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基于自适应反正切非奇异终端滑模的水下机械臂轨迹跟踪控制
作者:
作者单位:

1.广东电网有限责任公司电力科学研究院;2.哈尔滨理工大学;3.辽宁工业大学;4.中国科学院自动化研究所

中图分类号:

TP242

基金项目:

国家重点研发计划项目2023YFB4707000,国家自然科学基金项目U23B2038和62122087，北京自然科学基金项目4222055，中国科学院青年创新促进会项目Y2022053，北京联合国教科文组织科学研究计划项目KZ202210017024，辽宁工业大学创业基金XB2022005，辽宁省自然科学基金项目2023-BS-193，辽宁省教育厅科技攻关项目LIKMZ20963


Trajectory Tracking Control of an Underwater Manipulator Based on Adaptive Arctangent Non-singular Terminal Sliding Mode Control
Author:
Affiliation:

1.Guangdong Power Grid limited liability company electric power science research Institute;2.Harbin University of Science and Technology;3.Liaoning University of Technology;4.Institute of Automation, Chinese Academy of Sciences

Fund Project:

National Key R&D Program of China under Grant 2023YFB4707000，the National Natural Science Foundation of China under Grant U23B2038 and Grant 62122087，Beijing Natural Science Foundation under Grant 4222055，Youth Innovation Promotion Association CAS under Grant Y2022053,the Scientific Research Program of Beijing Municipal Commission of Education-Natural Science Foundation KZ202210017024,the Doctoral Startup Fund of Liaoning University of Technology under Grant XB2022005,the Natural Science Foundation of Liaoning Province under Grant 2023-BS-193,the Scientific Research Project of Liaoning Provincial Department of Education under Grant LJKMZ20220963.

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    摘要:

    针对水下机械臂的末端轨迹跟踪控制问题,提出一种自适应快速反正切非奇异终端滑模控制器.首先,基于莫里森方程建立考虑水作用效应的水下机械臂动力学模型;其次,结合非奇异终端滑模控制和反正切函数特性,设计了一种反正切非奇异终端滑模控制器,并基于径向基神经网络预测了系统的未知扰动;然后,通过李雅普诺夫理论验证了所提控制方法可在有限时间内收敛到期望位置;最后,通过仿真实验验证了所提控制方法的有效性.

    Abstract:

    An adaptive fast arctangent non-singular terminal sliding mode controller is proposed for the end trajectory tracking control of the underwater manipulator. Firstly, the dynamic model of the underwater manipulator considering water effects is established based on the Morrison equation. Secondly, combining the characteristics of non-singular terminal sliding mode control and arctangent function, an arctangent non-singular terminal sliding mode controller is designed, and the unknown disturbance of the system is predicted based on the radial basis function neural network. Then, the Lyapunov theory is used to verify that the proposed control method can converge to the desired position in finite time. Finally, the effectiveness of the proposed control method is verified by simulation experiments.

    参考文献
    [1] Martin S C , Whitcomb L L. Nonlinear Model-Based Tracking Control of Underwater Vehicles With Three Degree-of-Freedom Fully Coupled Dynamical Plant Models: Theory and Experimental Evaluation[J]. IEEE Transactions on Control Systems Technology, 2018,26(2):404-414.
    [2] 袁凯,刘延俊,孙景余等.基于模糊RBF神经网络的水下机械臂控制研究[J].工程设计学报,2019,26(6):675-682.
    (Yuan K, Liu Y J, Sun J Y, et al. Research on Control of Underwater Manipulator Based on Fuzzy RBF Neural Network [J]. Journal of Engineering Design, 2019,26 (6): 675-682.)
    [4] [3]Li Z, Zhai J. Event-Triggered-Based Sliding Mode Asymptotic Tracking Control of Robotic Manipulators[J]. IEEE Transactions on Circuits and Systems II: Express Briefs,2024,71(3):1266-1270.
    [5] [4]Chen J Q, Zhang H Y, Zhu T T, et al. Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis[J]. Applied Mathematics and Computation,2024, 470:128552.
    [6] [5] Zhai J Y, Xu G. A Novel Non-Singular Terminal Sliding Mode Trajectory Tracking Control for Robotic Manipulators[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68(1):391-395.
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    [8] [7] Shrivastava A, Dalla V K, Abhishek K. Optimum joint and trajectory tracking control of multi-axes space manipulator based on amnesia feedback controller[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science,2023, 237(19):4546-4563.
    [9] [8] Zhang S, Wu Y, He X, Wang J Y. Neural Network-Based Cooperative Trajectory Tracking Control for a Mobile Dual Flexible Manipulator[J]. IEEE Transactions on Neural Networks and Learning Systems,2023, 34(9): 6545-6556.
    [10] [9] Dai L, Yu Y, Zhai D H, et al. Robust Model Predictive Tracking Control for Robot Manipulators With Disturbances[J]. IEEE Transactions on Industrial Electronics, 2021, 68(5):4288-4297.
    [11] [10] Wang B, Zhang C, Jia Q, et al. Robust Continuous Sliding Mode Control for Manipulator PMSM Trajectory Tracking System Under Time-Varying Uncertain Disturbances[J]. IEEE Access,2020, 8:196618-196632.
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    [19] [18] 华长春,陈传虎,陈健楠,等.基于干扰观测器的水下机器人预定性能控制[J].控制与决策,2022,37(5):1160-1166.
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    [21] [19]张奇. 水下机械臂轨迹规划及控制系统研究与实现 [D]. 哈尔滨: 哈尔滨工程大学, 2019: 67-134.
    (Zhang Q. Research and Implementation of Trajectory Planning and Control System of Underwater Manipulator [D]. Harbin: Harbin Engineering University, 2019: 67-134.)
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历史
  • 收稿日期:2024-03-30
  • 最后修改日期:2024-09-20
  • 录用日期:2024-06-22
  • 在线发布日期: 2024-08-01
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技术支持：北京勤云科技发展有限公司