Artificial neural network based voltage controller for the single phase true sine wave inverter – a repetitive control approach

• Autorzy: Ufnalski B. , Grzesiak L.M.

Warianty tytułu

PL

Neuronowy regulator napięcia dla jednofazowego falownika o sinusoidalnym wyjściu – sterowanie procesem powtarzalnym

• Języki publikacji: EN

Abstrakty

EN

The paper presents novel error backpropagation based neurocontroller for true sine wave inverter. The controller is trained in on-line mode. Adaptation algorithm takes into account repetitiveness of the process to be controlled. The cost function evaluates performance of the controller over the whole period of the reference signal and the weights are updated only once a period of this signal. A model-free concept is employed and hence no neural (or of any other type) model of the plant is needed. Proposed topology does not limit its area of implementation to the discussed converter. The controller is capable to maintain a high-quality output voltage waveform in the presence of periodic disturbance caused by nonlinear loads.

PL

W artykule przedstawiono metodę budowy neuronowego regulatora napięcia dla falownika o sinusoidalnym napięciu wyjściowym. Regulator uczony jest w trybie on-line. Algorytm adaptacji wag sieci uwzględnia powtarzalność procesu poprzez odpowiednią definicję funkcji celu oraz uaktualnianie wag sieci raz na okres sygnału zadanego. Synteza układu regulacji nie wymaga identyfikowania modelu obiektu (podejście typu model-free). Zaproponowana topologia regulatora umożliwia jego wykorzystanie również do sterowania innymi procesami powtarzalnymi. Regulator pozwala na utrzymanie wysokiej jakości napięcia wyjściowego również dla okresowych obciążeń nieliniowych.

Słowa kluczowe

EN

adaptive neural voltage controller (ANVC); converted waves; iterative learning control; repetitive control

PL

regulator neuronowy; falownik; sterowanie procesami

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 4
• Strony: 14--18
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Ufnalski B.

• Warsaw University of Technology, Institute of Control and Industrial Electronics

autor

Grzesiak L.M.

• Warsaw University of Technology, Institute of Control and Industrial Electronics

Bibliografia

• [1] Finn S.D., A high performance inverter technology, architecture and applications, Proc. of the 8th Annual IEEE Applied Power Electronics Conference and Exposition APEC (1993), 556-560
• [2] Ryan M.J., Lorenz R.D., A high performance sine wave inverter controller with capacitor current feedback and “back-EMF” decoupling, Proc. of the 26th Annual IEEE Power Electronics Specialists Conference. PESC, 1 (1995), 507-513
• [3] Ryan M.J., Brumsickle W.E., Lorenz R.D., Control topology options for single-phase UPS inverters, Proc. of the Int. Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 1 (1996), 553-558
• [4] Gokhale K.P., Kawamura A., Hoft R.G., Dead beat microprocessor control of PWM inverter for sinusoidal output waveform synthesis, IEEE Trans. on Industry Applications, 23 (1987), n.5, 901-910
• [5] Mihalache L., DSP control method of single-phase inverters for UPS applications, Proc. of the 17th Annual IEEE Applied Power Electronics Conference and Exposition APEC, 1 (2002), 590-596
• [6] Tzou Y.-Y., DSP-based fully digital control of a PWM DC-AC converter for AC voltage regulation, Proc. of the 26th Annual IEEE Power Electronics Specialists Conference PESC, 1 (1995), 138-144
• [7] Kawamura A., Hoft R., Instantaneous feedback controlled PWM inverter with adaptive hysteresis, IEEE Trans. on Industry Applications, 20 (1984), n.4, 769-775
• [8] Carpita M., Marchesoni M., Experimental study of a power conditioning system using sliding mode control, IEEE Trans. on Power Electronics, 11 (1996), n.5, 731-742
• [9] Rogers E., Galkowski K., Owens D.H., Control Systems Theory and Applications for Linear Repetitive Processes (Lecture Notes in Control and Information Sciences 349), Springer, 2007.
• [10] Escobar G., Valdez A.A., Leyva-Ramos J., Mattavelli P., Repetitive-based controller for a UPS inverter to compensate unbalance and harmonic distortion, IEEE Trans. on Industrial Electronics, 54 (2007), n.1, 504-510
• [11] Zhou K., Wang D., Zhang B., Wang Y., Plug-in dual-modestructure repetitive controller for CVCF PWM inverters, IEEE Trans. on Industrial Electronics, 56 (2009), n.3, 784-791
• [12] Kulawinek R., Galkowski K., Grzesiak L., Kummert A., Iterative learning control method for a single-phase inverter with sinusoidal output voltage, Proc. IEEE Industrial Electronics Society 37th Annual Conf. IECON (2011), 1402-1407
• [13] Kaszewski A., Grzesiak L.M., Ufnalski B., Multi-oscillatory LQR for a three-phase four-wire inverter with L3nC output filter, Proc. IEEE Industrial Electronics Society 38th Annual Conf. IECON (accepted for publication) (2012)
• [14] Grzesiak L.M., Meganck V., Sobolewski J., Ufnalski B., On-line trained neural speed controller with variable weight update period for direct-torque-controlled AC drive, Proc. of the Power Electronics and Motion Control EPE-PEMC 12th Int. Conference (2006), 1127-1132
• [15] Grzesiak L.M., Sobolewski J., Energy flow control system based on neural compensator in the feedback path for autonomous energy source, Bulletin of the Polish Academy of Sciences – Technical Sciences, 54 (2006), nr.3, 335-340
• [16] Sobolewski J., Neuro-control system for converter based electrical energy source with combustion engine, Ph.D. Thesis, Warsaw University of Technology, 2008
• [17] Efe M.O., Neural network-based control, Chapter III in The Industrial Electronics Handbook: Intelligent Systems, CRC Press, 2011
• [18] Sarangapani J., Neural Network Control of Nonlinear Discrete-Time Systems, CRC Press, 2006
• [19] Kalkkuhl J., Hunt K.J., Zbikowski R., Dzielinski A. eds., Applications of neural adaptive control technology (World Scientific Series in Robotics and Intelligent Systems, 17), World Scientific, 1997
• [20] Tanomaru J., Takahashi Y., A general structure for neurocontrol of dynamical systems, Proc. of the IEEE IECON Int. Conf., 1 (1993), 304-309
• [21] Potocnik P., Grabec I., Adaptive self-tuning neurocontrol, Mathematics and Computers in Simulation, 51 (2000), nr.3-4, 201-207
• [22] Wang Y.-C., Chien C.-J., Lee D.-T., An output recurrent fuzzy neural network based iterative learning control for nonlinear systems, Proc. of the IEEE World Congress on Computational Intelligence, FUZZ-IEEE 2008, 1563-1569
• [23] Ufnalski B., Grzesiak L.M., Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle”, Bulletin of The Polish Academy of Sciences – Technical Sciences, 60 (2012), n.3