An indirect iterative learning controller for nonlinear systems with mismatched uncertainties and matched disturbances

The article proposes an intelligent controller for output regulation of nonlinear non-autonomous continuous-time systems with mismatched uncertainties and matched disturbances. This controller is established by firstly converting the original system into an input-to-state stable one via learning-bas...

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Bibliographic Details
Published in:International journal of systems science 2022-12, Vol.53 (16), p.3375-3389
Main Authors: Thanh Cao, Trung, Doan Nguyen, Phuoc, Hoai Nguyen, Nam, Thu Nguyen, Ha
Format: Article
Language:English
Subjects:
Compensators
Continuous time systems
Controllers
Disturbance compensation
Disturbances
indirect ILC
Iterative methods
Learning
Nonlinear control
Nonlinear systems
sampled data system
Sampled data systems
Uncertainty
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Summary:The article proposes an intelligent controller for output regulation of nonlinear non-autonomous continuous-time systems with mismatched uncertainties and matched disturbances. This controller is established by firstly converting the original system into an input-to-state stable one via learning-based compensation of lumped system disturbances and then applying the standard P-type iterative learning control. The learning-based disturbance compensator is basically created with the numerical backward differentiation in iterative mode. The control performance of the obtained sampled data system using the proposed controller has been verified throughout both in theory and comparison with the existing method via numerical simulations.
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2022.2083259