Nonlinear model predictive position control for a tail-actuated robotic fish
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ORIGINAL PAPER
Nonlinear model predictive position control for a tail-actuated robotic fish Pengfei Zhang · Zhengxing Wu · Yan Meng · Min Tan · Junzhi Yu
Received: 25 June 2020 / Accepted: 15 September 2020 © Springer Nature B.V. 2020
Abstract Position control is a significant technique for the underwater application of robotic fish; however, it is also very challenging due to the underactuated property and input coupling of system dynamics. In this article, a two-stage orientation–velocity nonlinear model predictive controller is proposed to solve this problem. A scaled averaging model of tail-actuated robotic fish is constructed at first. Then, the novel strategy based on orientation and velocity control is developed as well as proved to be equivalent with position control in the sense of Lyapunov. Furthermore, a nonlinear model predictive controller with a two-stage switching strategy is designed to regulate the orientation and velocity error. Finally, the simulation results demonstrate the superiority of the proposed control Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11071-020-05963-2) contains supplementary material, which is available to authorized users. P. Zhang · Z. Wu · Y. Meng · M. Tan · J. Yu (B) State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China e-mail: [email protected] P. Zhang · Z. Wu · Y. Meng · M. Tan School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China J. Yu State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, China
algorithm compared with other methods. Particularly, there exists an interesting twist-braking behavior in simulation, which indicates that the proposed method makes better use of the system dynamics. The proposed method is efficient for not only bionic robotic fish but also other aquatic underactuated robots, which offers new insight into the position control of underwater robots. Keywords Bionic robotic fish · Position control · Nonlinear model predictive control · Underactuated dynamics
1 Introduction The development of bionic robotic fish is an active area of underwater robotic research. Owing to its high maneuverability, high efficiency, and noiseless performance, bionic robotic fish is differentiated from the commercially developed autonomous underwater vehicles (AUVs) and holds tremendous promise for underwater applications undoubtedly [1,2]. However, the prerequisite is the robust and precise control for carrying out the underwater mission efficiently and successfully. In particular, the precise position control is an essential technique for the large amounts of underwater applications, e.g., underwater salvage and rescue, underwater sensor network deployment. At present, the vast majority of existing studies about the control problem of robotic fish involve swim
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