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Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network

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Abstract

For the social robot with serial elastic actuator, the joint dynamics model has the problems of strong coupling and high nonlinear, and the traditional PD control algorithm cannot achieve accurate trajectory tracking effect on the joint position of social robot using series elastic actuator. Therefore, an optimized Radial basis function (RBF) neural network adaptive control algorithm was proposed. The method based on RBF neural network approximates the social robot joint model parameters, an adaptive law was designed to estimate the weights of the neural network and the joint model online. The dynamic plane method is combined to improve the robustness of the control algorithm. The simulation results show that the trajectory tracking error peak of PD control algorithm is 0.2 rad. Compared with PD control algorithm, the trajectory tracking error peak of RBF neural network adaptive control algorithm based on dynamic surface optimization is reduced to ± 0.05 rad, which realizes accurate approximation of the parameters of social robot joint model, and accurate dynamics model approximation provides a theoretical basis for further research on human–robot interaction (HRI) of social robots.

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References

  1. Wang Y, Chen Y, Chen K, Wu Y, Huang Y (2017) A flat torsional spring with corrugated flexible units for series elastic actuators. In: 2nd International conference on advanced robotics and mechatronics (ICARM). IEEE, pp 138–143.

  2. Li X, Pan Y, Chen G, Yu H (2016) Adaptive human–robot interaction control for robots driven by series elastic actuators. IEEE Trans Rob 33(1):169–182

    Article  Google Scholar 

  3. Calanca A, Muradore R, Fiorini P (2017) Impedance control of series elastic actuators: passivity and acceleration-based control. Mechatronics 47:37–48

    Article  Google Scholar 

  4. Zhu Q, Mao Y, Xiong R, Wu J (2016) Adaptive torque and position control for a legged robot based on a series elastic actuator. Int J Adv Rob Syst 13(1):26

    Article  Google Scholar 

  5. Calzado J, Lindsay A, Chen C, Samuels G, Olszewska JI (2018). SAMI: interactive, multi-sense robot architecture. In: IEEE 22nd International conference on intelligent engineering systems (INES). IEEE, pp 000317–000322

  6. Brooks R (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23

    Article  Google Scholar 

  7. Murphy RR (2019) Introduction to AI robotics. MIT Press, Cambridge

    Google Scholar 

  8. Connell JH (1992) SSS: a hybrid architecture applied to robot navigation. In: ICRA, vol 3, pp 2719–2724

  9. Fikes RE, Nilsson NJ (1971) STRIPS: A new approach to the application of theorem proving to problem solving. ArtifIntell 2(3–4):189–208

    MATH  Google Scholar 

  10. Gat E, Bonnasso RP, Murphy R (1998) On three-layer architectures. Artificial intelligence and mobile robots 195:210

    Google Scholar 

  11. Mattar E (2013) A survey of bio-inspired robotics hands implementation: new directions in dexterous manipulation. Robotics and Autonomous Systems 61(5):517–544

    Article  Google Scholar 

  12. Liu X, Yang C, Chen Z, Wang M, Su CY (2018) Neuro-adaptive observer based control of flexible joint robot. Neurocomputing 275:73–82

    Article  Google Scholar 

  13. Kong K, Bae J, Tomizuka M (2009) Control of rotary series elastic actuator for ideal force-mode actuation in human–robot interaction applications. IEEE/ASME Trans Mechatron 14(1):105–118

    Article  Google Scholar 

  14. Kong K, Bae J, Tomizuka M (2011) A compact rotary series elastic actuator for human assistive systems. IEEE/ASME Trans Mechatron 17(2):288–297

    Article  Google Scholar 

  15. Yoo S, Chung WK (2015) SEA force/torque servo control with model-based robust motion control and link-side motion feedback. In: IEEE international conference on robotics and automation (ICRA). IEEE, pp 1042–1048

  16. Schindlbeck C, Haddadin S (2015) Unified passivity-based cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. In: 2015 IEEE international conference on robotics and automation (ICRA). IEEE, pp 440–447

  17. Soltanpour MR, Moattari M (2019) Voltage based sliding mode control of flexible joint robot manipulators in presence of uncertainties. Robot AutonSyst 118:204–219

    MATH  Google Scholar 

  18. Cambera JC, Feliu-Batlle V (2017) Input-state feedback linearization control of a single-link flexible robot arm moving under gravity and joint friction. Robot AutonSyst 88:24–36

    Google Scholar 

  19. He W, Amoateng DO, Yang C, Gong D (2016) Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis. IET Control Theory Appl 11(4):567–575

    Article  MathSciNet  Google Scholar 

  20. He W, David AO, Yin Z, Sun C (2015) Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans Syst Man CybernSyst 46(6):759–770

    Article  Google Scholar 

  21. Sun L, Li M, Wang M, Yin W, Sun N, Liu J (2020) Continuous finite-time output torque control approach for series elastic actuator. MechSyst Signal Process 139:105853

    Google Scholar 

  22. Wei J, Cao D, Liu L, Huang W (2017) Global mode method for dynamic modeling of a flexible-link flexible-joint manipulator with tip mass. Appl Math Model 48:787–805

    Article  MathSciNet  Google Scholar 

  23. Olszewska JI, Houghtaling M, Goncalves PJ, Fabiano N, Haidegger T, Carbonera JL et al (2020) Robotic standard development life cycle in action. J Intell Rob Syst 98(1):119–131

    Article  Google Scholar 

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Acknowledgements

We thank all the participants, as well as the graduate research team led by Professor Zhou Qinyuan from Central South University of Forestry and Technology, for facilitating the discussion and providing valuable feedback. In particular, Dr. Zhou played an important role in the careful guidance in the writing process of this article. Finally, We are very grateful to Chenyang Shao, Yanzhao, Rirong Lu, Xianzhe Hu, members of the Robotics Research Group in the School of Mechatronics Engineering of Central South University of Forestry and Technology for their expertise and efforts in programming robots.

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  1. School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China

    Nianfeng Shao, Qinyuan Zhou, Chenyang Shao & Yan Zhao

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  1. Nianfeng Shao
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Correspondence to Nianfeng Shao.

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Shao, N., Zhou, Q., Shao, C. et al. Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network. Int J of Soc Robotics 13, 1823–1832 (2021). https://doi.org/10.1007/s12369-021-00762-0

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  • DOI: https://doi.org/10.1007/s12369-021-00762-0

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