DocumentCode :
295011
Title :
Iterative learning control for discrete time systems using optimal feedback and feedforward actions
Author :
Amann, Notker ; Owens, David H. ; Rogers, Eric
Author_Institution :
Centre for Syt. & Control Eng., Exeter Univ., UK
Volume :
2
fYear :
1995
fDate :
13-15 Dec 1995
Firstpage :
1696
Abstract :
An algorithm for iterative learning control is proposed based on an optimization principle used by other authors to derive gradient type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realization in terms of Riccati feedback and feed-forward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants which can be arbitrarily changed by design parameters
Keywords :
Riccati equations; conjugate gradient methods; discrete time systems; feedback; feedforward; iterative methods; learning systems; optimal control; Riccati components; discrete-time systems; iterative learning control; optimal feedback; optimal feedforward; Algorithm design and analysis; Automatic control; Control systems; Convergence; Discrete time systems; Feedback; Feedforward systems; Iterative algorithms; Optimal control; Riccati equations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1995., Proceedings of the 34th IEEE Conference on
Conference_Location :
New Orleans, LA
ISSN :
0191-2216
Print_ISBN :
0-7803-2685-7
Type :
conf
DOI :
10.1109/CDC.1995.480384
Filename :
480384
Link To Document :
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