MODEL-BASED BIOMORPHIC UNDERWADER ROBOTS SYSTEM CONTROL DESIGN
Abstract
Currently, the field of underwater robotics is actively developing to solve applied and research problems. One of the promising areas of underwater robots’ application is the implementation of bioinspired type of swimming. The use of autonomous bioinspired underwater vehicles (BUV) will potentially expand the scope of application of low-noise and safe for local fauna underwater robots for monitoring and exploring the terrain. The aim of the work is to develop and test a methodology for model-based design of a motion control system for biomorphic underwater robots. In this work a typical BUV design with oscillatory type of swimming is considered. Problematic issues of modeling the BUV dynamics, as well as the synthesis of their control systems are described. For BUVs with oscillatory types of swimming, typical technological operations are identified. Typical technological operations are chosen based on the design features of the BUVs and the composition of their propulsion and steering complex. A control system design methodology based on the combined use of numerical modeling technologies and classical automatic control theory is proposed. Based on the proposed methodology, numerical hydrodynamic BUV’s models with oscillatory types of swimming are developed. Identification computational experiments are conducted. The transient processes which characterize BUV’s dynamics during the performance of each typical operation are defined. Based on the simulation results, cybernetic simplified models of BUV’s based on the typical blocks of the automatic control theory are developed. Based on the cybernetic models, based on the numerical optimization a synthesis of BUV’s control system in accordance with the proposed methodology is performed. The developed algorithms are tested based on numerical hydrodynamic simulation results. Possible prospects for the use of the BUV’s are formulated.
References
1. Xiaofeng Z. et al. Review of research and control technology of underwater bionic robots // Journal of
Marine Science and Engineering. – 2023. – P. 1-28.
2. Fossen T.I. Handbook of Marine Craft Hydrodynamics and Motion Control. – Hoboken, NJ: Wiley,
2021. – 736 с.
3. Pshikhopov V., Gurenko B., Shapovalov I., Beresnev M. Development and research of path-planning
module for control system of underwater vehicle // Internation Journal of Mechanical Engineering and
Robotics Research. – 2016. – Vol. 5, No. 4. – P. 301-304.
4. Пантов Е.Н., Махинин Н.Н., Шереметов Б.Б. Основы теории движения подводных аппаратов.
– Л.: Судостроение, 1973. – 216 с.
5. Gong Y. et al Investigating the Influence of Counter-flow Regions on the Hydrodynamic Performance
of Biomimetic Robotic Fish // Biomimetics. – 2024. – Vol. 9, No. 425. – P. 1-19.
6. Yan X., Ma Y. A Distributed Control Method for Flexible Robotic Fish Based on PDE // IET Control
Theory and Applications. – 2023. – Vol. 17. – P. 1930-1943.
7. Щур Н.А., Половко С.А., Деулин А.А. Применение методов вычислительной гидродинамики для
получения характеристик переходных процессов АНПА // Робототехника и техническая кибер-
нетика. – 2020. – Т. 8, №4. – С. 287-295.
8. Ламб Г. Гидродинамика. – М.: ОГИЗ, 1947. – 929 с.
9. Kastalsky I.A., Gordleeva S.Y., Hramov A.E., Kasantsev V.B. Bridging nonlinear dynamics and physiology:
implications for CPGs and biomimetic robotics. Reply to comments on “control of movement
of underwater swimmers: animals, simulated animates and swimming robots” // Physic of life reviews.
– 2024. – Vol. 50. – P. 32-34.
10. Cafer Bal et. al. CPG-based autonomous swimming control for multi-tasks of a biomi-metic robotic
fish // Ocean Engineering. – 2019 – Vol. 189. – P. 1-24.
11. Ijspeert A. Central pattern generators for locomotion control in animals and robots: A review // Neural
Networks. – 2008. – Vol. 21, Iss. 4. – P. 642-653.
12. Buşoniu L. et al. Reinforcement Learning for Control: Performance, Stability, and Deep
Approximators // Annual Re-views in Control. – 2018. – Vol. 46. – P. 8-28.
13. Xiaozhu L., Xiaopei L., Yang W. Learning Agile Swimming: An End-to-End Approach without CPGs
// IEEE Robotics and Automation Letters. – 2025. – P. 1-8.
14. Jingdong L., Lynne E., Raj M. Reinforcement Learning for Autonomous Robotic Fish // Studies in
Computational Intelligence – 2007. – Vol. 50. – P. 121-135.
15. Горюнов В.В., Половко С.А., Щур Н.А. Технология создания кибернетических моделей для син-
теза и отработки регуляторов системы управления движением автономного необитаемого под-
водного аппарата // Робототехника и техническая кибернетика. – 2020. – Т. 8, № 4. – С. 308-318.
16. Юревич Е. И. Теория автоматического управления. – Л.: Энергия, 1975. – 413 с.
17. Kwakernaak H., Sivan R. Linear Optimal Control Systems. – 1st ed. – New York: Wiley-Interscience,
1972. – 608 с.
18. Reshmi K.R., Priya P.S. Design and Control of Autonomous Unerwater Vehicle for Depth Control
Using LQR Controller // International Journal of Science and Research. – 2016. – Vol. 5, Iss. 7.
– P. 1432-1436.
19. Poli R. Analysis of the Publications on the Applications of Particle Swarm Optimisation // Journal of
Artificial Evolution and Applications. – 2008. – Vol. 2008. – P. e685175.
20. Avriel M. Nonlinear Programming: Analysis and Methods. Nonlinear Programming. – Eaglewood
Cliffs: Prentice-Hall Inc., 2003. – 548 p.
