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Optimality of Norm-Optimal Iterative Learning Control Among Linear Time Invariant Iterative Learning Control Laws in Terms of Balancing Robustness and Performance.
- Source :
-
Journal of Dynamic Systems, Measurement, & Control . Apr2019, Vol. 141 Issue 4, p1-5. 5p. - Publication Year :
- 2019
-
Abstract
- This paper presents a frequency domain analysis toward the robustness, convergence speed, and steady-state error for general linear time invariant (LTI) iterative learning control (ILC) for single-input-single-output (SISO) LTI systems and demonstrates the optimality of norm-optimal iterative learning control (NO-ILC) in terms of balancing the tradeoff between robustness, convergence speed, and steady-state error. The key part of designing LTI ILC updating laws is to choose the Q-filter and learning gain to achieve the desired robustness and performance, i.e., convergence speed and steady-state error. An analytical equation that characterizes these three terms for NO-ILC has been previously presented in the literature. For general LTI ILC updating laws, however, this relationship is still unknown. Adopting a frequency domain analysis approach, this paper characterizes this relationship for LTI ILC updating laws and, subsequently, demonstrates the optimality of NO-ILC in terms of balancing the tradeoff between robustness, convergence speed, and steady-state error. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ITERATIVE learning control
*LINEAR time invariant systems
*ROBUST control
Subjects
Details
- Language :
- English
- ISSN :
- 00220434
- Volume :
- 141
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Dynamic Systems, Measurement, & Control
- Publication Type :
- Academic Journal
- Accession number :
- 134890684
- Full Text :
- https://doi.org/10.1115/1.4042091