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The multiple-user terminals in a satellite transponder’s communication channel compete for limited radio resources to meet their own data rate needs. Because inter-user interference limits on the satellite transponder’s performance, the transponder’s power-control system needs to coordinate all its users to reduce interference and maximizes overall performance of this channel. This paper studies Stackelberg competition among the asymmetrical users in a transponder’s channel, where some users called leader have priority to choose their power control strategy, but other users called followers have to optimize their power control strategy with given leader’s controls. A Stackelberg Differential Game (SDG) is set up to model the Stackelberg competition in a transponder’s communication channel. Each user’s utility function is a trade-off between transmission data rate and power consumption. The dynamics of the system is the changing of channel gain. The optimality condition of Stackelberg equilibrium of leaders and followers is a set of Differential Algebraic Equations (DAE) with an imbedded control strategies from its counterpart. In order to solve for Stackelberg equilibrium, an algorithm based on optimizing leaders’ and followers’ Hamiltonians iteratively is developed. The numerical solution of the SDG model provides the transponder’s power control system with each user’s power-control strategy at the Stackelberg equilibrium.
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Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach
In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme.
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- Award ID(s):
- 2144646
- PAR ID:
- 10402345
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 89
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/electronics12010089
