Please use this identifier to cite or link to this item:
|Title:||An iterative learning controller with initial state learning||Authors:||Chen, Y.
Uncertainty nonlinear systems
|Issue Date:||1999||Citation:||Chen, Y., Wen, C., Gong, Z., Sun, M. (1999). An iterative learning controller with initial state learning. IEEE Transactions on Automatic Control 44 (2) : 371-376. ScholarBank@NUS Repository. https://doi.org/10.1109/9.746269||Abstract:||In iterative learning control (ILC), a common assumption is that the initial states in each repetitive operation should be inside a given ball centered at the desired initial states which may be unknown. This assumption is critical to the stability analysis, and the size of the ball will directly affect the final output trajectory tracking errors. In this paper, this assumption is removed by using an initial state learning scheme together with the traditional D-type ILC updating law. Both linear and nonlinear time-varying uncertain systems are investigated. Uniform bounds for the final tracking errors are obtained and these bounds are only dependent on the system uncertainties and disturbances, yet independent of the initial errors. Furthermore, the desired initial states can be identified through learning iterations.||Source Title:||IEEE Transactions on Automatic Control||URI:||http://scholarbank.nus.edu.sg/handle/10635/61812||ISSN:||00189286||DOI:||10.1109/9.746269|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 16, 2021
WEB OF SCIENCETM
checked on Oct 1, 2021
checked on Oct 14, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.