The decentralized controller can be further improved by incorporating additional local information or coordination mechanisms between agents to enhance its performance and efficiency. Some strategies to enhance the decentralized controller include:
Local Model Fusion: Agents can share summarized local model information to improve the accuracy of the sensitivity matrix approximation. By fusing local models or sharing learned information, agents can collectively build a more accurate representation of the system dynamics, leading to better control decisions.
Dynamic Communication: Introducing dynamic communication mechanisms where agents selectively exchange critical information based on the current system state can enhance the decentralized controller's performance. Adaptive communication strategies can optimize the information flow between agents, improving coordination without excessive communication overhead.
Distributed Learning: Incorporating distributed learning algorithms within the decentralized controller can enable agents to adapt their control strategies based on local observations and interactions. By allowing agents to learn from their experiences and adjust their behavior autonomously, the decentralized controller can become more adaptive and responsive to changing system conditions.
Hierarchical Control: Implementing hierarchical control structures where agents operate at different levels of decision-making can improve coordination and efficiency. By organizing agents into hierarchies based on their roles and responsibilities, the decentralized controller can achieve better coordination and optimization of the overall system.
By integrating these additional elements into the decentralized controller, the system can benefit from enhanced coordination, improved decision-making, and increased adaptability, leading to better overall performance in optimizing the system's behavior.