Minimax Q-learning design for H∞ control of linear discrete-time systems
- 4 February 2022
- journal article
- research article
- Published by Zhejiang University Press in Frontiers of Information Technology & Electronic Engineering
- Vol. 23 (3) , 438-451
Abstract
The H∞ control method is an effective approach for attenuating the effect of disturbances on practical systems, but it is difficult to obtain the HKeywords
This publication has 29 references indexed in Scilit:
- Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online DataIEEE Transactions on Neural Networks and Learning Systems, 2016
- Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence AnalysisIEEE Transactions on Cybernetics, 2016
- $ {H}_{ {\infty }}$ Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy Reinforcement LearningIEEE Transactions on Neural Networks and Learning Systems, 2015
- Online Adaptive Policy Learning Algorithm for $H_{\infty }$ State Feedback Control of Unknown Affine Nonlinear Discrete-Time SystemsIEEE Transactions on Cybernetics, 2014
- Off-Policy Reinforcement Learning for $ H_\infty $ Control DesignIEEE Transactions on Cybernetics, 2014
- Strategy Iteration Is Strongly Polynomial for 2-Player Turn-Based Stochastic Games with a Constant Discount FactorJournal of the ACM, 2013
- Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity controlAutomatica, 2007
- Value-function reinforcement learning in Markov gamesCognitive Systems Research, 2001
- Q-learningMachine Learning, 1992
- State-space solutions to standard H/sub 2/ and H/sub infinity / control problemsIEEE Transactions on Automatic Control, 1989