Leader-Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader using Reinforcement Learning
This paper develops optimal control protocols for the distributed output synchronization problem of leader-follower multiagent systems with an active leader. Agents are assumed to be heterogeneous with different dynamics and dimensions. The desired trajectory is assumed to be preplanned and is generated by the leader. Other follower agents autonomously synchronize to the leader by interacting with each other using a communication network. The leader is assumed to be active in the sense that it has a nonzero control input so that it can act independently and update its control to keep the followers away from possible danger. A distributed observer is first designed to estimate the leader's state and generate the reference signal for each follower. Then, the output synchronization of leader-follower systems with an active leader is formulated as a distributed optimal tracking problem, and inhomogeneous algebraic Riccati equations (AREs) are derived to solve it. The resulting distributed optimal control protocols not only minimize the steady-state error but also optimize the transient response of the agents. An off-policy reinforcement learning algorithm is developed to solve the inhomogeneous AREs online in real time and without requiring any knowledge of the agents' dynamics. Finally, two simulation examples are conducted to illustrate the effectiveness of the proposed algorithm.
Y. Yang et al., "Leader-Follower Output Synchronization of Linear Heterogeneous Systems With Active Leader using Reinforcement Learning," IEEE Transactions on Neural Networks and Learning Systems, pp. 1 - 15, Institute of Electrical and Electronics Engineers (IEEE), Mar 2018.
The definitive version is available at https://doi.org/10.1109/TNNLS.2018.2803059
Electrical and Computer Engineering
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Algebra; Autonomous Agents; Heuristic Algorithms; Multi Agent Systems; Network Protocols; Optimization; Reinforcement Learning; Riccati Equations; Synchronization; Trajectories; Transient Analysis; Active Leaders; Algebraic Riccati Equations; Heterogeneous Systems; Learning (artificial Intelligence); Nonhomogeneous Media; Observers; Output Synchronization; Unknown Follower; Learning Algorithms; Active Leader; Heterogeneous System; Inhomogeneous Algebraic Riccati Equations (AREs); Protocols; Reinforcement Learning (RL); Trajectory; Unknown Follower
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Mar 2018