Current-Iteration Non-Causal Iterative Learning Control for Beam Loading Cancellation in Particle Accelerators

被引：0

作者：

Shahriari, Zahra ^{[1
]}

Dumont, Guy Albert ^{[1
]}

Fong, Ken ^{[2
]}

机构：

[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada

[2] TRIUMF, Canadas Natl Lab Particle & Nucl Phys & Accelerat, Vancouver, BC V6T 2A3, Canada

来源：

2019 8TH INTERNATIONAL CONFERENCE ON SYSTEMS AND CONTROL (ICSC'19) | 2019年

关键词：

D O I：

10.1109/icsc47195.2019.8950532

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Iterative learning control (ILC) is an approach to improve the performance of a system that carries out the same operation repeatedly. In this work, ILC is used to reduce beam loading effect in cavity resonators. Since beam loading is a repeating disturbance, the error is repetitive as well, and the controller can use a non-causal learning function to preemptively counteract the beam loading effect. The learning filter parameters are chosen using simulations, then the controller is implemented on hardware to control the field of a normal conducting quarter wave cavity resonator.

引用

页码：170 / 175

页数：6

共 15 条

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