Neural-network based online policy iteration for continuous-time infinite-horizon optimal control of nonlinear systems
Files
(Accepted version)
Date
2015
Authors
Tang, D.
Chen, L.
Tian, Z.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 2015 IEEE China Summit & International Conference on Signal and Information processing, 2015, pp.792-796
Statement of Responsibility
Difan Tang, Lei Chen, and Zhao Feng Tian
Conference Name
3rd IEEE China Summit & International Conference on Signal and Information processing (ChinaSIP 2015) (12 Jul 2015 - 15 Jul 2015 : Chengdu, China)
Abstract
A new policy-iteration algorithm based on neural networks (NNs) is proposed in this paper to synthesize optimal control laws online for continuous-time nonlinear systems. Latest advances in this field have enabled synchronous policy iteration but require an additional tuning loop or a logic switch mechanism to maintain system stability. A new algorithm is thus derived in this paper to address this limitation. The optimal control law is found by solving the Hamilton-Jacobi- Bellman (HJB) equation for the associated value function via synchronous policy iteration in a critic-actor configuration. As a major contribution, a new form of NN approximation for the value function is proposed, offering the closed-loop system asymptotic stability without additional tuning scheme or logic switch mechanism. As a second contribution, an extended Kalman filter is introduced to estimate the critic NN parameters for fast convergence. The efficacy of the new algorithm is verified by simulations.
School/Discipline
Dissertation Note
Provenance
Description
IEEE Catalog Number: CFP15SIP-USB
Access Status
Rights
© 2015 IEEE