Open Access
Volume 42, Number 2, April 2024
Page(s) 368 - 376
Published online 30 May 2024
  1. MARK W·Spang, SETH Hutchinson, M·Vaderjasaga. Robot modeling and control[M]. Beijing: Machinery Industry Press, 2016 (in Chinese) [Google Scholar]
  2. PETER Corke. Robotics, Machine Vision and Control[M]. Beijing: Electronics Industry Press, 2016 (in Chinese) [Google Scholar]
  3. DING Xuegong. Research on robot control[M]. Hangzhou: Zhejiang University Press, 2006 (in Chinese) [Google Scholar]
  4. CERVANTES I, ALVARE-RAMIREZ J. On the PID tracking control of robot manipulators[J]. Systems & Control Letters, 2001, 42(1): 37–46 [Google Scholar]
  5. ZHANG Tie, HONG Jingdong, LI Qiufen, et al. Wave friction correction method for a robot based on BP neural net-work[J]. Journal of Engineering Science, 2019, 41(8): 1085–1091. [Article] (in Chinese) [Google Scholar]
  6. MA Yuhao, LIANG Yanbing. An obstacle avoidance algorithm for manipulator based on sixth-order polynomial trajectory planning[J]. Journal of Northwest Polytechnical University, 2020, 38(2): 392–400. [Article] (in Chinese) [Google Scholar]
  7. YANRU L, YAN Z. Two-DOF manipulator trajectory tracking control based on unfalsified control[C]//The 27th Chinese Cont-rol and Decision Conference, Qingdao, 2015: 4563–4566 [Google Scholar]
  8. ABDALLA A Y, ABDALLA T Y, CHYAID A M. Grasshopper algorithm based fuzzy system for trajectory tracking of robot manipulator[C]//2022 International Conference on Electrical, Computer and Energy Technologies, Prague, 2022: 1–5 [Google Scholar]
  9. ZHANG X, GU J, ASAD M U, et al. Beetle bee algorithm applied to trajectory tracking control of omni manipulator[C]//2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, Lahore, 2022: 1–5 [Google Scholar]
  10. SINGH R, PRASAD L B. Optimal trajectory tracking of robotic manipulator using ant colony optimization[C]//2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, Gorakhpur, 2018: 1–6 [Google Scholar]
  11. SHAOMING L, RUIPENG L. Research on trajectory tracking control of multiple degree of freedom manipulator[C]//2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, Hefei, 2017: 218–222 [Google Scholar]
  12. JUAN W, YANG H, XIE H. Control of manipulator trajectory tracking based on improved RBFNN[C]//2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, 2009: 142–145 [Google Scholar]
  13. MIRÓ J V, WHITE A S, GILL R. On-line time-optimal algorithm for manipulator trajectory planning[C]//1997 European Control Conference, Brussels, 1997: 2611–2616 [Google Scholar]
  14. ATALAR-AYYLLDLZ B, KARAHAN O. Tuning of fractional order pid controller using CS algorithm for trajectory tracking control[C]//2018 6th International Conference on Control Engineering & Information Technology, Istanbul, 2018: 1–6 [Google Scholar]
  15. ZHANG L, CHENG L. An adaptive neural network control method for robotic manipulators trajectory tracking[C]//2019 Chinese Control and Decision Conference, Nanchang, 2019: 4839–4844 [Google Scholar]
  16. WIDYIANTO A, YAZID E, MIRDANIES M, et al. Optimization of PD controller using ACO for the trajectory tracking of a ship-mounted two-DOF manipulator system[C]//2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering, Yogyakarta, 2022: 634–638 [Google Scholar]
  17. LAMPINEN S, NIEMI J, MATTILA J. Flow-bounded trajectory-scaling algorithm for hydraulic robotic manipulators[C]//2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Boston, 2020: 619–624 [Google Scholar]
  18. ZHU Q, WANG J, ZHANG W A, et al. A Geometry based IK solver and b-spline method for trajectory tracking of 5-DOF manipulators[C]//2018 37th Chinese Control Conference, Wuhan, 2018: 3865–3870 [Google Scholar]
  19. YANG Yimin. Researches on extreme learning theory for system identification and applications[D]. Changsha: Hunan University, 2013 (in Chinese) [Google Scholar]
  20. YU Xinbo, HE Wei, XUE Chengqian, et al. Disturbance observer-based adaptive neural network tracking control for robots[J]. Acta Automatica Sinica, 2019, 45(7): 1307–1324. [Article] (in Chinese) [Google Scholar]
  21. WANG Leikun. Research on trajectory control of drilling arm of rock drilling robot[D]. Ganzhou: Jiangxi University of Science and Technology, 2019 (in Chinese) [Google Scholar]
  22. CUI Minqi. Dynamical modeling of SCARA robot based on lagrange formulation[J]. Mechanical Design and Manufacturing, 2013(12): 76–78. [Article] (in Chinese) [Google Scholar]
  23. ZHOU Gang, YAO Qionghui, CHEN Yongbing, et al. Global straight-line tracking control of ships based on input-output linearization[J]. Control Theory and Application, 2007(1): 117–121. [Article] (in Chinese) [Google Scholar]
  24. LI Tieshan, YANG Yansheng, ZHENG Yunfeng. Input-output linearization designs for straight-line tracking control of undera-ctuated ships[J]. System Engineering and Electronics, 2004(7): 945–948. [Article] (in Chinese) [Google Scholar]
  25. SHUAI Xin, LI Yanjun, WU Tiejun. Real time predictive control algorithm for endpoint trajectory tracking of flexible mani-pulator[J]. Journal of Zhejiang University, 2010, 44(2): 259–264. [Article] (in Chinese) [Google Scholar]
  26. SHOHEI Hagane, LIZ Katherine Rincon Ardila, TAKUMA Katsumata, et al. Adaptive generalized predictive controller and cartesian force control for robot arm using dynamics and geometric identification[J]. Journal of Robotics and Mechatronics, 2018, 30(6): 927–942. [Article] [Google Scholar]
  27. CHENG Linyun, ZHANG Lei, SONG Xiaona. Adaptive control method of manipulator based on RBF neural network[J]. Computer Measurement and Control, 2019, 27(7): 79–84. [Article] (in Chinese) [Google Scholar]

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