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Backstepping Fuzzy Adaptive Control Based on RBFNN for a Redundant Manipulator

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13456))

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Abstract

Redundant manipulator is a highly nonlinear and strongly coupled system. In practical application, dynamic parameters are difficult to determine due to uncertain loads and external disturbances. These factors will adversely affect the control performance of manipulator. In view of the above problems, this paper proposes a backstepping fuzzy adaptive control algorithm based on the Radial Basis Function Neural Network (RBFNN), which effectively eliminates the influence of the internal uncertainty and external interference on the control of the manipulator. Firstly, the algorithm adopts the backstepping method to design the controller framework. Then, the fuzzy system is used to fit the unknown system dynamics represented by nonlinear function to realize model-free control of the manipulator. The fuzzy constants are optimized by RBFNN to effectively eliminate the control errors caused by unknown parameters and disturbance. Finally, in order to realize RBFNN approximating the optimal fuzzy constant, an adaptive law is designed to obtain the weight value of RBFNN. The stability of the closed-loop system is proved by using Lyapunov stability theorem. Through simulation experiments, the algorithm proposed in this paper can effectively track the target joint angle when the dynamic parameters of the 7-DOF redundant manipulator are uncertain and subject to external torque interference. Compared with fuzzy adaptive control, the tracking error of the algorithm in this paper is smaller, and the performance is better.

The work has been financially supported by Natural Science Foundation of China (Grant Nos. 51805449 and 62103291), Sichuan Science and Technology Program (Grant Nos. 2021ZHYZ0019 and 2022YFS0021) and 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant Nos. ZYYC21004 and ZYJC21081). All findings and results presented in this paper are by those of the authors and do not represent the funding agencies.

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References

  1. Burgar, C.G., Lum, P.S., Shor, P.C.: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J. Rehabil. Res. Dev. 37(6), 663–674 (2000)

    Google Scholar 

  2. Souza, D.A., Batista, J.G., dos Reis, L.L.N., Júnior, A.B.S.: PID controller with novel PSO applied to a joint of a robotic manipulator. J. Braz. Soc. Mech. Sci. Eng. 43(8), 1–14 (2021). https://doi.org/10.1007/s40430-021-03092-4

    Article  Google Scholar 

  3. Li, J.: Robot manipulator adaptive motion control with uncertain model and payload. Beijing Institute of Technology (2016)

    Google Scholar 

  4. Ren, Z.: The present situation and development trend of industrial robot. Equip. Manuf. Technol. 3, 166–168 (2015)

    Google Scholar 

  5. Yao, Q.: Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities. Adv. Space Res. 67(6), 1830–1843 (2021)

    Article  Google Scholar 

  6. Ghafarian, M., Shirinzadeh, B., Al-Jodah, A., et al.: Adaptive fuzzy sliding mode control for high-precision motion tracking of a multi-DOF micro/nano manipulator. IEEE Robot. Autom. Lett. 5(3), 4313–4320 (2020)

    Article  Google Scholar 

  7. Bui, H.: A deep learning-based autonomous robot manipulator for sorting application. In: IEEE Robotic Computing, IRC 2020 (2020)

    Google Scholar 

  8. Song, Z., Sun, K.: Prescribed performance adaptive control for an uncertain robotic manipulator with input compensation updating law. J. Franklin Inst. 358(16), 8396–8418 (2021)

    Article  MathSciNet  Google Scholar 

  9. Zhang, Z., Yan, Z.: An adaptive fuzzy recurrent neural network for solving the nonrepetitive motion problem of redundant robot manipulators. IEEE Trans. Fuzzy Syst. 28(4), 684–691 (2019)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, Chengdu, 610041, Sichuan, China

    Qinlin Yang

  2. Department of Mechanical Engineering, Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, 610041, Sichuan, China

    Qi Lu & Kang Li

  3. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China

    Xiangyun Li & Kang Li

  4. Med-X Center for Informatics, Sichuan University, Chengdu, 610041, Sichuan, China

    Xiangyun Li & Kang Li

Authors
  1. Qinlin Yang
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  2. Qi Lu
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  3. Xiangyun Li
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  4. Kang Li
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Corresponding author

Correspondence to Qi Lu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Yang, Q., Lu, Q., Li, X., Li, K. (2022). Backstepping Fuzzy Adaptive Control Based on RBFNN for a Redundant Manipulator. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_14

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  • DOI: https://doi.org/10.1007/978-3-031-13822-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13821-8

  • Online ISBN: 978-3-031-13822-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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