Heterogeneous formation control of multiple rotorcrafts with unknown dynamics by reinforcement learning

被引：33

作者：

Liu, Hao ^{[1
,2
]}

Peng, Fachun ^{[1
,2
]}

Modares, Hamidreza ^{[3
]}

Kiumarsi, Bahare ^{[4
]}

机构：

[1] Beihang Univ, Sch Astronaut, Beijing 100191, Peoples R China

[2] Beihang Univ, Minist Educ, Key Lab Spacecraft Design Optimizat & Dynam Simul, Beijing 100191, Peoples R China

[3] Michigan State Univ, Dept Mech Engn, E Lansing, MI 48824 USA

[4] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA

来源：

INFORMATION SCIENCES | 2021年 / 558卷

基金：

中国国家自然科学基金;

关键词：

Formation control; Multi-agent systems; Heterogeneous systems; Reinforcement learning; Rotorcrafts;

D O I：

10.1016/j.ins.2021.01.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a distributed model-free solution based on reinforcement learning is proposed for the leader-follower formation control problem of heterogeneous multi-agent systems. The multi-agent system consists of multiple rotorcrafts involving a virtual leader and multiple followers, where the dynamics of leaders and followers is unknown. The formation control problem is firstly formulated as an optimal output regulation problem. A discounted performance function is then introduced to guarantee that the tracking error asymptotically converges to zero, and an online off-policy reinforcement learning algorithm is proposed to solve the optimal output problem online using the data generated along the trajectories of the agents. A simulation example is provided to validate the effectiveness of the proposed control method. (C) 2021 Elsevier Inc. All rights reserved.

引用

页码：194 / 207

页数：14

共 35 条

[1] Reinforcement Learning-Based Adaptive Optimal Exponential Tracking Control of Linear Systems With Unknown Dynamics [J].