] LANDAU I.D., LOZANO R., M’SAAD M., KARIMI A., Adaptive Control Algorithms, Analysis and Applications, Springer-Verlag, London 2011.
• [14] CHAIYATHAM T., NGAMROO I., Improvement of power system transient stability by pv farm with fuzzy gain scheduling of PID controller, IEEE Syst. J., 2014, 99, 1-8.
• [15] LIN F.-J., WAI R.-J., Adaptive Fuzzy-Neural-Network Control for Induction Spindle Motor Drive, IEEE Trans. En. Conv., 2002, 17(4), 507-513.
• [16] SEDHURAMAN K., HIMAVATHI S., MUTHURAMALINGAM A., Comparison of learning algorithms for neural network based speed estimator in sensorless induction motor drives, IEEE International Conference on Advances In Engineering, Science and Management (ICAESM), India, 2012, 196-202.
• [17] SZABAT K., ORLOWSKA-KOWALSKA T., Vibration suppression in a two-mass drive system using PI speed controller and additional feedbacks. Comparative study, IEEE Trans. Ind. Electr., 2007, 54(2), 1193-1206.
• [18] ZHANG G., FURSHO J., Speed control of two-inertia system by PI/PID control, IEEE Trans. Ind. Electr., 2000, 47(3), 603-609.
• [19] SZABAT K., TRAN-VAN T., KAMINSKI M., A modified fuzzy Luenberger observer for a two-mass drive system, IEEE Trans. Ind. Informatics, 2015, 11(2), 531-539.
• [20] SYED F.U., KUANG M.L., SMITH M.,OKUBO S., YING H., Fuzzy gain-scheduling proportional-integral control for improving engine power and speed behavior in a hybrid electric vehicle, IEEE Trans. Vehicular Techn., 2009, 58(1), 69-84.
• [21] KANTHAPHAYAO Y., CHUNKAG V., Current-sharing bus and fuzzy gain scheduling of proportional-integral controller to control a parallel-connected AC/DC converter, IET Power Electr., 2014, 7(10), 2525-2532.
• [22] POPA D.D., CRACIUNESCU A., KREINDLER L., A PI-fuzzy controller designated for industrial motor control applications, IEEE International Symposium on Industrial Electronics (ISIE), 2008, 949-954.
• [23] QIAO J., HAN H.-G., RUAN X., Research on fuzzy neural network based on lyapunov stability theory and its application, Proceedings of the IEEE International Conference on Networking, Sensing and Control, ICNSC, China, 2008.
• [24] ATIG A., DRUAUX F., LEFEBVRE D., ABDERRAHIM K., ABDENNOUR R.B., On Lyapunov stability of nonlinear adaptive control based on Neural Networks Emulator and Controller, 20th Mediterranean Conference on Control & Automation (MED), Spain, 2012, 272-277.
• [25] ZERAATKAR E., KARIMAGHAEE P., NOROOZI N., A quasi-PID backpropagation algorithm based on Lyapunov stability theory for neural network, 19th Iranian Conference on Electrical Engineering (ICEE), 2011.
• [26] ZHAO Z.-Y., TOMIZUKA M., ISAKA S., Fuzzy gain scheduling of PID controllers, IEEE Trans. Syst., Man, Cybern., 1993, 23(5), 1392-1398.
• [27] COSTA E.B., SERRA G.L.O., Fuzzy gain scheduling design based on multiobjective particle swarm optimization, Latin America Congress on Computational Intelligence, 2015.
• [28] MIRJALILIA S., MIRJALILIB S.M., LEWIS A., Grey Wolf Optimizer, Advances in Engineering Software, 69, 2014, 46-61.
• [29] LEITH D.J., LEITHEAD W.E., Survey of gain-scheduling analysis and design, Int. J. Control, 2000, 73(11), 1001-1025.