[1] SUN Jie, QU Zhongxing, ZHANG Liwu, et al. Research and application progress of abrasive belt grinding for complex surface[J]. Manufacturing Technology & Machine Tool, 2017(11): 43-47. 孙杰, 曲中兴, 张立武, 等. 复杂型面砂带磨削技术的研究应用进展[J]. 制造技术与机床, 2017(11): 43-47. [2] HAO Lihua, WU Mingyu. Polishing technology and recent development of aero-impeller and blade difficult to machine[J]. Machine Building & Automation, 2017, 46(6): 54-56. 郝立华, 吴鸣宇. 难加工航空发动机叶轮叶片抛光技术及其发展现状[J]. 机械制造与自动化, 2017, 46(6): 54-56. [3] CUI Haijun, ZHANG Mingqi. Current situation and development trend of aircraft engine blade polishing technology[J]. Aeronautical Manufacturing Technology, 2015, 11: 128-131. 崔海军, 张明岐. 航空发动机叶片抛光技术现状及发展趋势[J]. 航空制造技术, 2015, 11: 128-131. [4] HUANG Yun, XIAO Guijian, ZOU Lai. Current situation and development trend of polishing technology for blisk[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(7): 2045-2064. 黄云, 肖贵坚, 邹莱. 整体叶盘抛光技术的研究现状及发展趋势[J]. 航空学报, 2016, 37(7): 2045-2064. [5] TANG Yangyang, DING Jinhua, WANG Jiaxun, et al. Force control research and application of robot flexible abrasive belt grinding[J]. Manufacturing Technology & Machine Tool, 2017(5): 97-102, 106. 唐洋洋, 丁金华, 王嘉循, 等. 机器人柔性砂带磨削加工力控制研究与应用[J]. 制造技术与机床, 2017(5): 97-102, 106. [6] HAO Daxian, WANG Wei, WANG Qilong, et al. Applications and development trend of robotics in composite material process[J]. Journal of Mechanical Engineering, 2019, 55(3): 1-17. 郝大贤, 王伟, 王琦珑, 等. 复合材料加工领域机器人的应用与发展趋势[J]. 机械工程学报, 2019, 55(3): 1-17. [7] WANG W, YUN C. A path planning method for robotic belt surface grinding[J]. Chinese Journal of Aeronautics, 2011, 24(4): 520-526. [8] REN Yongjie, YIN Shibin, ZHU Jigui. High precision control method of industrial robot for modern flexible manufacturing[J]. Aeronautical Manufacturing Technology, 2018, 61(5): 16-21. 任永杰, 尹仕斌, 邾继贵. 面向现代柔性制造的工业机器人高精度控制方法[J]. 航空制造技术, 2018, 61(5): 16-21. [9] OTT C, MUKHERJEE R, NAKAMURA Y. Unified impedance and admittance control[C]// IEEE International Conference on Robotics & Automation. IEEE, 2010: 554-561. [10] BICCHI A, TONIETTI G, INTERDIPARTIMENTALE C. Dealing with the safety-performance trade-off in robot arms design and control[J]. IEEE Robotics and Automation Magazine, 2004, 11(2): 22-33. [11] TSUJI T, TERAUCHI M, TANAKA Y. Online learning of virtual impedance parameters in non-contact impedance control using neural networks[J]. IEEE Transactions on Cybernetics, 2004, 34(5): 2112-2118. [12] BUCHLI J, STULP F, THEODOROU E, et al. Learning variable impedance control[J]. International Journal of Robotics Research, 2011, 30(7): 820-833. [13] YANG R, YANG C, CHEN M, et al. Adaptive impedance control of robot manipulators based on Q-learning and disturbance observer[J]. Systems Science & Control Engineering, 2017, 5(1): 287-300. [14] IKEURA R, INOOKA H. Variable impedance control of a robot for cooperation with a human[C]// Robotics and Automation, 1995. Proceedings. 1995 IEEE International Conference on. IEEE, 1995: 3097-3102. [15] JUNG S, HSIA T C, BONITZ R G. Force tracking impedance control for robot manipulators with an unknown environment: Theory, simulation, and experiment[J]. International Journal of Robotics Research, 2001, 20(9): 765-774. [16] JUNG S, HSIA T C, BONITZ R G. Force tracking impedance control of robot manipulators under unknown environment[J]. IEEE Transactions on Control Systems Technology, 2004, 12(3): 474-483. [17] ROVEDA L, PEDROCCHI N, TOSATTI L M. Exploiting impedance shaping approaches to overcome force overshoots in delicate interaction tasks[J]. International Journal of Advanced Robotic System, 2016, 13(5): 1-11. [18] ROVEDA L, PALLUCCA G, PEDROCCHI N, et al. Iterative learning procedure with reinforcement for high-accuracy force tracking in robotized tasks[J]. IEEE Transactions on Industrial Informatics, 2017, (99): 1-11. [19] WANG C, LI Y, GE S S, et al. Reference adaptation for robots in physical interactions with unknown environments[J]. IEEE Transactions on Cybernetics, 2017, 47(11): 3504-3515. [20] XU W, MINAMI M, MAE Y. Position/force control of grinding robot by using real-time presumption of constrained condition[C]// Sice Conference. IEEE, 2008: 1861-1868. [21] VENATOR E, LEE G S, NEWMAN W. Hardware and software architecture of ABBY: An industrial mobile manipulator[C]// IEEE International Conference on Automation Science & Engineering. IEEE, 2013: 324-329. [22] SERAJI H, COLBAUGH R. Force tracking in impedance control[J]. The International Journal of Robotics Research, 1997, 16(1): 97-117. [23] LIANG X Q, ZHAO H, LI X F, et al. Force tracking impedance control with unknown environment via an iterative learning algorithm[J]. Science China-Information Sciences, 2019, 62(5): 050215. [24] XU Xiaohu. Research on the key technology of robotic abrasive belt grinding of compressor blade[D]. Wuhan: Huazhong University of Science & Technology, 2019. 徐小虎. 压气机叶片机器人砂带磨抛加工关键技术研究[D]. 武汉: 华中科技大学, 2019. [25] BI Qingzhen. Key issues in CNC machining of complex surface parts—interpretation of "Theory and Technology of Five-axis CNC Machining of Complex Surface Parts"[J]. China Mechanical Engineering, 2018, 29(14): 1758-1763. 毕庆贞. 复杂曲面零件数控加工的关键问题——解读《复杂曲面零件五轴数控加工理论与技术》[J]. 中国机械工程, 2018, 29(14): 1758-1763. [26] WANG Yongding, LI Ningye. Optimization and research of tidal turbine blades based on genetic algorithm[J]. Journal of Mechanical Strength, 2021, 43(2): 327-332. 王永鼎, 李宁业. 采用遗传算法的海流能发电机叶片优化与研究[J]. 机械强度, 2021, 43(2): 327-332. [27] REN Yiru, ZHANG Tiantian, ZENG Lingbin. Tidal turbine hydrofoil design method based on genetic algorithm[J]. Journal of Hunan University, 2015, 42(10): 59-64. 任毅如, 张田田, 曾令斌. 基于遗传算法的潮流能水轮机翼型优化设计[J]. 湖南大学学报, 2015, 42(10): 59-64. [28] GEN M, CHENG R. Genetic algorithms and engineering optimization[M]. New York: John Wiley & Sons, 2000. [29] LIN Yang, ZHAO Huan, DING Han. Solution of inverse kinematics for general robot manipulators based on multiple population genetic algorithm[J]. Journal of Mechanical Engineering, 2017, 53(3): 1-8. 林阳, 赵欢, 丁汉. 基于多种群遗传算法的一般机器人逆运动学求解[J]. 机械工程学报, 2017, 53(3): 1-8. [30] FOGEL D B. An introduction to simulated evolutionary optimization[J]. IEEE Transactions on Neural Networks, 1994, 5(1): 3-14. [31] LIU Yu, XIAO Shide, ZHANG Rui, et al. Initial estimation of digital image correlated deformation based on genetic algorithms[J]. Laser Technology, 2020, 44(1): 130-135. 刘禹, 肖世德, 张睿, 等. 基于遗传算法的数字图像相关变形初值估计[J]. 激光技术, 2020, 44(1): 130-135. |