A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller

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Abstract

In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO) to solve in on-line mode a dynamic optimization problem (DOP) related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs), is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.

[1] Ufnalski B., Grzesiak L.M., Kaszewski A., Advanced control and optimization techniques in AC drives and DC/AC sine wave voltage inverters: selected problems. Advanced and intelligent control in power electronics and drives, Springer (2014).

[2] Ufnalski B., Grzesiak L.M., Galkowski K., Particle swarm optimization of an iterative learning controller for the single-phase inverter with sinusoidal output voltage waveform. Bulletin of the Polish Academy of Sciences – Technical Sciences 61(3): 649-660 (2013).

[3] Longman R.W., Iterative/repetitive learning control: learning from theory, simulations, and experiments. Encyclopedia of the Sciences of Learning, pp. 1652-1657, Springer US (2012).

[4] Shi Y., Robustification in repetitive and iterative learning control. Ph.D. thesis, Columbia University, USA (2013).

[5] Deng H., Oruganti R., Srinivasan D., Neural controller for UPS inverters based on B-spline network. Industrial Electronics, IEEE Transactions on 55(2): 899-909 (2008).

[6] Ufnalski B., Grzesiak L.M., Artificial neural network based voltage controller for the single phase true sine wave inverter – a repetitive control approach. Electrical Review (Przegląd Elektrotechniczny), in English, 84(4): 14-18 (2013).

[7] Ufnalski B., Grzesiak L.M., Particle swarm optimization of an online trained repetitive neurocontroller fot the sine-wave inverter. Proc. of IEEE Industrial Electronics Society Annual Conference IECON 2013, pp. 6001-6007 (2013).

[8] Ufnalski B., Grzesiak L.M., A plug-in direct particle swarm repetitive controller for a single-phase inverter. Electrical Review 90(6): 6-11 (2014) (in English).

[9] Ufnalski B., Grzesiak L.M., Feedback and feedforward repetitive control of single-phase UPS inverters – an online particle swarm optimization approach. Scientific Reports of the Cologne University of Applied Sciences 1: 59-67 (2014).

[10] Riget J., Vesterstrom J.S., A diversity-guided particle swarm optimizer – the ARPSO. Tech. Rep. of Aarhus University, Denmark (2002).

[11] Cui X., Charles J.S., Potok T.E., A simple distributed particle swarm optimization for dynamic and noisy environments. Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), Studies in Computational Intelligence 236: 89-102, Springer (2009).

[12] Rada-Vilela J., Zhang M., Seah W., A performance study on synchronous and asynchronous updates in particle swarm optimization. Proc. of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO’11), pp. 21-28 (2011).

Archives of Electrical Engineering

The Journal of Polish Academy of Sciences

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CiteScore 2016: 0.71

SCImago Journal Rank (SJR) 2016: 0.238
Source Normalized Impact per Paper (SNIP) 2016: 0.535

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