Name
Title | ||
|---|---|---|
Cooperative and Adaptive Optimal Output Regulation of Discrete-Time Multi-Agent Systems Using Reinforcement Learning |
Abstract | ||
|---|---|---|
This paper proposes an original data-driven intelligent control solution to the cooperative output regulation problem of discrete-time multi-agent systems. Based on the combination of internal model principle and reinforcement learning, a distributed suboptimal controller is learned realtime via online input-state data collected from system trajectories. Rigorous theoretical analysis guarantees the convergence of the proposed algorithm and the asymptotic stability of the closed-loop system. Numerical results validate the effectiveness of the proposed control methodology. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/RCAR.2018.8621852 | 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) |
Keywords | Field | DocType |
online input-state data,closed-loop system,adaptive optimal output regulation,discrete-time multiagent systems,reinforcement learning,internal model principle,distributed suboptimal controller,data-driven intelligent control | Convergence (routing),Intelligent control,Control theory,Control theory,Computer science,Multi-agent system,Exponential stability,Discrete time and continuous time,Internal model,Reinforcement learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-6870-2 | 0 | 0.34 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weinan Gao | 1 | 159 | 11.39 |
| Yiyang Liu | 2 | 12 | 3.30 |
| Adedapo Odekunle | 3 | 0 | 0.34 |
| Zhong-Ping Jiang | 4 | 4595 | 351.78 |
| Yunjun Yu | 5 | 1 | 1.71 |
| Pingli Lu | 6 | 5 | 3.81 |