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SCIENCE CHINA Information Sciences, Volume 63 , Issue 7 : 170202(2020) https://doi.org/10.1007/s11432-019-2760-5

Trajectory tracking control of a bionic robotic fish based on iterative learning

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  • ReceivedAug 31, 2019
  • AcceptedNov 29, 2019
  • PublishedMay 15, 2020

Abstract

A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper, an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is proved. Finally, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61573226, U1806204, U1909206).


References

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  • Figure 1

    Three-joint bionic robot fish multi-link model.

  • Figure 2

    The tracking trajectories of the 10th iteration (a, c, e) and their corresponding convergent tracking errors (b, d, f) of joints 1, 2, and 3, respectively.

  • Figure 3

    (Color online) Screenshots of bionic robotic fish swimming.

  • Table 1   Joint angle values (rad)
    $q_{1}$ $q_{2}$ $q_{3}$
    0.018599 0.982761 0.668262
    0.019931 0.982589 0.671295
    0.021075 0.982439 0.674106
    0.022140 0.982299 0.676608
    0.023320 0.982147 0.679044
    0.024707 0.981975 0.681737
    0.025980 0.981793 0.685615
    0.027107 0.981623 0.687857
    0.028215 0.981471 0.690092
    0.029449 0.981322 0.693007
    0.030827 0.981151 0.696253
    0.032189 0.980964 0.699150
    0.033389 0.980785 0.701525
    0.034488 0.980627 0.703754
    0.035652 0.980471 0.706347
    0.036927 0.980300 0.709420
    0.038324 0.980108 0.712490
    0.039721 0.979917 0.715080
    0.040961 0.979745 0.717330
    0.042073 0.979586 0.719713
    0.043216 0.979418 0.722542

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