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

• AcceptedNov 29, 2019
• PublishedMay 15, 2020
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### 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

[1] Chowdhury A R, Prasad B, Vishwanathan V, et al. Kinematics study and implementation of a biomimetic robotic-fish underwater vehicle based on Lighthill slender body model. In: Proceedings of 2012 IEEE/OES Autonomous Underwater Vehicles (AUV), 2012. Google Scholar

[2] Tai Fei , Kraus D, Zoubir A M. Contributions to Automatic Target Recognition Systems for Underwater Mine Classification. IEEE Trans Geosci Remote Sens, 2015, 53: 505-518 CrossRef ADS Google Scholar

[3] Zhang F, Thon J, Thon C. Miniature Underwater Glider: Design and Experimental Results. IEEE/ASME Trans Mechatron, 2014, 19: 394-399 CrossRef Google Scholar

[4] Yu J, Wang M, Dong H. Motion Control and Motion Coordination of Bionic Robotic Fish: A Review. J Bionic Eng, 2018, 15: 579-598 CrossRef Google Scholar

[5] Wu Z, Yu J, Su Z. Implementing 3-D high maneuvers with a novel biomimetic robotic fish. IFAC Proc Volumes, 2014, 47: 4861-4866 CrossRef Google Scholar

[6] Yu J, Wang C, Xie G. Coordination of Multiple Robotic Fish With Applications to Underwater Robot Competition. IEEE Trans Ind Electron, 2016, 63: 1280-1288 CrossRef Google Scholar

[7] Wang M, Yu J Z, Tan M. Multimodal swimming control of a robotic fish with pectoral fins using a CPG network. Chin Sci Bull, 2012, 57: 1209-1216 CrossRef ADS Google Scholar

[8] Liu J, Hu H. Biological inspiration: From carangiform fish to multi-joint robotic fish. J Bionic Eng, 2010, 7: 35-48 CrossRef Google Scholar

[9] Muller U K. Riding the waves: the role of the body wave in undulatory fish swimming.. Integrative Comp Biol, 2002, 42: 981-987 CrossRef PubMed Google Scholar

[10] Wu Z X, Yu J Z, Tan M. Acta Automatica Sin, 2013, 39: 2032-2042 CrossRef Google Scholar

[11] Feng C, Modarres-Sadeghi Y. A mechanical fish to emulate the fast-start performance of pike. In: Proceedings of Meeting of the Aps Division of Fluid Dynamics, 2010. Google Scholar

[12] Porez M, Boyer F, Ijspeert A J. Improved Lighthill fish swimming model for bio-inspired robots: Modeling, computational aspects and experimental comparisons. Int J Robotics Res, 2014, 33: 1322-1341 CrossRef Google Scholar

[13] Candelier F, Boyer F, Leroyer A. Three-dimensional extension of Lighthill's large-amplitude elongated-body theory of fish locomotion. J Fluid Mech, 2011, 674: 196-226 CrossRef ADS Google Scholar

[14] Coene R. The Swimming of Slender Fish-Like Bodies in Waves. In: Swimming and Flying in Nature. Berlin: Springer, 1975. 673--686. Google Scholar

[15] Su Z, Yu J, Tan M, et al. Bio-inspired design of body wave and morphology in fish swimming based on linear density. In: Proceedings of 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2010. Google Scholar

[16] Yu J, Tan M, Wang S. Development of a biomimetic robotic fish and its control algorithm.. IEEE Trans Syst Man Cybern B, 2004, 34: 1798-1810 CrossRef PubMed Google Scholar

[17] Sproewitz A, Moeckel R, Maye J. Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization. Int J Robotics Res, 2008, 27: 423-443 CrossRef Google Scholar

[18] Li X, Ren Q, Xu J. Precise Speed Tracking Control of A Robotic Fish via Iterative Learning Control. IEEE Trans Ind Electron, 2015, : 1-1 CrossRef Google Scholar

[19] Li X, Ren Q, Xu J X. Speed trajectory tracking of a robotic fish based on iterative learning control approach. In: Proceedings of the 10th Asian Control Conference, 2015. Google Scholar

[20] Iterative Learning Control: An Optimization Paradigm [Bookshelf]. IEEE Control Syst, 2017, 37: 185-186 CrossRef Google Scholar

[21] Verma S, Xu J X. Analytic Modeling for Precise Speed Tracking of Multilink Robotic Fish. IEEE Trans Ind Electron, 2018, 65: 5665-5672 CrossRef Google Scholar

[22] Morgansen K A, Triplett B I, Klein D J. Geometric Methods for Modeling and Control of Free-Swimming Fin-Actuated Underwater Vehicles. IEEE Trans Robot, 2007, 23: 1184-1199 CrossRef Google Scholar

[23] Ouyang P R, Zhang W J, Gupta M M. An adaptive switching learning control method for trajectory tracking of robot manipulators. Mechatronics, 2006, 16: 51-61 CrossRef Google Scholar

[24] Zou K, Wang C, Xie G, et al. Cooperative control for trajectory tracking of robotic fish. In: Proceedings of 2009 American Control Conference, 2009. 5504--5509. Google Scholar

[25] Yu L, Fei S, Sun L. An adaptive neural network switching control approach of robotic manipulators for trajectory tracking. Int J Comput Math, 2014, 91: 983-995 CrossRef Google Scholar

[26] Wang J, Kim J. Optimization of fish-like locomotion using hierarchical reinforcement learning. In: Proceedings of International Conference on Ubiquitous Robots and Ambient Intelligence, 2015. Google Scholar

[27] Liu J, Wu Z, Yu J. Sliding mode fuzzy control-based path-following control for a dolphin robot. Sci China Inf Sci, 2018, 61: 024201 CrossRef Google Scholar

[28] Yu J, Li X, Pang L. Design and attitude control of a novel robotic jellyfish capable of 3D motion. Sci China Inf Sci, 2019, 62: 194201 CrossRef Google Scholar

[29] Ji Z, Yu H. A New Perspective to Graphical Characterization of Multiagent Controllability.. IEEE Trans Cybern, 2017, 47: 1471-1483 CrossRef PubMed Google Scholar

[30] Chowdhury A R, Prasad B, Vishwanathan V, et al. Kinematics study and implementation of a biomimetic robotic-fish underwater vehicle based on Light hill slender body model. In: Proceedings of 2012 IEEE/OES Autonomous Underwater Vehicles (AUV), 2012. Google Scholar

• 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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