Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/93639
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Type: | Conference paper |
Title: | Neural-network based online policy iteration for continuous-time infinite-horizon optimal control of nonlinear systems |
Author: | Tang, D. Chen, L. Tian, Z. |
Citation: | Proceedings of the 2015 IEEE China Summit & International Conference on Signal and Information processing, 2015, pp.792-796 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Issue Date: | 2015 |
ISBN: | 9781479919475 |
Conference Name: | 3rd IEEE China Summit & International Conference on Signal and Information processing (ChinaSIP 2015) (12 Jul 2015 - 15 Jul 2015 : Chengdu, China) |
Statement of Responsibility: | Difan Tang, Lei Chen, and Zhao Feng Tian |
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. |
Keywords: | machine learning; neural network; policy iteration; optimal control; nonlinear system |
Description: | IEEE Catalog Number: CFP15SIP-USB |
Rights: | © 2015 IEEE |
DOI: | 10.1109/ChinaSIP.2015.7230513 |
Published version: | http://dx.doi.org/10.1109/chinasip.2015.7230513 |
Appears in Collections: | Aurora harvest 7 Mechanical Engineering publications |
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hdl_93639.pdf | Accepted version | 457.16 kB | Adobe PDF | View/Open |
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