Abstract
In this research, to obtain satisfactory control performance of ultrasonic motor (USM) in position control, a hybrid intelligent method based on PID control using neural networks (NNs) combined with Artificial Bee Colony (ABC) algorithm is proposed. Using the proposed intelligent control method, the gains of PID control are auto-tuned to minimize the errors in position control. The proposed method is with simple structure and easy to be implemented in estimation. It is designed to compensate the nonlinearity and characteristic changes in position control. The proposed hybridization of NN and ABC makes the intelligent scheme superior in usefulness and effectiveness. In this paper, the control performance and effectiveness of the proposed method were confirmed and studied according to experimental results on an existing USM servo system.
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References
T. Kenjo, T. Sashida, An Introduction of Ultrasonic Motor (Oxford Science Publications, Oxford, 1993)
K. Adachi, Actuator with Friction Drive: Ultrasonic Motor. The Japan Society of Mechanical Engineers, vol. 108, pp. 48–51, 2005
C. Zhao, Ultrasonic Motor - Technologies and Applications (Science Press Beijing and Springer-Verlag Berlin Heidelberg, Beijing, 2011)
I. Yamano, T. Maeno, Five-fingered robot hand using ultrasonic motors and elastic elements, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2673–2678, 2005
T. Senjyo, H. Miyazato, K. Uezato, Quick and Precise Position Control of Ultrasonic Motor using Hybrid Control, Electrical Engineering in Japan, vol. 116, No. 3, pp. 83–95, 1996
H. Lu, Y. Li, S. Mu, D. Wang, H. Kim, S. Serikawa, Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. 5(4), 2315–2322 (2018)
H. Lu, Y. Li, M. Chen, H. Kim, S. Serikawa, Brain Intelligence: go beyond artificial intelligence. Mobile Networks Appl. 23, 368–375 (2018)
Q. Zhou, J. Cheng, H. Lu, Y. Fan, S. Zhang, X. Wu, B. Zheng, W. Ou, L.J. Latecki, Learning adaptive contrast combinations for visual saliency detection. Multimed. Tools Appl., 1–29 (2018). https://doi.org/10.1007/s11042-018-6770-2
Q. Zhou, W. Yang, G. Gao, W. Ou, H. Lu, J. Chen, L.J. Latecki, World Wide Web vol. 22(2), pp. 555–570 (2019). https://doi.org/10.1007/s11280-018-0556-3
A. Aoyama, F.J. Doyle III, V. Venkatasubramanian, A fuzzy neural-network approach for nonlinear process control. Eng. Appl. Artif. Intell. 8, 483–498 (1995)
S. Mu, K. Tanaka, S. Nakashima, D. Alrijadjis, Real-time PID Controller using Neural Network Combined with PSO for ultrasonic motor. ICIC Express Lett. 8(11), (2014)
K. Karaboga, An idea based on Honey Bee Swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, 2005
D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, A Comprehensive survey: Artificial Bee Colony (ABC) algorithm and applications. Artif. Intell. Rev. 42, 21–57 (2014)
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Mu, S., Shibata, S., Yamamoto, T., Nakashima, S., Tanaka, K. (2021). Intelligent Control of Ultrasonic Motor Using PID Control Combined with Artificial Bee Colony Type Neural Networks. In: Li, Y., Lu, H. (eds) 3rd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-46032-7_7
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DOI: https://doi.org/10.1007/978-3-030-46032-7_7
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