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An improved neural network tracking control strategy for linear motor-driven inverted pendulum on a cart and experimental study

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

Much recently, based on the discrete-time nonlinear output regulation (NOR) theory, a neural network (NN) method combined with feedforward friction compensation was proposed to tackle the tracking problem of the linear motor-driven inverted pendulum on a cart (IPC) system, where the NN was used to approximate the solution of the discrete regulator equations (DREs) for the IPC system whose dimension is equal to the sum of the dimensions of the system state and the control input. However, it is quite tedious to calculate the approximate solution and the feedforward friction compensation requires a complicated off-line friction identification procedure. This paper proposes an improved NN method combined with adaptive friction compensation for this tracking problem. In particular, the NN is used to approximate a feedforward function instead of the solution of the DREs. Since the dimension of the feedforward function is the same as that of the control input, the computational efficiency is improved. Moreover, the adaptive friction compensator is easy to implement since it does not need to establish detailed friction model. Experimental results demonstrate that our control strategy can lead to much more satisfactory tracking performance compared with the existing NN control strategy.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61873083 and Grant 62073217 and in part by the Fundamental Research Funds for the Central Universities under Grant PA2020GDKC0013.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Zhaowu Ping, Mengya Zhou, Chenxi Liu, Yunzhi Huang & Ming Yu

  2. Anhui Engineering Technology Research Center of Industrial Automation, Hefei, 230009, China

    Zhaowu Ping, Mengya Zhou, Chenxi Liu, Yunzhi Huang & Ming Yu

  3. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jun-Guo Lu

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  1. Zhaowu Ping
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  2. Mengya Zhou
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  3. Chenxi Liu
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  4. Yunzhi Huang
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  5. Ming Yu
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  6. Jun-Guo Lu
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Correspondence to Yunzhi Huang.

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Ping, Z., Zhou, M., Liu, C. et al. An improved neural network tracking control strategy for linear motor-driven inverted pendulum on a cart and experimental study. Neural Comput & Applic 34, 5161–5168 (2022). https://doi.org/10.1007/s00521-021-05986-9

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  • DOI: https://doi.org/10.1007/s00521-021-05986-9

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