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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Bibliographic Details
Published in:	IEEE transactions on industrial electronics (1982) Vol. 70; no. 11; pp. 1 - 10
Main Authors:	Pan, Jie, Zhang, Pengfei, Wang, Jian, Liu, Mingxin, Yu, Junzhi
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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