Controller Tuning Using a Cauchy Mutated Artificial Bee Colony Algorithm

  • Anguluri Rajasekhar
  • Ajith Abraham
  • Ravi Kumar Jatoth
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)


Permanent Magnet Synchronous Motors (PMSM) are immensely popular because they can meet the huge capacity needs of industrial applications. Speed regulation of PMSM Drives with conventional Proportional-Integral (PI) regulator reduces the speed control precision because of the disturbances in Motor and load characteristics, leading to poor performance of whole system. The values so obtained may not give satisfactory results in a wide range of speed. In this research, we considered the Mathematical model of speed controller for controlling the speed, which can be formulated as an optimization problem subject to various constraints imposed due to motor and other limitation factors. For solving this problem we used a modified version of Artificial Bee Colony (ABC) algorithm known as Cauchy Mutation ABC (C-ABC).We first illustrate the proposed method using various standard benchmark functions and then it is used for tuning PI controller for speed regulation in PMSM drive. Empirical results obtained are compared with the basic version of ABC, which clearly indicates the superior performance of the C-ABC algorithm.


Permanent Magnet Synchronous Motor Controller Tuning Bacterial Forage Optimization Cauchy Mutate Failure Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bose, B.K.: Power Electronics and Motion Control-Technology Status and Recent Trends. IEEE Trans. Ind. App. 29, 902–909 (1993)CrossRefGoogle Scholar
  2. 2.
    Lipo, T.A.: Recent Progress in the Development of Solid state AC Motor Drives. IEEE Trans. Power Electron 3, 105–117 (1988)CrossRefGoogle Scholar
  3. 3.
    Pillay, P., Krishnan, R.: Modeling, Simulation, and Analysis of Permanent-Magnet Motor Drives, Part I: The Permanent-Magnet Synchronous Motor Drive. IEEE Transactions on Industrial Applications 25(2), 265–273 (1989)CrossRefGoogle Scholar
  4. 4.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm: Applied Soft computing, vol. 8(1), pp. 687–697 (2008)Google Scholar
  5. 5.
    Stacey, A., Jancic, M., Grundy, I.: Particle swarm optimization with mutation. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 2, pp. 1425–1430 (2003)Google Scholar
  6. 6.
    Dasgupta, S., Das, S., Abraham, A., Biswas, A.: Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis. IEEE Transactions on Evolutionary Computation 13(4), 919–941 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anguluri Rajasekhar
    • 1
  • Ajith Abraham
    • 2
  • Ravi Kumar Jatoth
    • 1
  1. 1.National Institute of TechnologyWarangalIndia
  2. 2.Machine Intelligence Research Labs (MIR Labs)USA

Personalised recommendations