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Improved Four-channel PBTDPA Control Strategy Using Force Feedback Bilateral Teleoperation System

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Abstract

Bilateral teleoperation robots with force feedback enable humans to accomplish these tasks without exposing them to these hazardous environments. Its stability and transparency describe the performance of bilateral teleoperation systems with force feedback. Bilateral teleoperation with force feedback enables humans to combine tactics with optesthesia. However, the force feedback may lead to bilateral teleoperation instability if the communication channels’ time delay exists. The instability of bilateral teleoperation with force feedback, which is brought in by the time delay, has become one of the complicated problems researchers need to solve. Transparency is one of the leading design objectives of the teleoperation system. There are two evaluation criteria for transparency: the accuracy of the position followed by the master mechanical arm and the accuracy of the feedback received by the slave arm from the master arm. The main content of this paper is as follows: 1) This paper researches and summarizes the control structures and control algorithms of several well-developed force-feedback bilateral teleoperation systems and decides to improve the PBTDPA algorithm, which aligns with practical application requirements. 2) The four-channel structure makes the transparency of force-feedback bilateral teleoperation systems perfect in theory. This paper uses the four-channel structure combined with the PBTDPA algorithm to improve the transparency of the approach. 3) Moreover, the delay predictor is used to improve the four-channel power-based time domain passivity approach (PBTDPA) control strategy. The delay differential predictor is added to the communication channel. The delay change rate differential predictor can estimate the communication channel’s delay change rate instead of the maximum delay change rate to improve transparency. The simulation experiment of the improved control strategy was carried out. The results show the excellent performance of our design.

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Funding

This work was jointly supported by the Sichuan Science and Technology Program (2021YFQ0003, 2019YJ0189) and the Fundamental Research Funds for the Central Universities (ZYGX2019J059).

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Correspondence to Wenfeng Zheng or Lirong Yin.

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The authors declare no conflict of interest.

Xin Gong is an undergraduate student at the Xi’an Fanyi University who has participated as a research assistant in the collaborative research with the Research Center of Machine Perception and Intelligent Systems of the University of Electronic Science and Technology of China.

Lixiao Wang is a Professor and an Assistant to the President and Chief of the Science and Technology Division of Xi’an Fanyi University. She has a Ph.D. degree in economics from Northwest University and a Postdoctoral in Business Administration. She has published more than 30 papers.

Yuanyuan Mou is an undergraduate student at the Xi’an Fanyi university. She participates in the collaborative research between Xi’an Fanyi University and the University of Electronic Science and Technology of China as a research assistant at the Research Center of Machine Perception and Intelligent Systems.

Haili Wang is an undergraduate student at the Xi’an Fanyi university. She participates as a research assistant in the collaborative research between Xi’an Fanyi University and the Research Center of Machine Perception and Intelligent Systems of the University of Electronic Science and Technology of China.

Xiaoqian Wei obtained a bachelor’s degree and master’s degree in Automatic Control at the University of Electronic Science and Technology of China. Her research interest is in the area of analysis and modeling of time-delay systems and bilateral teleoperation systems.

Wenfeng Zheng is an associate professor at the School of Automation Engineering of the University of Electronic Science and Technology of China since 2008. He received his Ph.D. degree in earth exploration and information technology from the Chengdu University of Technology in 2008. The focused research interests involve environmental science, information technology, and artificial intelligent. He has published more than 100 papers, and authorized more than 30 Chinese national invention patents. He is a member of Association for Computing Machinery, IEEE, America Association Geographer, American Geophysical Union, and a membership of China Association of Inventions.

Lirong Yin is a Ph.D. student in the Department of Geography and Anthropology at Louisiana State University with a study interest in remote sensing, server weather and climate change, Coastal environment, natural hazard, and coupled human and natural dynamic system. Acquired the Master of Science in Geography from Louisiana State University and the Bachelor of Science in Geography Information Science from the University of Iowa, she has experienced the artificial intelligence studies and machine learning techniques, geo-data processing and information analysis skills. She is well experienced in programming and database design as a geo-analyst. She has published more than 30 papers.

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Gong, X., Wang, L., Mou, Y. et al. Improved Four-channel PBTDPA Control Strategy Using Force Feedback Bilateral Teleoperation System. Int. J. Control Autom. Syst. 20, 1002–1017 (2022). https://doi.org/10.1007/s12555-021-0096-y

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