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Adaptive sliding mode control of underwater manipulator based on nonlinear dynamics model compensation |
| FU Wen1, WEN Hao1, HUANG Junhui1, SUN Binxuan1, CHEN Jiajie2, CHEN Wu2, FENG Yue1, DUAN Xingguang1 |
1. School of Mechatronical Engineering, Beijing Insitute of Technology, Beijing 100081, China;
2. China Nuclear Power Technology Research Institute Co., Ltd., China General Nuclear Power Group, Shenzhen 518000, China |
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Abstract [Objectives] The South-to-North water diversion project is a strategic project in China. Since its construction, it has become the main source of water conservancy in more than 280 cities.The diversion tunnel is the key building to support the South-to-North water diversion project. Due to its long line, large diameter, high water pressure, complex surrounding rock geology, as well as many years of water conservancy erosion, biochemical substances erosion, geological effect and other influences, typical defects such as cracks, collapse, exposed steel bars are prone to occur. Artificial detection of defects in the tunnel not only consumes time and energy, but also has low accuracy and timeliness. Therefore, underwater robot inspection technology has become a hotspot of current research.Among them, the underwater manipulator can not only be installed on the underwater vehicle, but also can be selectively installed on the required platform to complete the tasks of cleaning the water surface, laying and repairing cables, salvaging sunken objects, cutting off ropes and so on. However, the control of the underwater manipulator is more complicated and difficult due to its time-varying mechanics, nonlinear properties, external interference and hydrodynamic influence. The main purpose of this paper is to establish the dynamics model of the underwater manipulator and improve the accuracy of the trajectory tracking of the manipulator. [Methods] In this paper, a modeling method combining Newton-Euler equation and Morrison's dynamic model is proposed, and then the dynamic parameters are identified. Then, in order to improve the precise control ability of the manipulator in complex transient underwater environment, an adaptive sliding mode control method is designed based on compensating nonlinear dynamics model and using radial basis function (RBF) neural network to compensate the unmodeled and modeling errors of the system. Through the dynamic modeling in Section 4, a detailed dynamic simulation environment of the underwater manipulator is obtained. Gaussian noise errors with amplitudes of 5, 20, 15, 10, 8, and 5 N·m are set for each joint. On this basis, Experiment 1(P1): double loop proportional integral differential (PID) controller is designed for control simulation. Then, in experiment 2(P2), RBF neural network is used to make fitting compensation for system modeling errors and unmodeled items. In experiment 3(P3), dynamic model compensation is added on the basis of P2. [Results] The trajectory tracking effect ratio of P2 and P3 was obviously better than that of P1 experiment, and the tracking effect of P3 experiment was also better than that of P2 experiment after compensating the dynamic model. [Conclusions] Through simulation, this paper has proved the effectiveness of the proposed hydrodynamic modeling of the manipulator, and on the basis of compensating nonlinear dynamic model, The adaptive sliding mode control method using RBF neural network to compensate the unmodeled and modeling errors of the system has higher trajectory tracking accuracy than the traditional PID control and the general RBF network adaptive sliding mode control.
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| Keywords
underwater manipulator
dynamic parameters identification
radial basis function neural network compensation
adaptive sliding mode control
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Issue Date: 27 June 2023
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[1] SIVCEV S, COLEMAN J, OMERDIC E, et al. Underwater manipulators:A review[J]. Ocean engineering, 2018, 163:431-450.
[2] PAREDIS C J J, BROWN H B, KHOSLA P K. A rapidly deployable manipulator system[J]. Robotics and Autonomous Systems, 1997, 21(3):289-304.
[3] SAGARA S, TAMURA M, YATOH T, et al. Digital RAC for underwater vehicle-manipulator systems considering singular configuration[J]. Artificial Life and Robotics, 2006, 10(2):106-111.
[4] PANDIAN S R, SAKAGAMI N. System integration aspects of underwater vehicle-manipulator systems for oceanic exploration[J]. Journal of the Society of Instrument and Control Engineers, 2008, 47(10):830-836.
[5] ARMENIO V. Dynamic loads on submerged bodies in a viscous numerical wave tank at small KC numbers[J]. Ocean Engineering, 1998, 25(10):881-905.
[6] CHEN Y, WANG K L, ZHAI L Y, et al. Feedforward fuzzy trajectory compensator with robust adaptive observer at input trajectory level for uncertain multi-link robot manipulators[J]. Journal of the Franklin Institute, 2017, 354(8):3237-3266.
[7] CHEN B, LIU X P, LIU K F, et al. Direct adaptive fuzzy control of nonlinear strict-feedback systems[J]. Automatica, 2009, 45(6):1530-1535.
[8] XU B, PANDIAN S R, SAKAGAMI N, et al. Neuro-fuzzy control of underwater vehicle-manipulator systems[J]. Journal of the Franklin Institute, 2012, 349(3):1125-1138.
[9] KHALIL W, DOMBRE E. Modeling, identification&control of robots[M]. London:Hermes Penton Science, 2002.
[10] 布鲁诺·西西里安诺,洛伦索·夏维科,路易吉·维拉尼,等.机器人学:建模、规划与控制[M].张国良,曾静,陈励华,等,译.西安:西安交通大学出版社, 2015. SICILIANO B, SCIAVICCO L, VILLANI L, et al. Robotics:Modelling, planning and control[M]. ZHANG G L, ZENG J, CHEN L H, et al, trans. Xi'an:Xi'an Jiaotong University Press, 2015.(in Chinese)
[11] ISKANDAR M, WOLF S. Dynamic friction model with thermal and load dependency:Modeling, compensation, and external force estimation[C]//2019 International Conference on Robotics and Automation (ICRA). Montreal, Canada:IEEE, 2019:7367-7373.
[12] KIM S. Moment of inertia and friction torque coefficient identification in a servo drive system[J]. IEEE Transactions on Industrial Electronics, 2019, 66(1):60-70.
[13] 杨帆.水下机械手运动学与动力学控制关键问题研究[D].哈尔滨:哈尔滨工业大学, 2019. YANG F. Research on the key problems of kinematics and dynamics control based on underwater manipulator[D]. Harbin:Harbin Engineering University, 2019.(in Chinese)
[14] ZHONG Y G, YANG F. Dynamic modeling and adaptive fuzzy sliding mode control for multi-link underwater manipulators[J]. Ocean Engineering, 2019, 187:106202.
[15] HUANG S F, CHEN J H, ZHANG J W, et al. Robust estimation for an extended dynamic parameter set of serial manipulators and unmodeled dynamics compensation[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(2):962-973.
[16] INDRI M, TRAPANI S. Framework for static and dynamic friction identification for industrial manipulators[J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(3):1589-1599.
[17] LEE T, WENSING P M, PARK F C. Geometric robot dynamic identification:A convex programming approach[J]. IEEE Transactions on Robotics, 2020, 36(2):348-365.
[18] GAZ C, COGNETTI M, OLIVA A, et al. Dynamic identification of the Franka Emika panda robot with retrieval of feasible parameters using penalty-based optimization[J]. IEEE Robotics and Automation Letters, 2019, 4(4):4147-4154.
[19] MADSEN E, ROSENLUND O S, BRANDT D, et al. Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators[J]. Control Engineering Practice, 2020, 101:104462.
[20] SWEVERS J, GANSEMAN C, TUKEL D B, et al. Optimal robot excitation and identification[J]. IEEE transactions on Robotics and Automation, 1997, 13(5):730-740.
[21] PANDIAN S R, SAKAGAMI N. A neuro-fuzzy controller for underwater robot manipulators[C]//2010 11th International Conference on Control Automation Robotics&Vision. Singapore:IEEE, 2010:2135-2140.
[22] 刘金琨.滑模变结构控制MATLAB仿真[M]. 4版.北京:清华大学出版社, 2019. LIU J K. Sliding mode control design and MATLAB simulation[M]. 4th ed. Beijing:Tsinghua University Press, 2019.(in Chinese) |
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2023,
Vol. 63
