Repetitive neurocontroller with disturbance feedforward path active in the pass-to-pass direction for a VSI inverter with an output LC filter

Open access

Abstract

An enhancement to the previously developed repetitive neurocontroller (RNC) is discussed and investigated in the paper. Originally, the time-base generator (TBG) has been used to produce the only input signal for the neural approximator. The resulting search space makes the dynamic optimization problem (DOP) of shaping the control signal solvable with the help of a function approximator such as the feed-forward neural network (FFNN). The plant under consideration, i.e. a constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an output LC filter, is assumed to be equipped with the disturbance load current sensor to enable implementation of the disturbance feed-forward (pDFF) path as a part of the non-repetitive subsystem acting in the along the pass p-direction. An investigation has been undertaken to explore potential benefits of using this signal also as an additional input for the RNC to augment the approximation space and potentially enhance the convergence rate of the real-time search process. It is numerically demonstrated in the paper that the disturbance feed-forward path active in the pass-to-pass k-direction (kDFF) improves the dynamics of the repetitive part as well indeed.

[1] Y. Wang, F. Gao, and F.J. Doyle III, “Survey on iterative learning control, repetitive control, and run-to-run control”, J. Process Control 19 (10), 1589-1600 (2009).

[2] J. Wallén, “Estimation-based iterative learning control”, Ph.D. thesis, Linköping University, Linköping, 2011.

[3] E. Rogers, K. Galkowski, W. Paszke, and D.H. Owens, “Two decades of research on linear repetitive processes part II: Applications”, Multidimensional Systems (nDS) 2013, Proc. 8th Int. Workshop on 1, 1-6 (2013).

[4] ABB Group, “ABB at the iREX 2013 - robotic laser cutting solutions”, abb.com (2013).

[5] B. Ufnalski, L.M. Grzesiak, and A. Kaszewski, “Advanced control and optimization techniques in AC drives and DC/AC sine wave voltage inverters: selected problems”, in: Advanced and Intelligent Control in Power Electronics and Drives, Studies in Computational Intelligence, eds. T. Orlowska-Kowalska, F. Blaabjerg, and J. Rodriguez, vol. 531, pp. 303-333, Springer, Berlin, 2014.

[6] J. Farrell and W. Baker, “Learning control systems”, in: An Introduction to Intelligent and Autonomous Control, eds. P.J. Antsaklis and K.M. Passino, Kluwer Academic Publishers, London, 1993.

[7] B. Ufnalski, A. Kaszewski, and L.M. Grzesiak, “Particle swarm optimization of the multioscillatory LQR for a threephase four-wire voltage-source inverter with an LC output filter”, IEEE Trans. Industrial Electronics 62 (1), 484-493 (2015).

[8] M. Monfared, “A simplified control strategy for single-phase UPS inverters”, Bull. Pol. Ac.: Tech. 62 (2), 367-373 (2014).

[9] B. Ufnalski and L.M. Grzesiak, “Artificial neural network based voltage controller for the single phase true sine wave inverter - a repetitive control approach”, Electrical Review (Przegląd Elektrotechniczny) 89 (4), 14-18 (2013).

[10] B. Ufnalski and L.M. Grzesiak, “Particle swarm optimization of an online trained repetitive neurocontroller for the sine-wave inverter”, Industrial Electronics Society, IECON 2013 - 39th Annual Conf. IEEE, 6003-6009 (2013).

[11] H. Deng, R. Oruganti, and D. Srinivasan, “Neural controller for UPS inverters based on B-spline network”, IEEE Trans. Industrial Electronics 55 (2), 899-909 (2008).

[12] B. Ufnalski and L.M. Grzesiak, “A plug-in direct particle swarm repetitive controller for a single-phase inverter”, Electrical Review (Przegląd Elektrotechniczny) 90 (6), 6-11 (2014).

[13] B. Ufnalski and L.M. Grzesiak, “Feedback and feedforward repetitive control of single-phase UPS inverters - an online particle swarm optimization approach”, Technical Report 1/2014, Scientific Reports of the Cologne University of Applied Sciences, Köln, 2014.

[14] B. Ufnalski and L.M. Grzesiak, “A performance study on synchronous and asynchronous update rules for a plug-in direct particle swarm repetitive controller”, Archives of Electrical Engineering 63 (4), 635-646 (2014).

[15] B. Ufnalski, L.M. Grzesiak, and K. Galkowski, “Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform”, Bull. Pol. Ac.: Tech. 61 (3), 649-660 (2013).

[16] R.W. Longman, “Iterative/repetitive learning control: learning from theory, simulations, and experiments”, in: Encyclopedia of the Sciences of Learning, pp. 1652-1657, Springer, New York, 2012.

[17] H. Elci, R.W. Longman, M.Q. Phan, J.-N. Juang, and R. Ugoletti, “Simple learning control made practical by zero-phase filtering: applications to robotics”, IEEE Trans. Circuits and Systems I: Fundamental Theory and Applications 49 (6), 753-767 (2002).

[18] Y. Shi, “Robustification in repetitive and iterative learning control”, Ph.D. Thesis, Columbia University, New York, 2013.

[19] M.H.A. Verwoerd, “Iterative learning control - a critical review”, Ph.D. Thesis, University of Twente, Enschede, 2005.

[20] B. Ufnalski, “Plug-in direct particle swarm repetitive controller”, MATLAB Central, http://www.mathworks.com/matlabcentral/fileexchange/47847-plug-in-direct-particle-swarm-repetitive-controller (2014).

[21] K. Hornik, “Approximation capabilities of multilayer feedforward networks”, Neural Networks 4 (2), 251-257 (1991).

[22] M. Kaminski and T. Orlowska-Kowalska, “Application of neural network with adaptive interaction for speed control of the drive system with elastic joint”, IEEE Int. Symp. Industrial Electronics (ISIE) 1, 1-6 (2013).

[23] M. Kaminski and T. Orlowska-Kowalska, “FPGA implementation of ADALINE-based speed controller in a two-mass system”, IEEE Trans. Industrial Informatics 9 (3), 1301-1311 (2013).

[24] T. Orlowska-Kowalska and M. Kaminski, “Adaptive neurocontrollers for drive systems: Basic concepts, theory and applications”, Advanced and Intelligent Control in Power Electronics and Drives, Studies in Computational Intelligence 531, 269-302 (2014).

[25] T. Pajchrowski, “Adaptive neural speed controller for servodrive trained online”, 18th Int. Conf. Methods and Models in Automation and Robotics (MMAR) 1, 183-188 (2013).

[26] T. Pajchrowski and K. Zawirski, “Application of artificial neural network for adaptive speed control of PMSM drive with variable parameters”, COMPEL: Int. J. Computation and Mathematics in Electrical and Electronic Engineering 32 (4), 1287-1299 (2013).

[27] T. Pajchrowski, “Application of artificial neural network for speed control of servodrive with variable parameters”, Mechatronics 2013, International Publishing, London, 693-700 (2014).

[28] J. Sobolewski and L.M. Grzesiak, “Neuro-control system for converter based electrical energy source - test performed in laboratory setup with combustion engine emulator”, IEEE Int. Conf. Industrial Technology (ICIT), 1603-1608 (2013).

[29] B. Ufnalski and L.M. Grzesiak, “Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle”, Bull. Pol. Ac.: Tech. 60 (3), 661-667 (2012).

[30] L.M. Grzesiak, V. Meganck, J. Sobolewski, and B. Ufnalski, “On-line trained neural speed controller with variable weight update period for direct-torque-controlled AC drive”, 12th Int. Power Electronics and Motion Control Conf. (EPE-PEMC) 3, 1127-1132 (2006).

[31] L.M. Grzesiak and J. Sobolewski, “Energy flow control system based on neural compensator in the feedback path for autonomous energy source”, Bull. Pol. Ac.: Tech. 54 (3), 335-340 (2006).

[32] D.L. Elliott, “A better activation function for artificial neural networks”, Institute for Systems Research (ISR) Technical Report 93-8, CD-ROM (1993).

[33] B. Ufnalski and L.M. Grzesiak, “Selected methods in angular rotor speed estimation for induction motor drives”, IEEE Int. Conf. Computer as a Tool (EUROCON) 2, 1764-1771 (2007).

[34] M. Kaminski, T. Orlowska-Kowalska, and K. Szabat, “Neural speed controller based on two state variables applied for a drive with elastic connection”, 16th Int. Power Electronics and Motion Control Conference and Exposition (PEMC), 610-615 (2014).

[35] B.W. Bequette, Process Control: Modeling, Design, and Simulation, Prentice Hall PTR, London, 2003.

[36] B. Ufnalski, “Repetitive neurocontroller with disturbance feedforward”, MATLAB Central, http://www.mathworks.com/matlabcentral/fileexchange/47867-repetitive-neurocontrollerwith- disturbance-feedforward (2014).

[37] K. Sozański, Digital Signal Processing in Power Electronics Control Circuits, Power Systems, Springer-Verlag, London, 2013.

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IMPACT FACTOR 2016: 1.156
5-year IMPACT FACTOR: 1.238

CiteScore 2016: 1.50

SCImago Journal Rank (SJR) 2016: 0.457
Source Normalized Impact per Paper (SNIP) 2016: 1.239

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