Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Using Adaptive Multi-Agent Systems to Simulate Economic Models
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Automatica (Journal of IFAC)
Mean field asymptotics of Markov decision evolutionary games and teams
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle
SIAM Journal on Control and Optimization
A mean field approach for optimization in discrete time
Discrete Event Dynamic Systems
Large-scale games in large-scale systems
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Distributed output feedback control of Markov jump multi-agent systems
Automatica (Journal of IFAC)
| Hi-index | 22.15 |
Distributed control of the multi-agent systems involving a major agent and a large number of minor agents is investigated in this paper. There exist Markov jump parameters in the dynamic equation and random parameters in the index functions. The major agent has salient impact on others. Each minor agent merely has tiny influence, while the average effect of all the minor agents is not negligible, which plays a significant role in the evolution and performance index of each agent. Besides the state of the major agent, each minor agent can only access to the information of its state and parameters. Based on the mean field (MF) theory, a set of distributed control laws is designed. By the probability limit theory, the uniform stability of the closed-loop system and the upper bound of the corresponding index values are obtained. Via a numerical example, the consistency of the MF estimation and the influence of the initial state values and parameters on the index values are demonstrated.