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Found 8 papers, 5 papers with code
Learning to optimize with convergence guarantees using nonlinear system theory
The emerging paradigm of learning to optimize (L2O) automates the discovery of algorithms with optimized performance leveraging learning models and data - yet, it lacks a theoretical framework to analyze convergence and robustness of the learned algorithms.
Closing the Gap to Quadratic Invariance: a Regret Minimization Approach to Optimal Distributed Control
In this work, we focus on the design of optimal controllers that must comply with an information structure.
On the Guarantees of Minimizing Regret in Receding Horizon
Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion.
Regret Optimal Control for Uncertain Stochastic Systems
Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances.
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
We consider control of dynamical systems through the lens of competitive analysis.
Safe Control with Minimal Regret
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods.
Near-Optimal Design of Safe Output Feedback Controllers from Noisy Data
As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern.
A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees
Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems.
