scholarly journals Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

Engineering ◽  
2016 ◽  
Vol 08 (01) ◽  
pp. 1-10
Author(s):  
Waleed I. Hameed ◽  
Ahmed S. Kadhim ◽  
Ali Abdullah K. Al-Thuwaynee
Keyword(s):  
Neural Network ◽  
Dc Motor ◽  
Social Spider ◽  
Field Weakening ◽  
Social Spider Algorithm

Nonlinear MIMO Speed Sensorless Controller for DC Motor Field Weakening

2000 ◽  
Vol 28 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Zuo Z. Liu, Fang L. Luo, Prof. Muhammad
Keyword(s):  
Dc Motor ◽  
Speed Sensorless ◽  
Field Weakening

Microprocessor-based DC motor drive with spillover field weakening

1991 ◽  
Vol 38 (6) ◽  
pp. 469-475 ◽  
Author(s):  
J.G. Kettleborough ◽  
I.R. Smith ◽  
V.V. Vadher ◽  
F.L.M. Antunes
Keyword(s):  
Dc Motor ◽  
Motor Drive ◽  
Field Weakening

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

Neural network-based optimal control of a DC motor positioning system

2013 ◽  
Vol 7 (1/2) ◽  
pp. 83 ◽  
Author(s):  
Maksym Khomenko ◽  
Volodymyr Voytenko ◽  
Yuriy Vagapov
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Dc Motor ◽  
Positioning System
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