Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 41 tok/s
Gemini 2.5 Pro 46 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 20 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 178 tok/s Pro
GPT OSS 120B 474 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

A Note on Linear Quadratic Regulator and Kalman Filter (2308.15798v1)

Published 30 Aug 2023 in math.OC, cs.SY, and eess.SY

Abstract: Two central problems in modern control theory are the controller design problem: which deals with designing a control law for the dynamical system, and the state estimation problem (observer design problem): which deals with computing an estimate of the states of the dynamical system. The Linear Quadratic Regulator (LQR) and Kalman Filter (KF) solves these problems respectively for linear dynamical systems in an optimal manner, i.e., LQR is an optimal state feedback controller and KF is an optimal state estimator. In this note, we will be discussing the basic concepts, derivation, steady-state analysis, and numerical implementation of the LQR and KF.

Citations (1)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Authors (1)