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Adaptive NN-based finite-time trajectory tracking control of wheeled robotic systems

  • Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems
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Abstract

The trajectory tracking and finite-time control problems of wheeled robotic systems with nonlinear dynamics and uncertainties are investigated in this paper. An adaptive neural network (NN)-based control technique is developed to deal with the nonlinearities and uncertainties. Then, finite-time dynamic control and kinematic control schemes are constructed on the basis of the adaptive estimations to remedy the negative influence of uncertainties and nonlinearities. Specific forward and azimuthal angular velocities are developed by using NN-based kinematic control schemes to obtain the finite-time tracking of the desired position trajectory for the wheeled robotic system. Furthermore, the asymptotic tracking of the specific forward and azimuthal angular velocities is further achieved based on the finite-time dynamic control schemes with uncertainties and nonlinear dynamics. The efficacy of the developed finite-time tracking control approach is substantiated by a robotic system.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Shandong Province for Excellent Young Scholars in Provincial Universities under Grant ZR2018JL022, in part by the Science and Technology Plan for Young Talents in Colleges and Universities of Shandong Province under Grant 2020KJN007, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018MF003, and in part by the National Natural Science Foundation of China under Grant 61773149, Grant 61772309, and Grant 92067108.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, Shandong, People’s Republic of China

    Xiaozheng Jin, Zhiye Zhao & Xiaoming Wu

  2. Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, 250014, Shandong, People’s Republic of China

    Xiaozheng Jin, Zhiye Zhao & Xiaoming Wu

  3. Shandong Laboratory of Computer Networks, Jinan, 250014, Shandong, People’s Republic of China

    Xiaozheng Jin, Zhiye Zhao & Xiaoming Wu

  4. Department of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, Shandong, People’s Republic of China

    Jing Chi

  5. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Chao Deng

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  1. Xiaozheng Jin
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Correspondence to Xiaozheng Jin or Jing Chi.

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Jin, X., Zhao, Z., Wu, X. et al. Adaptive NN-based finite-time trajectory tracking control of wheeled robotic systems . Neural Comput & Applic 34, 5119–5133 (2022). https://doi.org/10.1007/s00521-021-06021-7

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  • DOI: https://doi.org/10.1007/s00521-021-06021-7

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