Incremental model-based heuristic dynamic programming with output feedback applied to aerospace system identification and control

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Abstract

Sufficient information about system dynamics and inner states is often unavailable to aerospace system controllers, which requires model-free and output feedback control techniques, respectively. This paper presents a novel self-learning control algorithm to deal with these two problems by combining the advantages of heuristic dynamic programming and incremental modeling. The system dynamics is completely unknown and only input/output data can be acquired. The controller identifies the local system models and learns control polices online both by tuning the weights of neural networks. The novel method has been applied to a multi-input multi-output nonlinear satellite attitude tracking control problem. The simulation results demonstrate that, compared with the conventional actor-critic-identifier-based heuristic dynamic programming algorithm with three networks, the proposed adaptive control algorithm improves online identification of the nonlinear system with respect to precision and speed of convergence, while maintaining similar performance compared to the full state feedback situation.

Original languageEnglish
Title of host publicationCCTA 2020 - 4th IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages366-371
Number of pages6
ISBN (Electronic)9781728171401
DOIs
Publication statusPublished - 2020
Event4th IEEE Conference on Control Technology and Applications, CCTA 2020 - Virtual, Montreal, Canada
Duration: 24 Aug 202026 Aug 2020

Publication series

NameCCTA 2020 - 4th IEEE Conference on Control Technology and Applications

Conference

Conference4th IEEE Conference on Control Technology and Applications, CCTA 2020
CountryCanada
CityVirtual, Montreal
Period24/08/2026/08/20

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