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Found 7 papers, 2 papers with code
State Space Models as Foundation Models: A Control Theoretic Overview
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models.
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
The proposed approach jointly optimizes a nominal nonlinear trajectory and an error feedback, requiring minimal offline design effort and offering low conservatism.
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory.
Asynchronous Computation of Tube-based Model Predictive Control
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
A System Level Approach to Regret Optimal Control
We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial initial conditions.
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
A System Level Approach to Tube-based Model Predictive Control
Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a priori determined tube controller in the prediction to tighten the constraints.
