Containment Control of Heterogeneous Systems with Active Leaders of Bounded Unknown Control using Reinforcement Learning
This paper solves the containment problem of multi-agent systems on undirected graph with multiple active leaders using off-policy reinforcement learning (RL). The leaders are active in the sense that there exists bounded control input in the dynamics which is unknown to all followers and the followers are heterogeneous with different dynamics. Not only the steady states of agent i but also the transient trajectories are taken into account to impose optimality to the proposed containment control. Inhomogeneous algebraic Riccati equations (ARE) are derived to solve the optimal containment control protocol. To avoid the requirement of agents' dynamics to obtain containment control, an off-policy RL algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, a simulation example is presented to illustrate the effectiveness of the proposed algorithm.
Y. Yang et al., "Containment Control of Heterogeneous Systems with Active Leaders of Bounded Unknown Control using Reinforcement Learning," Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (2017, Honolulu, HI), Institute of Electrical and Electronics Engineers (IEEE), Nov 2017.
The definitive version is available at https://doi.org/10.1109/SSCI.2017.8285254
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 (2017: Nov. 27-Dec. 1, Honolulu, HI)
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Dynamics; Intelligent agents; Multi agent systems; Riccati equations; Active leaders; Containment control; Distributed observer; Heterogeneous systems; Model free; Reinforcement learning; Model-free
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Nov 2017