A novel generalized value iteration scheme for uncertain continuous-time linear systems

Jae Young Lee, Jin Bae Park, Yoon Ho Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

In this paper, a novel generalized value iteration (VI) technique is presented which is a reinforcement learning (RL) scheme for solving online the continuous-time (CT) discounted linear quadratic regulation (LQR) problems without exactly knowing the system matrix A. In the proposed method, a discounted value function is considered, which is a general setting in RL frameworks, but not fully considered in RL for CT dynamical systems. Moreover, a stepwise-varying learning rate is introduced for the fast and safe convergence. In relation to this learning rate, we also discuss the locations of the poles of the closed-loop system and monotone convergence to the optimal solution. The results from these discussions give the conditions on the stability and monotone convergence of the existing VI methods.

Original languageEnglish
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages4637-4642
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: 2010 Dec 152010 Dec 17

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA
Period10/12/1510/12/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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