arXiv:2603.14762v3 Announce Type: replace-cross Abstract: We study supervisory switching control for partially-observed linear dynamical systems. The objective is to identify and deploy a suitable controller for the unknown system by periodically selecting among a collection of $N$ candidate controllers, some of which may destabilize the underlying system. While classical estimator-based supervisory control guarantees asymptotic stability, it lacks quantitative finite-time performance bounds. Conversely, current non-asymptotic methods in both online learning and system identification require r
The increasing complexity and autonomy of AI systems necessitate more robust and reliable control mechanisms, driving research into advanced supervisory methods.
This research addresses a critical limitation in current AI control systems for dynamic environments by offering improved stability and quantifiable performance for partially-observed systems, impacting the reliability of AI agents in real-world applications.
The ability to deploy AI controllers that can switch between candidates, including potentially destabilizing ones, with improved finite-time performance guarantees suggests a new paradigm for adaptive and resilient autonomous systems.
- · AI agents developers
- · Robotics industry
- · Process control sectors
- · Aerospace and defence
- · Developers of brittle or non-adaptive control systems
- · Sectors reliant on purely asymptotic stability guarantees
More reliable and adaptable autonomous systems become feasible across various industries.
Reduced need for human intervention in complex control environments as AI systems gain self-correction capabilities.
Accelerated adoption of AI in safety-critical applications due to enhanced stability and performance guarantees, potentially leading to fully autonomous operations in some high-stakes domains.
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Read at arXiv cs.LG