Learning for Depth Control of a Robotic Penguin: A Data-Driven Model Predictive Control Approach

For bionic underwater robots, it is a great challenge for depth control due to model uncertainty and strong nonlinearity. To this end, we propose a data-driven model predictive control (MPC) approach using reinforcement learning (RL) for robotic penguin depth control. First, by imitating the underwa...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 70; číslo 11; s. 1 - 10
Hlavní autoři: Pan, Jie, Zhang, Pengfei, Wang, Jian, Liu, Mingxin, Yu, Junzhi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Back propagation networks
Bionics
Closed loops
Computational fluid dynamics
Controllers
data-driven model predictive control (MPC)
depth control
Liquids
Motion capture
Motion control
Neural networks
Predictive control
Prototypes
reinforcement learning (RL)
Robot control
Robot dynamics
Robot kinematics
Robotic penguin
Robotics
Robots
Tail
Tendons
Three-dimensional displays
Underwater robots
ISSN:0278-0046, 1557-9948
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Popis
Shrnutí:For bionic underwater robots, it is a great challenge for depth control due to model uncertainty and strong nonlinearity. To this end, we propose a data-driven model predictive control (MPC) approach using reinforcement learning (RL) for robotic penguin depth control. First, by imitating the underwater mode of the biological penguin, a robotic prototype with a tendon-driven head, two degrees of freedom wings, and a tendon-driven tail was designed. Then, a data-driven MPC framework is proposed considering the structure and motion properties of the robotic penguin. Specially, a data-based learning environment is constructed using a motion capture system, computational fluid dynamics, and a back-propagation neural network. Meanwhile, to maximize the benefits of the controller while ensuring safety and stability, a data-driven MPC using the RL scheme is applied to approximate the optimal policy. Combined with an appropriate reward design and periodic training, the closed-loop controller performance is significantly improved, and the validity of the proposed framework is finally tested by extensive simulations and experiments. Notably, this work will provide valuable insights into the learning-based motion control of bionic underwater robots.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3225840