Bionic Fish Trajectory Tracking Based on a CPG and Model Predictive Control

Yan, Zheping; Yang, Haoyu; Zhang, Wei; Gong, Qingshuo; Lin, Fantai

doi:10.1007/s10846-022-01644-x

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Generally, the design of CPG usually uses a mathematical model to simulate the direct mutual inhibition and alternating excitation mechanism of two neurons. The commonly used neuron oscillator models are recursive neural oscillators, phase oscillators and Hopf oscillators (Yan et al , 2022).…”

Section: Design Of Motion Control Systemmentioning

confidence: 99%

CPG-MPC controller for wheel-fin-flipper integrated amphibious robot

Qiao,

Wei,

et al. 2023

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Purpose The purpose of this paper is to introduce a motion control method for WFF-AmphiRobot, which can effectively realize the flexible motion of the robot on land, underwater and in the transition zone between land and water. Design/methodology/approach Based on the dynamics model, the authors selected the appropriate state variables to construct the state space model of the robot and estimated the feedback state of the robot through the maximum a posteriori probability estimation. The nonlinear predictive model controller of the robot is constructed by local linearization of the model to perform closed-loop control on the overall motion of the robot. For the control problem of the terminal trajectory, using the neural rhythmic movement theory in bionics to construct a robot central pattern generator (CPG) for real-time generation of terminal trajectory. Findings In this paper, the motion state of WFF-AmphiRobot is estimated, and a model-based overall motion controller for the robot and an end-effector controller based on neural rhythm control are constructed. The effectiveness of the controller and motion control algorithm is verified by simulation and physical prototype motion experiments on land and underwater, and the robot can ideally complete the desired behavior. Originality/value The paper designed a controller for WFF-AmphiRobot. First, when constructing the robot state estimator in this paper, the robot dynamics model is introduced as the a priori estimation model, and the error compensation of the a priori model is performed by the method of maximum a posteriori probability estimation, which improves the accuracy of the state estimator. Second, for the underwater oscillation motion characteristics of the flipper, the Hopf oscillator is used as the basis, and the flipper fluctuation equation is modified and improved by the CPG signal is adapted to the flipper oscillation demand. The controller effectively controls the position error and heading angle error within the desired range during the movement of the WFF-AmphiRobot.

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Section: Design Of Motion Control Systemmentioning

confidence: 99%

CPG-MPC controller for wheel-fin-flipper integrated amphibious robot

Qiao,

Wei,

et al. 2023

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“…In combination with the central pattern generator control system, the method of central neural network control for the underwater bionic robot is to closely simulate the engineering of a central pattern generator (CPG) in an animal rhythmic motion control area in biology. Then, there is a need to apply the method to practical engineering, and form a control method which is more intuitive, flexible, and easy for computer programming, thus realizing the rhythmic motion of the robot and improving the working performance of robots in various practical unstructured working environments [10].…”

Section: Materials Classification and Selection Of Underwater Bionic ...mentioning

confidence: 99%

A review on the application of bionic underwater robots in different underwater environment

Zhang

2023

ACE

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People have the spirit of constantly exploring the unknown areas of the world. The flow of fluid is ever-changing, and it can be simply divided into laminar flow and turbulent flow. In this context, the underwater robot was born. In order to cater to people's exploration of complex fluid flows such as lakes and oceans, bionic underwater robots with innate advantages have given full play to their role and become a certain trend. This paper mainly reviews the bionic underwater robots from three aspects, namely the prediction of the robot's trajectory in complex fluid, the rationality of its structural design, and the strength suitability of materials. Finally, the future of bionic underwater robots is analysed.

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“…This strategy aims to effectively address the trajectory tracking problem. Based on a central pattern generator (CPG), Yan et al put forth a model predictive controller designed for trajectory tracking control [9]. Many robotic fishes currently available are unable to accurately track their swimming trajectory.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control of four-jointed robotic fish based on Udwadia-Kalaba theory

Chen,

Wang,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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To improve the trajectory tracking control of the robotic fish driven by four-joint tail fins, the paper has established the dynamic model of four-joint tail fins on Udwadia-Kalaba theory and designed a sliding mode controller for trajectory tracking. Meanwhile, the stability of the robotic fish has been analyzed and discussed. A comparative study of the sliding mode controller and PID controller has been conducted on numerical simulation experiments. The results show the effectiveness and accuracy of the proposed control strategy. Compared with the PID controller, the sliding mode controller can converge to the desired value relatively quickly.

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Bionic Fish Trajectory Tracking Based on a CPG and Model Predictive Control

Cited by 12 publications

References 30 publications

CPG-MPC controller for wheel-fin-flipper integrated amphibious robot

CPG-MPC controller for wheel-fin-flipper integrated amphibious robot

A review on the application of bionic underwater robots in different underwater environment

Trajectory tracking control of four-jointed robotic fish based on Udwadia-Kalaba theory

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