Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/124816
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dc.contributor.author | Tang, D. | - |
dc.contributor.author | Chen, L. | - |
dc.contributor.author | Tian, Z. | - |
dc.contributor.author | Hu, E. | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | International Journal of Control, 2021; 94(5):1321-1333 | - |
dc.identifier.issn | 0020-7179 | - |
dc.identifier.issn | 1366-5820 | - |
dc.identifier.uri | http://hdl.handle.net/2440/124816 | - |
dc.description | Published online: 11 Aug 2019. | - |
dc.description.abstract | This study proposes a modified value-function-approximation (MVFA) and investi-gates its use under a single-critic configuration based on neural networks (NNs) for synchronous policy iteration (SPI) to deliver compact implementation of optimal control online synthesis for control-affine continuous-time nonlinear systems. Exist-ing single-critic algorithms require stabilising critic tuning laws while eliminating actor tuning. This paper thus studies alternative single-critic realisation aiming to relax the needs for stabilising mechanisms in the critic tuning law. Optimal control laws are determined from the Hamilton-Jacobi-Bellman equality by solving for the associated value function via SPI in a single-critic configuration. Different from other existing single-critic methods, an MVFA is proposed to deal with closed-loop stabil-ity during online learning. Gradient-descent tuning is employed to adjust the critic NN parameters in the interests of not complicating the problem. Parameters conver-gence and closed-loop stability are examined. The proposed MVFA-based approach yields an alternative single-critic SPI method with uniformly ultimately bounded closed-loop stability during online learning without the need for stabilising mecha-nisms in the critic tuning law. The proposed approach is verified via simulations. | - |
dc.description.statementofresponsibility | Difan Tang, Lei Chen, Zhao Feng Tian and Eric Hu | - |
dc.language.iso | en | - |
dc.publisher | Taylor & Francis | - |
dc.rights | © 2019 Informa UK Limited, trading as Taylor & Francis Group | - |
dc.source.uri | https://www.tandfonline.com/ | - |
dc.subject | Adaptive dynamic programming; approximate dynamic programming; neural networks; nonlinear control; optimal control; policy iteration | - |
dc.title | Modified value-function-approximation for synchronous policy iteration with single-critic configuration for nonlinear optimal control | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1080/00207179.2019.1648874 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Tang, D. [0000-0002-7143-0441] | - |
dc.identifier.orcid | Chen, L. [0000-0002-2269-2912] | - |
dc.identifier.orcid | Tian, Z. [0000-0001-9847-6004] | - |
dc.identifier.orcid | Hu, E. [0000-0002-7390-0961] | - |
Appears in Collections: | Aurora harvest 4 Mechanical Engineering publications |
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File | Description | Size | Format | |
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hdl_124816.pdf | Submitted version | 3.07 MB | Adobe PDF | View/Open |
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