Journal ArticleDOI
Simultaneous policy update algorithms for learning the solution of linear continuous-time H∞ state feedback control
Huai-Ning Wu,Biao Luo +1 more
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TLDR
A simultaneous policy update algorithm (SPUA) for solving the Riccati equation (ARE) is proposed, and the convergence of the online SPUA is proved by demonstrating that it is mathematically equivalent to the offline S PUA.About:
This article is published in Information Sciences.The article was published on 2013-02-01. It has received 73 citations till now. The article focuses on the topics: Reinforcement learning & Riccati equation.read more
Citations
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Journal ArticleDOI
$ {H}_{ {\infty }}$ Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement Learning
TL;DR: This paper deals with the design of an H∞ tracking controller for nonlinear continuous-time systems with completely unknown dynamics and an off-policy reinforcement learning algorithm is used to learn the solution to the tracking HJI equation online without requiring any knowledge of the system dynamics.
Journal ArticleDOI
Off-Policy Reinforcement Learning for $ H_\infty $ Control Design
TL;DR: An off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved.
Journal ArticleDOI
Online adaptive policy learning algorithm for H∞ state feedback control of unknown affine nonlinear discrete-time systems.
TL;DR: An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem.
Journal ArticleDOI
Integral Reinforcement Learning for Linear Continuous-Time Zero-Sum Games With Completely Unknown Dynamics
TL;DR: An integral reinforcement learning algorithm based on policy iteration to learn online the Nash equilibrium solution for a two-player zero-sum differential game with completely unknown linear continuous-time dynamics is developed.
Journal ArticleDOI
Neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming
TL;DR: The neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming approach is investigated and it is shown that this robust controller can achieve optimality under a specified cost function.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Book
Aircraft Control and Simulation
Brian L. Stevens,Frank L. Lewis +1 more
TL;DR: Equations of Motion Building the Aircraft Model Basic Analytical and Computational Tools Aircraft Dynamics and Classical Design Techniques Modern Design Techniques Robustness and Multivariable Frequency-Domain Techniques Digital Control Appendices Index.
BookDOI
Approximate dynamic programming : solving the curses of dimensionality
TL;DR: This book discusses the challenges of dynamic programming, the three curses of dimensionality, and some experimental comparisons of stepsize formulas that led to the creation of ADP for online applications.
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Frank L. Lewis,Draguna Vrabie +1 more