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.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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
Fei T, Kraus D, Zoubir A M. Contributions to automatic target recognition systems for underwater mine classification. IEEE Trans Geosci Remote Sens, 2015, 53: 505–518
Zhang F, Thon J, Thon C, et al. Miniature underwater glider: design and experimental results. IEEE/ASME Trans Mechatron, 2014, 19: 394–399
Yu J, Wang M, Dong H, et al. Motion control and motion coordination of bionic robotic fish: a review. J Bionic Eng, 2018, 15: 579–598
Wu Z, Yu J, Su Z, et al. Implementing 3-D high maneuvers with a novel biomimetic robotic fish. IFAC Proc Vol, 2014, 47: 4861–4866
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
Wang M, Yu J Z, Tan M, et al. Multimodal swimming control of a robotic fish with pectoral fins using a CPG network. Chin Sci Bull, 2012, 57: 1209–1216
Liu J, Hu H. Biological inspiration: from carangiform fish to multi-joint robotic fish. J Bionic Eng, 2010, 7: 35–48
Muller U K, Stamhuis E J, Videler J J. Riding the waves: the role of the body wave in undulatory fish swimming. Integrative Comp Biol, 2002, 42: 981–987
Wu Z X, Yu J Z, Tan M. Comparison of two methods to implement backward swimming for a carangiform robotic fish. Acta Automatica Sin, 2013, 39: 2032–2042
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
Porez M, Boyer F, Ijspeert A J. Improved Lighthill fish swimming model for bio-inspired robots: modeling, computational aspects and experimental comparisons. Int J Robot Res, 2014, 33: 1322–1341
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
Coene R. The swimming of slender fish-like bodies in waves. In: Swimming and Flying in Nature. Berlin: Springer, 1975. 673–686
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
Yu J, Tan M, Wang S, et al. Development of a biomimetic robotic fish and its control algorithm. IEEE Trans Syst Man Cybern B, 2004, 34: 1798–1810
Sproewitz A, Moeckel R, Maye J, et al. Learning to move in modular robots using central pattern generators and online optimization. Int J Robot Res, 2008, 27: 423–443
Li X, Ren Q, Xu J X. Precise speed tracking control of a robotic fish via iterative learning control. IEEE Trans Ind Electron, 2015. doi: https://doi.org/10.1109/TIE.2015.2499719
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
Xia L. Iterative learning control: an optimization paradigm. IEEE Control Syst, 2017, 37: 185–186
Verma S, Xu J X. Analytic modeling for precise speed tracking of multilink robotic fish. IEEE Trans Ind Electron, 2018, 65: 5665–5672
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
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
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
Yu L, Fei S, Sun L, et al. An adaptive neural network switching control approach of robotic manipulators for trajectory tracking. Int J Comput Math, 2014, 91: 983–995
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
Liu J, Wu Z X, Yu J Z, et al. Sliding mode fuzzy control-based path-following control for a dolphin robot. Sci China Inf Sci, 2018, 61: 024201
Yu J Z, Li X B, Pang L, et al. Design and attitude control of a novel robotic jellyfish capable of 3D motion. Sci China Inf Sci, 2019, 62: 194201
Ji Z, Yu H. A new perspective to graphical characterization of multiagent controllability. IEEE Trans Cybern, 2017, 47: 1471–1483
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
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61573226, U1806204, U1909206).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, M., Zhang, Y., Dong, H. et al. Trajectory tracking control of a bionic robotic fish based on iterative learning. Sci. China Inf. Sci. 63, 170202 (2020). https://doi.org/10.1007/s11432-019-2760-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-019-2760-5