Extended Kalman Filter based Resilient Formation Tracking Control of Multiple Unmanned Vehicles Via Game-Theoretical Reinforcement Learning
In This Paper, We Discuss the Resilient Formation Tracking Control Problem of Multiple Unmanned Vehicles (MUV). a Dynamic Leader-Follower Distributed Control Structure is Utilized to Optimize the Performance of the Formation Tracking. for the Follower of the MUV, the Leader is a Cooperative Unmanned Vehicle, and the Target of Formation Tracking is a Non-Cooperative Unmanned Vehicle with a Nonlinear Trajectory. Therefore, an Extended Kalman Filter (EKF) Observer is Designed to Estimate the State of the Target. Then the Leader of the MUV is Adjusted Dynamically According to the State of the Target. in Order to Describe the Interactions between the Follower and Dynamic Leader, a Stackelberg Game Model is Constructed to Handle the Hierarchical Decision Problems. at the Lower Layer, Each Follower Responds by Observing the Leader's Strategy, and the Potential Game is Used to Prove a Nash Equilibrium among All Followers. at the Upper Layer, the Dynamic Leader Makes Decisions Depending on the Response of All Followers to Reaching the Stackelberg Equilibrium. Moreover, the Stackelberg-Nash Equilibrium of the Designed Game Theoretical Model is Proven. a Novel Reinforcement Learning-Based Algorithm is Designed to Achieve the Stackelberg-Nash Equilibrium of the Game. Finally, the Effectiveness of the Method is Verified by a Variety of Formation Tracking Simulation Experiments.
L. Xue et al., "Extended Kalman Filter based Resilient Formation Tracking Control of Multiple Unmanned Vehicles Via Game-Theoretical Reinforcement Learning," IEEE Transactions on Intelligent Vehicles, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/TIV.2023.3237790
Electrical and Computer Engineering
Keywords and Phrases
extended Kalman filter; formation tracking; Games; Heuristic algorithms; leader-switching; Mathematical models; Observers; reinforcement learning; Stackelberg-Nash equilibrium; Target tracking; Trajectory; Vehicle dynamics
International Standard Serial Number (ISSN)
Article - Journal
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
01 Jan 2023