The field of control systems is moving towards increased autonomy and adaptability, with a focus on integrating machine learning and model-based control. Recent developments have enabled the automation of controller design and online adaptation, allowing for more efficient and effective control of complex systems. Additionally, there is a growing interest in understanding and controlling opinion dynamics in networks, with applications in social influence and decision-making. Noteworthy papers include: AURORA, which proposes a multi-agent framework for autonomous updating of reduced-order models and controllers. S2C, which integrates LLM agents with LMI-based synthesis to map natural-language requirements to certified H-infinity state-feedback controllers. Other notable works investigate the control of microbial consortia, hypergraphs, and opinion dynamics in signed time-varying networks, demonstrating the diversity and depth of current research in this area.
Advances in Autonomous Control and Opinion Dynamics
Sources
AURORA: Autonomous Updating of ROM and Controller via Recursive Adaptation
From Natural Language to Certified H-infinity Controllers: Integrating LLM Agents with LMI-Based Synthesis
A bioreactor-based architecture for in vivo model-based and sim-to-real learning control of microbial consortium composition
Data-driven Control of Hypergraphs: Leveraging THIS to Damp Noise in Diffusive Hypergraphs
Steering Opinion Dynamics in Signed Time-Varying Networks via External Control Input
On the complexity of freezing automata networks of bounded pathwidth
A Phase Transition for Opinion Dynamics with Competing Biases