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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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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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  2. Qinyuan Zhou
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  3. Chenyang Shao
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  4. Yan Zhao
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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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