Abstract
In this study, a new controller method based on wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller through adaptive learning rates (ALRs) for the Internet-based bilateral teleoperation system is developed. The PID controller design suffers from dealing with a plant with an intricate dynamic model. To make an adaptive essence for PID controller, this study uses a trained offline self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in parallel with conventional PID controller. The SRWNN-PU parameters are updated online using an SRWNN-identifier (SRWNNI) in order to reduce the controller error in realtime function. Using feedback linearization method and a PID controller, the presented control method reduced the tracking error in the subsystems of the teleoperation system, i.e., master and slave which are stabilized, respectively. Additionally, time-varying delay in teleoperation systems is considered as noise making the master signals be modulated because wavelt neural networks have a high susceptibility to remove the noise, thus the WNAP+ID controller is able to eliminate the noise effect. In this paper, we concentrated on the efficiency and stability of the teleoperation system with time-varying parameters through simulation outcomes. Moreover, the results of the WNNs are compared with those of multi-layer perceptron neural networks (MLPNNs).
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Recommended by Associate Editor Huanqing Wang under the direction of Editor Hamid Reza Karimi. This work was supported by the National Natural Science Foundation of China (No. 51575292) and National Science and Technology Major Project of the Ministry of Science and Technology of China (No. 2018ZX04000020).
Soheil Ganjefar received his B.Sc. degree from the Ferdoowsi University, Mashhad, Iran, in 1994, and the M.Sc. and Ph.D. degrees from the Tarbiat Modares University, Tehran, Iran, in 1997 and 2003, respectively, all in electrical engineering. He is currently a Professor in the Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran. His main research interests include Teleoperation systems control, neural network, and Renewable Energy.
Mohammad Afshar received his M.Sc. degree in control engineering from the Bu- Ali Sina University of Hamedan, Iran, in 2013. His current research interests include neural network, intelligent systems control, robotic systems, and teleoperation systems.
Mohammad Hadi Sarajchi received his B.Sc. degree in electrical engineering from Razi University in 2010, and the M.S. degree in electrical engineering from Bu-Ali Sina University in Iran in 2013. In 2017, he joined the Department of Mechanical Engineering, Tsinghua University, in China as a Post Master Researcher. His current research interests include teleoperation system, artificial intelligence, cable-driven parallel robot (CDPR), and drone.
Zhufeng Shao is an associate professor in the Department of Mechanical Engineering, Tsinghua University. He received his Ph.D. degree in Mechanical Engineering from Tsinghua University in 2011. He joined Tsinghua University in the same year where he is teaching mechanical design and control of parallel manipulator. His research interests include cable-driven robot, motion control and optimal design.
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Ganjefar, S., Afshar, M., Sarajchi, M.H. et al. Controller Design Based On Wavelet Neural Adaptive Proportional Plus Conventional Integral-Derivative For Bilateral Teleoperation Systems With Time-Varying Parameters. Int. J. Control Autom. Syst. 16, 2405–2420 (2018). https://doi.org/10.1007/s12555-017-0739-1
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DOI: https://doi.org/10.1007/s12555-017-0739-1