UAV Power Component—DC Brushless Motor Design With Merging Adjacent-Disturbances and Integrated-Dispatching Pigeon-Inspired Optimization

As a widely used power component of unmanned aerial vehicle, the direct current (dc) brushless motor has the prominent advantages of high efficiency, long period, low noise, and better speed–torque. In this paper, a novel parameter design method is presented for a dc brushless motor. To maximize the efficiency of the power system, the pigeon-inspired optimization algorithm is utilized by converting the dc brushless motor parameter design problem to an optimization problem. A modified model is proposed to avoid falling into the local optimal value and increase the population diversity by introducing the adjacent-disturbances and integrated-dispatching strategies. Comparative experiments are conducted to verify the convergence speed and the effectiveness of the proposed method.

[1]  Kai Zhang Hydraulic Straightener Control Optimizer Based on Particle Swarm with Classification Learning , 2017 .

[2]  T. C. Bora,et al.  Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem , 2012, IEEE Transactions on Magnetics.

[3]  Haibin Duan,et al.  Robust attitude control for reusable launch vehicles based on fractional calculus and pigeon-inspired optimization , 2017, IEEE/CAA Journal of Automatica Sinica.

[4]  Douglas H. Werner,et al.  The Wind Driven Optimization Technique and its Application in Electromagnetics , 2013, IEEE Transactions on Antennas and Propagation.

[5]  G. Crevecoeur,et al.  A Two-Level Genetic Algorithm for Electromagnetic Optimization , 2010, IEEE Transactions on Magnetics.

[6]  Wang Hai-feng Pulsating Torque Minimization Techniques for Brushless DC Motor , 2002 .

[7]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[8]  Xiaohua Wang,et al.  Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Yuhui Shi,et al.  Brain Storm Optimization Algorithm , 2011, ICSI.

[10]  Ying Guan,et al.  A NOVEL IGA-EDSPSO HYBRID ALGORITHM FOR THE SYNTHESIS OF SPARSE ARRAYS , 2009 .

[11]  Jing Zhao,et al.  Analytical Solution of the Magnetic Field and EMF Calculation in Ironless BLDC Motor , 2016, IEEE Transactions on Magnetics.

[12]  Haibin Duan,et al.  Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning , 2014, Int. J. Intell. Comput. Cybern..

[13]  Helon V. H. Ayala,et al.  Multiobjective Krill Herd Algorithm for Electromagnetic Optimization , 2016, IEEE Transactions on Magnetics.

[14]  Bo Zhang,et al.  Three-Dimensional Path Planning for Uninhabited Combat Aerial Vehicle Based on Predator-Prey Pigeon-Inspired Optimization in Dynamic Environment , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  S.L. Ho,et al.  A modified ant colony optimization algorithm modeled on tabu-search methods , 2006, IEEE Transactions on Magnetics.

[16]  Stephane Brisset,et al.  Analytical model for the optimal design of a brushless DC wheel motor , 2005 .

[17]  Jong-Wook Kim,et al.  Interstellar Search Method With Mesh Adaptive Direct Search for Optimal Design of Brushless DC Motor , 2016, IEEE Transactions on Magnetics.

[18]  L. Lebensztajn,et al.  Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach , 2012, IEEE Transactions on Magnetics.

[19]  M. Samami,et al.  Optimal Design of a Brushless DC Motor, by Cuckoo Optimization Algorithm (RESEARCH NOTE) , 2017 .

[20]  L dos Santos Coelho,et al.  Gaussian Artificial Bee Colony Algorithm Approach Applied to Loney's Solenoid Benchmark Problem , 2010, IEEE Transactions on Magnetics.

[21]  Chang-Hwan Im,et al.  Hybrid genetic algorithm for electromagnetic topology optimization , 2003 .

[22]  W. Renhart,et al.  Pareto optimality and particle swarm optimization , 2004, IEEE Transactions on Magnetics.

[23]  Y. Perriard,et al.  Optimization Design of a Segmented Halbach Permanent-Magnet Motor Using an Analytical Model , 2009, IEEE transactions on magnetics.

[24]  P. Di Barba Multi-objective wind-driven optimisation and magnet design , 2016 .

[25]  U. Baumgartner,et al.  Particle swarm optimization - mass-spring system analogon , 2002 .

[26]  Yuhui Shi,et al.  Predator–Prey Brain Storm Optimization for DC Brushless Motor , 2013, IEEE Transactions on Magnetics.

[27]  M. Davison,et al.  Magnetoreception and its trigeminal mediation in the homing pigeon , 2004, Nature.