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



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


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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