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Found 7 papers, 0 papers with code
Online Residual Learning from Offline Experts for Pedestrian Tracking
In this paper, we consider the problem of predicting unknown targets from data.
On the Regret of Recursive Methods for Discrete-Time Adaptive Control with Matched Uncertainty
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.
Predictive Linear Online Tracking for Unknown Targets
The learned model is used in the optimal policy under the framework of receding horizon control.
Closed-Loop Finite-Time Analysis of Suboptimal Online Control
Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to sub-optimality.
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providing suboptimal solutions.
Online Linear Quadratic Tracking with Regret Guarantees
Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions.
Implications of Regret on Stability of Linear Dynamical Systems
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for online learning.
