Can I optimize LQR control with unknown systems using output feedback?
Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation
This paper addresses the optimal control of unknown linear systems using output feedback, a classic problem in control theory. It develops a learning-based approach, akin to a single-agent reinforcement learning scenario, where the controller learns the optimal control strategy without needing the full system dynamics. While not explicitly about multi-agent systems, the proposed dynamic output feedback controller with an internal model can be viewed as a specialized form of agent with internal state representation. The learning process, analogous to single-agent RL, could be extended to multi-agent scenarios where multiple controllers/agents learn to coordinate based on limited system information (output feedback). The stability analysis and switched iteration scheme could inform similar approaches in multi-agent learning where stability and convergence are critical. The use of data-driven methods aligns with how LLMs learn from data to generate actions/control policies. This work could potentially lay the groundwork for more complex multi-agent systems where agents with LLM-based internal models learn to control complex systems collaboratively through output feedback.