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Abstrakty
The paper describes a modification to the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) for the sine-wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI). The original PDPSRC algorithm assumes that the particle swarm optimizer (PSO) takes into account a performance index defined over the whole reference signal period. Each particle stores all the samples of the control signal, e.g. α = 200 samples for a controller working at 10 kHz and the reference frequency equal to 50 Hz. Therefore, the fitness landscape (i.e. the performance index) is -dimensional ( D), which makes optimization challenging. That solution can be categorized as the single-swarm one. It has been previously shown that the swarm controller does not suffer from long-term stability issues encountered in the classic iterative learning controllers (ILC). However, the convergence of the swarm has to be kept at a relatively low rate to enable successful exploitation in the D search space, which in turn results in slow responsiveness of the PDPSRC. Here a multi-swarm approach is proposed in which we divide a dynamic optimization problem (DOP) among less dimensional swarms. The reference signal period is segmented into shorter intervals and the control signal is optimized in each interval independently by separate swarms. The effectiveness of the proposed approach is illustrated with the help of numerical experiments on the CACF VSI with an output LC filter operating under nonlinear loads.
Słowa kluczowe
Rocznik
Tom
Strony
857--866
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
autor
- Faculty of Electrical Engineering, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland
autor
- Faculty of Electrical Engineering, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland
Bibliografia
- [1] 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).
- [2] 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.
- [3] Z. Cai, “Iterative learning control: algorithm development and experimental benchmarking”, Ph.D. Thesis, University of Southampton, Southampton, 2009.
- [4] Y. Shi, “Robustyfication in repetitive and iterative learning control”, Ph.D. Thesis, Columbia University, Columbia, 2013.
- [5] E. Rogers, K. Galkowski, and D.H. Owens, “Two decades of research on linear repetitive processes part I: theory”, Int. Workshop on Multidimensional Systems (nDS) 1, 1-6 (2013).
- [6] E. Rogers, K. Galkowski, W. Paszke, and D.H. Owens, “Two decades of research on linear repetitive processes part II: applications”, Int. Workshop on Multidimensional Systems (nDS) 1, 1-6 (2013).
- [7] 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).
- [8] Z. Cai, C. Freeman, E. Rogers, and P. Lewin, “Reference shift iterative learning control for a non-minimum phase plant”, American Control Conf. (ACC) 1, 558-563 (2007).
- [9] M. Heertjes, R. Rampadarath, and R. Waiboer, “Nonlinear Qfilter in the learning of nano-positioning motion systems”, Eur. Control Conf. (ECC) 1, CD-ROM (2009).
- [10] I. Rotariu, M. Steinbuch, and R. Ellenbroek, “Adaptive iterative learning control for high precision motion systems”, IEEE Trans. on Control Systems Technology 16 (5), 1075-1082 (2008).
- [11] M. Heertjes and T. Tso, “Nonlinear iterative learning control with applications to lithographic machinery”, Control Engineering Practice 15 (12), 1545-1555 (2007).
- [12] G. Escobar, P.Mattavelli, M. Hernandez-Gomez, and P.R.Martinez- Rodriguez, “Filters with linear-phase properties for repetitive feedback”, IEEE Trans. on Industrial Electronics 61 (1), 405-413 (2014).
- [13] A. Kaszewski, B. Ufnalski, and L.M. Grzesiak, “An LQ controller with disturbance feedforward for the 3-phase 4-leg true sine wave inverter”, IEEE Int. Conf. on Industrial Technology (ICIT) 1, 1924-1930 (2013).
- [14] 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 Transactions on Industrial Electronics 62 (1), 484-493 (2015).
- [15] G. Escobar, M. Hernandez-Gomez, G.A. Catzin, P.R. Martinez- Rodriguez, and A.A. Valdez-Fernandez, “Implementation of repetitive controllers subject to fractional delays”, Annual Conf. IEEE Industrial Electronics Society (IECON) 1, 5983-5988 (2013).
- [16] B. Ufnalski and L.M. Grzesiak, “Particle swarm optimization of an online trained repetitive neurocontroller for the sine-wave inverter”, Annual Conf. IEEE Industrial Electronics Society (IECON) 1, 6001-6007 (2013).
- [17] H. Deng, R. Oruganti, and D. Srinivasan, “Neural controller for UPS inverters based on B-spline network”, IEEE Trans. on Industrial Electronics 55 (2), 899-909 (2008).
- [18] Y.Q. Chen, K. Moore, and V. Bahl, “Learning feedforward control using a dilated B-spline network: frequency domain analysis and design”, IEEE Trans. on Neural Networks 15 (2), 355-366 (2004).
- [19] J. van de Wijdeven and O. Bosgra, “Stabilizability, performance, and the choice of actuation and observation time windows in iterative learning control”, IEEE Conf. on Decision and Control 1, 5042-5047 (2006).
- [20] J. van de Wijdeven and O.H. Bosgra, “Using basis functions in iterative learning control: analysis and design theory”, Int. J. Control 83 (4), 661-675 (2010).
- [21] J. Bolder and T. Oomen, “Rational basis functions in iterative learning control-with experimental verification on a motion system”, IEEE Trans. on Control Systems Technology 23 (2), 722-729 (2015).
- [22] T. Blackwell, J. Branke, and X. Li, “Particle swarms for dynamic optimization problems”, Swarm Intelligence, Natural Computing Series, pp. 193-217, Springer, Berlin, 2008.
- [23] A.P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, Wiley, London, 2005.
- [24] P. Biernat, B. Ufnalski, and L.M. Grzesiak, “Direct particle swarm repetitive controller with time-distributed calculations for real time implementation”, IEEE Int. Conf. on Intelligent Systems (IS) 1, DOI: 10.1007/978-3-319-11313-5 44 (2014).
- [25] J. Riget and J.S. Vesterstrøm, “A diversity-guided particle swarm optimizer - the ARPSO”, Technical Report, Aarhus Universitet, Denmark, 2002.
- [26] R.C. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence, Morgan Kaufmann Publishers, Berlin, 2001.
- [27] X. Cui, J.S. Charles, and T.E. Potok, “A simple distributed particle swarm optimization for dynamic and noisy environments”, Int. Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) 1, 89-102 (2009).
- [28] G. Franklin, D. Powell, and M. Workman, Digital Control of Dynamic Systems, Prentice Hall, New Jersey, 1997.
- [29] B. Ufnalski and L.M. Grzesiak, “Feedback and feedforward repetitive control of single-phase UPS inverters - an online particle swarm optimization approach”, Technical Report, Scientific Reports of the Cologne University of Applied Sciences, Cologne, 2014.
- [30] B. Ufnalski and P. Biernat, “Time-distributed particle swarm repetitive control algorithm”, MATLAB Central, http://www.mathworks.com/matlabcentral/fileexchange/48967-time-distributed-particle-swarm-repetitive-control-algorithm (2014).
- [31] 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).
Typ dokumentu
Bibliografia
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