Vertical-Plane Locomotion Control of a High-Speed Robotic Tuna Via NMPC
Ru Tong, Sijie Li, Di Chen, Zhengxing Wu, Junzhi Yu
AI summary
Problem
Existing control methods for bionic underwater robots struggle to maintain stability and accuracy at high swimming speeds due to rapidly changing hydrodynamic forces and the kinematic coupling between pitch attitude and depth.
Approach
The authors analyze the robot's structural configuration for stability, build a hydrodynamic model from experimental data, and use a nonlinear model predictive controller to track a converted pitch-depth trajectory curve for integrated vertical-plane motion.
Key results
- Configuration analysis method optimizing gravity-buoyancy distribution and pectoral fin size for pitch maneuverability and roll stability
- Hydrodynamic system model capturing vertical-plane dynamics across varying swimming speeds
- Motion planning technique converting complex vertical sequences into equivalent pitch-depth trajectory curves
- Experimental validation demonstrating accurate depth and pitch tracking at both low and high speeds with successful complex motion execution
Why it matters
Enables reliable high-speed maneuvering for bionic underwater robots, advancing their practical deployment in dynamic ocean exploration and inspection tasks.
Abstract
The development of bionic underwater robots has brought new vitality to ocean exploration. Motion control is crucial for the stability of underwater robots due to significant differences in flow field characteristics at various swimming speeds. This study focuses on vertical-plane motion and proposes a model predictive control method to achieve integrated control of depth position and pitch attitude for bionic robotic fish. First, based on a robotic tuna system, high-maneuverability vertical-plane motion configuration elements are analyzed and summarized, laying the foundation for motion stability and con- trollability. Second, through hydrodynamic sampling in aquatic environments, a system model covering the range of swimming speeds is established. Regarding the control method, the proposed motion planning approach converts the desired motion sequence into an equivalent “pitch-depth” trajectory curve. A nonlinear model predictive controller (NMPC) is then designed to track the trajectory curve, ultimately achieving the desired vertical- plane motion. Experimental results validate that the proposed method not only ensures control accuracy under both low and high-speed conditions, but also enables the execution of complex motion sequence control. This study provides a fresh perspective on the motion instability analysis of robotic fish at high swimming speed and a novel control framework for regulating continuous posture sequences in the vertical plane. Note to Practitioners—The motivation of this paper is to address the challenges associated with stable motion and control of robotic fish in the vertical plane, given the variability of Received 23 December 2024; revised 22 May 2025; accepted 20 July 2025. Date of publication 25 July 2025; date of current version 31 July 2025. This article was recommended for publication by Associate Editor Y. Huang and Editor H. Moon upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 62233001, Grant T2121002, and Grant 62473236; in part by Beijing Nova Program under Grant 20240484499; and in part by Hebei Natural Science Foundation under Grant F2024203130. (Corresponding author: Junzhi Yu.) Ru Tong and Junzhi Yu are with the State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China, and also with the Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (e-mail: tongru@pku.edu.cn; junzhi.yu@ia.ac.cn). Sijie Li and Zhengxing Wu are with the Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China (e-mail: lisijie2020@ia.ac.cn; zhengxing.wu@ia.ac.cn). Di Chen is with the School of Intelligence Science and Technology, the Institute of Artificial Intelligence, and the Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems, Ministry of Educa- tion, University of Science and Technology Beijing, Beijing 100083, China (e-mail: di.chen@ustb.edu.cn). This article has supplementary downloadable material available at https://doi.org/10.1109/TASE.2025.3592696, provided by the authors. Digital Object Identifier 10.1109/TASE.2025.3592696 flow field characteristics at different swimming speeds. Existing methods for controlling pitch attitude and depth in bionic under- water robots are typically designed for stable flow conditions encountered during low-speed swimming. However, the instability and agility of high-speed robotic fish movements have not been adequately considered. Additionally, the coupling between pitch attitude and depth poses challenges for joint control of their com- bined states. This paper proposes a configuration analysis and control methodology to achieve desired vertical-plane locomotion for robotic fish. Specifically, using a robotic tuna as the research subject, a configuration analysis method for high-maneuverability motion in the vertical plane is presented, providing a foundation for ensuring motion stability and controllability. To accurately evaluate the motion of robotic fish under varying flow speeds, a system model for vertical plane motion is constructed based on hydrodynamic data collected from aquatic environments. A motion planning approach is proposed to convert desired vertical plane motion sequences into controllable “pitch-depth” trajectory curves, and a nonlinear model predictive controller is designed to track these trajectories. Configuration simulations and control experiments validate the effectiveness of the proposed method. Hopefully, our proposed methods can provide valuable insights and support for high-maneuverability motion control and continuous posture sequences tracking of bionic underwater robots in the vertical plane.