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Year 2015, , 159 - 164, 30.12.2015
https://doi.org/10.17350/HJSE19030000020

Abstract

References

  • Zhang L, Zuo J, Yao X, Zhang X, Shuai L. A robot visual servo-based approach to the determination of next best views. Proceedings of 2015 IEEE International Conference on Mechatronics and Automation, Beijing, China, 2-5 August. pp. 2654-2659, 2015.
  • Gu J, Wang H, Pan Y, Wu Q. Neural network based visual servo control for CNC load/unload manipülatör. Optik 126 (2015) 4489-4492.
  • Wang ZH, Liu ZH. Application of Active Disturbance Rejection Controller in Wheeled Mobile Robot Servo System. Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China, 8-10 August. pp. 324- 329, 2014.
  • Zhu ZP, Du AM, Ma ZX, Zhang WY, Fan CG. Vehicle robot driver research and development based on servo motor control. Applied Mechanics and Materials 709 (2014) 272-275.
  • Nhon PNQ, Elamvazuthi I, Fayek HM, Parasuraman S, Ahamed Khan MKA. Intelligent control of rehabilitation robot: auto tuning pıd controller with interval type 2 fuzzy for dc servomotor. Procedia Computer Science 42 (2014) 183-190.
  • Kennedy J, Eberhart R. Particle swarm optimization. International Conference on Neural Networks Proceedings, Perth, USA, 27 November-01 December. pp. 1942-1948, 1995.
  • Steffy SA, Mangaiyarkarasi B, Jasper SS, Priyanka K, Soorya K. Analysis & reduction of THD in multilevel ınverter using PSO algorithm. International Journal of Advanced Research in Computer and Communication Engineering 3 (2014) 5417- 5422.
  • Bahrami S, Hooshmand RA, Parastegari M. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy 72 (2014) 434-442.
  • Sharafi M, Elmekkawy TY. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renewable Energy 68 (2014) 67-79.
  • Khansary MA, Sani AH. Using genetic algorithm (GA) and particle swarm optimization (PSO) methods for determination of interaction parameters in multicomponent systems of liquid–liquid equilibria. Fluid Phase Equilibria 365 (2014) 141-145.
  • Samet H, Hashemi F, Ghanbari T. Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO. Renewable and Sustainable Energy Reviews 52 (2015) 1-18.
  • Ghanad NK, Ahmadi S. Combination of PSO Algorithm and Naive Bayesian Classification for Parkinson Disease Diagnosis. Advances in Computer Science: an International Journal 4 (2015) 119-125.
  • Yang Q, Zour HY, Zhang Y, Tang LJ, Shen GL, Jiang JH, Yu RQ. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm. Talanta 147 (2016) 609-614.
  • Lin MY, Chin KS, Tsui KL, Wong TC. Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling. Computers & Operations Research 65 (2016) 125-138.
  • Shadmand S, Mashoufi B. A new personalized ecg signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control 25 (2016) 12-23.
  • Taherkhani M, Safabakhsh R. A novel stability-based adaptive inertia weight for particle swarm optimization. Applied Soft Computing 38 (2016) 281-295.

AC servo motor speed and position control using Particle Swarm Optimization PSO

Year 2015, , 159 - 164, 30.12.2015
https://doi.org/10.17350/HJSE19030000020

Abstract

I n this article, a new design method, called Particle Swarm Optimization PSO , is used for the determination of PID control parameters; this is designated for the controlling of the speed and the position of the AC servomotor. For the determination of the decision parameters AC servomotors are mathematically modelled. Rise time, settling time, and overshoot are taken into consideration, during the optimization process. Controller’s performance is determined based on different criteria, such as, ITAE Integral of Time Weighted Absolute Error , IAE Integral of Absolute Error , ISE Integral of Squared Error and ITSE Integral of Time Weighted Squared Error . Superiority and accuracy of the proposed technique was verified by simulation results. In addition, considering the quality of the obtained results, proposed technique is found effective and strong in reduction of the error of motion control systems

References

  • Zhang L, Zuo J, Yao X, Zhang X, Shuai L. A robot visual servo-based approach to the determination of next best views. Proceedings of 2015 IEEE International Conference on Mechatronics and Automation, Beijing, China, 2-5 August. pp. 2654-2659, 2015.
  • Gu J, Wang H, Pan Y, Wu Q. Neural network based visual servo control for CNC load/unload manipülatör. Optik 126 (2015) 4489-4492.
  • Wang ZH, Liu ZH. Application of Active Disturbance Rejection Controller in Wheeled Mobile Robot Servo System. Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference, Yantai, China, 8-10 August. pp. 324- 329, 2014.
  • Zhu ZP, Du AM, Ma ZX, Zhang WY, Fan CG. Vehicle robot driver research and development based on servo motor control. Applied Mechanics and Materials 709 (2014) 272-275.
  • Nhon PNQ, Elamvazuthi I, Fayek HM, Parasuraman S, Ahamed Khan MKA. Intelligent control of rehabilitation robot: auto tuning pıd controller with interval type 2 fuzzy for dc servomotor. Procedia Computer Science 42 (2014) 183-190.
  • Kennedy J, Eberhart R. Particle swarm optimization. International Conference on Neural Networks Proceedings, Perth, USA, 27 November-01 December. pp. 1942-1948, 1995.
  • Steffy SA, Mangaiyarkarasi B, Jasper SS, Priyanka K, Soorya K. Analysis & reduction of THD in multilevel ınverter using PSO algorithm. International Journal of Advanced Research in Computer and Communication Engineering 3 (2014) 5417- 5422.
  • Bahrami S, Hooshmand RA, Parastegari M. Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy 72 (2014) 434-442.
  • Sharafi M, Elmekkawy TY. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renewable Energy 68 (2014) 67-79.
  • Khansary MA, Sani AH. Using genetic algorithm (GA) and particle swarm optimization (PSO) methods for determination of interaction parameters in multicomponent systems of liquid–liquid equilibria. Fluid Phase Equilibria 365 (2014) 141-145.
  • Samet H, Hashemi F, Ghanbari T. Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO. Renewable and Sustainable Energy Reviews 52 (2015) 1-18.
  • Ghanad NK, Ahmadi S. Combination of PSO Algorithm and Naive Bayesian Classification for Parkinson Disease Diagnosis. Advances in Computer Science: an International Journal 4 (2015) 119-125.
  • Yang Q, Zour HY, Zhang Y, Tang LJ, Shen GL, Jiang JH, Yu RQ. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm. Talanta 147 (2016) 609-614.
  • Lin MY, Chin KS, Tsui KL, Wong TC. Genetic based discrete particle swarm optimization for Elderly Day Care Center timetabling. Computers & Operations Research 65 (2016) 125-138.
  • Shadmand S, Mashoufi B. A new personalized ecg signal classification algorithm using block-based neural network and particle swarm optimization. Biomedical Signal Processing and Control 25 (2016) 12-23.
  • Taherkhani M, Safabakhsh R. A novel stability-based adaptive inertia weight for particle swarm optimization. Applied Soft Computing 38 (2016) 281-295.
There are 16 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Mehmet Fatih Işık This is me

Erhan Cetin This is me

Halil Aykul This is me

Husamettin Bayram This is me

Publication Date December 30, 2015
Published in Issue Year 2015

Cite

Vancouver Işık MF, Cetin E, Aykul H, Bayram H. AC servo motor speed and position control using Particle Swarm Optimization PSO. Hittite J Sci Eng. 2015;2(2):159-64.

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