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Bionic Fish Trajectory Tracking Based on a CPG and Model Predictive Control
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AI
DOI：10.1007/s10846-022-01644-x.png)
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Abstract
En 中文
Bionic fish have received widespread attention due to their high mobility, high concealment and high propulsion efficiency. Trajectory tracking and tracking accuracy are the main challenges in controlling the motion of bionic fish. To realize the trajectory tracking control of bionic fish, in this paper, nonlinear dynamics model of bionic fish is established by the Newton-Euler equation and Denavit-Hartenberg (D-H) coordinate transformation, and it is reasonably simplified. Then, a model predictive controller is established based on the dynamic model, and combined with a central pattern generator (CPG) network, a CPG-based model predictive controller (MPC-CPG controller) is proposed. Finally, simulations and experiments are carried out on the bionic fish, tracking the circular trajectory and straight trajectory. Experiments show that under the condition of initial error, the MPC-CPG controller can quickly eliminate the position error and heading angle error of the bionic fish, and track to the reference trajectory. For the tracking circular trajectory and straight trajectory, the position errors are kept at -6.9% similar to 14.9% and - 8.6% similar to 8.6% of the body length, respectively, and the heading angle errors are always kept at -4.76 degrees similar to 4.73 degrees and - 3.24 degrees similar to 3.55 degrees, respectively. Experiments verify the effectiveness of the proposed MPC-CPG.
Keywords:
Bionic fish
Dynamics
CPG
Model predictive control
Trajectory tracking
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K