| 摘要: |
| In this paper, a quasi-Newton-type optimized iterative learning control (ILC)
algorithm is investigated
for a class of discrete linear time-invariant systems. The proposed learning
algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the
inversion of the plant. By means of the mathematical inductive method, the monotone
convergence of the proposed algorithm is analyzed, which shows
that the tracking error monotonously converges to zero after a
finite number of iterations. Compared with the existing optimized ILC
algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster
convergent rate and is robust to the ill-condition of the
system model, and thus owns a wide range of applications.
Numerical simulations demonstrate the validity and
effectiveness. |
| 关键词: Iterative learning control, optimization, quasi-Newton method, inverse plant |
| DOI： |
| Received:November 05, 2014Revised:July 04, 2015 |
| 基金项目: |
|
| Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems |
| Y. Geng,X. Ruan |
| (School of Mathematics and Statistics, Xi'an Jiaotong University,
Xi'an Shaanxi 710049, China) |
| Abstract: |
| In this paper, a quasi-Newton-type optimized iterative learning control (ILC)
algorithm is investigated
for a class of discrete linear time-invariant systems. The proposed learning
algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the
inversion of the plant. By means of the mathematical inductive method, the monotone
convergence of the proposed algorithm is analyzed, which shows
that the tracking error monotonously converges to zero after a
finite number of iterations. Compared with the existing optimized ILC
algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster
convergent rate and is robust to the ill-condition of the
system model, and thus owns a wide range of applications.
Numerical simulations demonstrate the validity and
effectiveness. |
| Key words: Iterative learning control, optimization, quasi-Newton method, inverse plant |
