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Found 18 papers, 0 papers with code
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.
Finite Sample Frequency Domain Identification
The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\, d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively.
Predictive Linear Online Tracking for Unknown Targets
The learned model is used in the optimal policy under the framework of receding horizon control.
A Tutorial on the Non-Asymptotic Theory of System Identification
This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification.
The Fundamental Limitations of Learning Linear-Quadratic Regulators
We present a local minimax lower bound on the excess cost of designing a linear-quadratic controller from offline data.
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.
Suboptimality analysis of receding horizon quadratic control with unknown linear systems and its applications in learning-based control
In this work, we aim to analyze how the trade-off between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller.
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.
Statistical Learning Theory for Control: A Finite Sample Perspective
This tutorial survey provides an overview of recent non-asymptotic advances in statistical learning theory as relevant to control and system identification.
Secure state estimation over Markov wireless communication channels (extended version)
This note studies state estimation in wireless networked control systems with secrecy against eavesdropping.
How are policy gradient methods affected by the limits of control?
We study stochastic policy gradient methods from the perspective of control-theoretic limitations.
Learning to Control Linear Systems can be Hard
In this paper, we study the statistical difficulty of learning to control linear systems.
Adaptive Stochastic MPC under Unknown Noise Distribution
Then, we employ this benchmark controller to derive a novel robustly stable adaptive SMPC scheme that learns the necessary noise statistics online, while guaranteeing time-uniform satisfaction of the unknown reformulated state constraints with high probability.
Linear Systems can be Hard to Learn
Statistically easy to learn linear system classes have sample complexity that is polynomial with the system dimension.
Sparse Approximate Solutions to Max-Plus Equations with Application to Multivariate Convex Regression
In this work, we study the problem of finding approximate, with minimum support set, solutions to matrix max-plus equations, which we call sparse approximate solutions.
Online Learning of the Kalman Filter with Logarithmic Regret
When the system model is unknown, we have to learn how to predict observations online based on finite data, suffering possibly a non-zero regret with respect to the Kalman filter's prediction.
Sample Complexity of Kalman Filtering for Unknown Systems
We show that when the system identification step produces sufficiently accurate estimates, or when the underlying true KF is sufficiently robust, that a Certainty Equivalent (CE) KF, i. e., one designed using the estimated parameters directly, enjoys provable sub-optimality guarantees.
Finite Sample Analysis of Stochastic System Identification
In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics.
