Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered-Reference-Cited by-同舟云学术

Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered

Published:2024-03 Issue:3 Volume:9 Page:4493-4503
ISSN:2379-8904
Container-title:IEEE Transactions on Intelligent Vehicles
language:
Short-container-title:IEEE Trans. Intell. Veh.

Author:

Jin Long¹^ORCID,Liu Longqi¹^ORCID,Wang Xingxia²^ORCID,Shang Mingsheng³^ORCID,Wang Fei-Yue⁴^ORCID

Affiliation:

1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

3. Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

4. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7274857/10517859/10414012.pdf?arnumber=10414012

Reference34 articles.

1. Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon

2. A Multi-Vehicle Game-Theoretic Framework for Decision Making and Planning of Autonomous Vehicles in Mixed Traffic

3. Safe Trajectory Tracking in Uncertain Environments

4. Binocular Vision-Based Motion Planning of An AUV: A Deep Reinforcement Learning Approach

5. Adaptive Neural Network-Quantized Tracking Control of Uncertain Unmanned Surface Vehicles With Output Constraints

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