Robust PID Parameters Optimization Design Based on Improved Particle Swarm Optimization

Jin Wei Fan; Qin Mei; Xiao Feng Wang

doi:10.4028/www.scientific.net/AMM.373-375.1125

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 373-375Robust PID Parameters Optimization Design Based on...

Robust PID Parameters Optimization Design Based on Improved Particle Swarm Optimization

Abstract:

The article, based on satisfying robustness of the system and put forward the objective function of time-domain performance and dynamic characteristics, introduced genetic operators into Particle Swarm Optimization. The algorithm improve the diversity of particles by selection and hybridization operations and strengthen the excellent characteristics of particles in the swarm by introducing crossover and mutation genes, which can avoid bog down into local optima and premature convergence and enhance searching efficiency. The simulation results indicate that when the algorithm is applied to the optimization of PID controller parameters of servo system of grinding wheel rack of MKS8332A CNC camshaft grinder, its performance is better than the single Genetic Algorithms or Particle Swarm Optimization, and it can also satisfy the demand of rapidity, stability and robustness.

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Periodical:

Applied Mechanics and Materials (Volumes 373-375)

Pages:

1125-1130

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.373-375.1125

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Online since:

August 2013

Authors:

Jin Wei Fan, Qin Mei, Xiao Feng Wang

Keywords:

Genetic Operators, Optimization of PID Controller Parameters, Particle Swarm Optimization (PSO), Robustness, Servo System of Grinding Wheel Rack

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References

[1] XIE Xiaofeng, ZHANG Wenjun, YANG Zhilian. Overview of particle swarm optimization[J]. Control and Decision, 2003, 18(2): 129-134.

Google Scholar

[2] Ciuprina G, Loan D, Munteanu I. Use of Intelligent-particle Swarm Optimization in Electromagnetics[J]. IEEE Trans. on Magnetics, 2002, 38(2): 1037-1040.

DOI: 10.1109/20.996266

Google Scholar

[3] Bergh F, Engelbrecht A P. A Cooperative Approach to Particle Swarm Optimization[J]. IEEE Trans. on Evolutionary Computation, 2004, 8(3): 225-239.

DOI: 10.1109/tevc.2004.826069

Google Scholar

[4] M. Senthil A, M.V.C. Rao, Aarthi C. A new and improved version of particle swarm optimization algorithm with global-local best parameters. Knowledge and Information Systems, 2008, 16(3): 331-357.

DOI: 10.1007/s10115-007-0109-z

Google Scholar

[5] Luitel B, Venayagamoorthy G K. Particle swarm optimization with quantum infusion for system identification[J]. Engineering Applications of Artificial Intelligence, 2010, 23(5): 635-649.

DOI: 10.1016/j.engappai.2010.01.022

Google Scholar