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Efficiency analysis of parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

Szczepański Rafał, Tarczewski Tomasz, Grzesiak Lech M.

Poznan University of Technology Academic Journals. Electrical Engineering

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2019

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No. 100

145--156

EN

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors are common in personal computers. These processors can be used to reduce computation time by using parallel computing on multiple cores. The most popular software applied to simulate behavior of the plant is MATLAB/Simulink. A single simulation of Simulink model cannot be computed by multiple cores, but there are many engineering problems, that require a multiple simulation of the same model with different parameters. In these problems, the parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive. In order to obtain the optimal coefficients of the controller, an Artificial Bee Colony optimization algorithm is employed.

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Zastosowanie algorytmów rojowych do optymalizacji parametrów w modelach układów regulacji

Tomera M.

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2015

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Nr 46

97--102

PL

W pracy przedstawione zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Dla tych algorytmów przygotowane zostało oprogramowanie w Matlabie, pozwalające na optymalizację parametrów poszukiwanych modeli matematycznych, wyznaczanych na podstawie przeprowadzonych testów identyfikacyjnych lub na optymalizację parametrów regulatorów zastosowanych w modelach matematycznych układów sterowania.

EN

The paper presents the swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Ant colony optimization (ACO) based upon the observation of the behavior of ant colonies looking for food in the surrounding anthill. Feeding ants it is based on finding the shortest path transitions between a food source and the anthill. In the process of foraging ants on their paths crossing from the nest to a food source and back, they leave a pheromone trail. The work presents also the modified ant colony algorithm (MACO). This algorithm is based on searching the solution space surrounded by the best solution obtained in the previous iteration. If you find a local minimum, the proposed algorithm uses pheromone to find a new solution space, while retaining the position information current local minimum. The artificial bee colony algorithm is one of the well-known swarm intelligence algorithms. In the past decade there has been created several different algorithms based on the observation of the behavior of cooperative bees. Among them, the most frequently analyzed and used is bee algorithm proposed in 2005 by Dervis Karaboga and was be used in the proposed paper. The particle swarm optimization algorithm (PSO) is based on adjusting the change speed of the moving particles to a speed of particles movement in the neighborhood. Particle optimization algorithm is one of the computational techniques derived on the basis of swarm behavior such as flocks of birds and schools of fish, which is the basis for the functioning of the exchange of information to enable them to cooperate. It was noticed that the animals in the herd tend to maintain the optimum distance from their neighbors, by appropriate adjustment of their speed. This method allows the synchronous and collision-free motion, often accompanied by sudden changes of direction and due to the rearrangement of the optimal formation. For these algorithms has been prepared the software in Matlab, allowing to optimization of the mathematical models designated on the basis of the carried out identification tests and control parameters used in the mathematical model of the control system.

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A hybrid algorithm combining auto-encoder network with Sparse Bayesian Regression optimized by Artificial Bee Colony for short-term Wind Power Forecasting

Li Y., Yang R.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 2a

223--228

EN

To forecast the short-term wind power precisely, this paper proposes a hybrid strategy which consists of a nonlinear dimensionality reduction component by auto-encoder network and a forecasting component based on Sparse Bayesian Regression optimized by Artificial Bee Colony Optimization. The proposed model can predict wind power curve per hour with a lead time of 3hours. Finally, an experiment is conducted to test the effectiveness of the forecasting model based on the detailed data from a wind farm in China.

PL

W artykule zaproponowano hybrydową metodę przewidywania krzywej prędkości wiatru w okresie kolejnej godziny. Algorytm bazuje na nieliniowej redukcji wymiarowości przez sieć auto-enkoderową (sztuczną sieć neuronową) oraz na elemencie przewidującym, opartym na rzadkiej regresji Bayesa (ang. Sparse bayesian Regression) zoptymalizowanej metodą sztucznej koloni pszczół.

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Updated food positions in artificial bee colony algorithm for supply chain management

Sharma T. K., Pant M.

Computer Methods in Materials Science

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2013

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Vol. 13, No. 1

113--119

EN

The present study deals with the management of supply chain using an updated Artificial Bee Colony (ABC) algorithm named UABC. UABC employs a linear combination of Gaussian and Cauchy distributions to update the candidate food positions from the older ones in memory. Optimization of a supply chain model is an integer programming problem or a constrained integer-mixed problem, for which suitable modifications are done in the algorithm. Statistical analysis of the proposed variant when compared with three ABC based algorithms indicates its efficiency and validity.

PL

Opisane w pracy badania dotyczą zarządzania łańcuchem dostaw przy zastosowaniu zmodyfikowanego algorytmu sztucznej kolonii pszczół (ang: Artificial Bee Colony - ABC), zwanego UABC (ang. Updated Artificial Bee Colony). Algorytm ten wykorzystuje liniową kombinację rozkładów Gaussa i Cauchy’ego w celu uaktualnienia pozycji rozmieszczenia pożywienia. Optymalizacja modelu łańcucha dostaw jest problemem programowania całkowitoliczbowego lub problemem programowania mieszanego z ograniczeniami. Opracowany algorytm UABC uwzględnia te informacje. Analiza statystyczna algorytmu UABC, w porównaniu z trzema innymi algorytmami opartymi na ABC, wskazuje na jego wydajność i poprawność.