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Adaptive sliding mode attitude control of two-wheel mobile robot with an integrated learning-based RBFNN approach

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Abstract

To eliminate the adverse effects of nonlinear external disturbances and model uncertainties on the attitude motion stability control of two-wheel mobile robot (TWMR), a novel adaptive sliding mode attitude control scheme is proposed for the TWMR by integrating minimum parameter learning method (MPLM) with radial basis function neural network (RBFNN). Based on the established dynamics model of a practical TWMR, a hyperbolic tangent function-based adaptive sliding mode controller is developed to remove the negative impacts imposed by the nonlinear external disturbances on the TWMR, together restraining the chattering effects caused by the large switching gain in sliding mode control. Next, a type of RBFNN is employed to approximate the model uncertainties of the TWMR system, and MPLM is introduced to replace the RBFNN weights with a single parameter, thereby reducing the fluctuations in attitude tracking and improving the real-time control capability. The stability of the developed controller is analyzed via Lyapunov stability theory. Lastly, a comparative simulation study is conducted to show that the proposed control method has better attitude tracking performance and strong anti-interference robustness.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 51675423.

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China

    Hui Pang, Minhao Liu & Fengqi Zhang

  2. Department of Mechanical Engineering, University of Alaska Fairbanks, Fairbanks, AK, USA

    Chuan Hu

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  1. Hui Pang
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  2. Minhao Liu
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  3. Chuan Hu
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  4. Fengqi Zhang
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Correspondence to Chuan Hu.

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Pang, H., Liu, M., Hu, C. et al. Adaptive sliding mode attitude control of two-wheel mobile robot with an integrated learning-based RBFNN approach. Neural Comput & Applic 34, 14959–14969 (2022). https://doi.org/10.1007/s00521-022-07304-3

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  • DOI: https://doi.org/10.1007/s00521-022-07304-3

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