Data-driven self-tuning feedforward control by iterative learning control

Author

Noack, Rene ; Jeinsch, Torsten ; Sari, Adel Haghani Abandan ; Weinhold, N.

Author_Institution

Inst. of Autom., Univ. of Rostock, Rostock, Germany

fYear

2014

fDate

1-4 June 2014

Firstpage

2439

Lastpage

2444

Abstract

In this paper, a data-driven iterative learning control (ILC) based approach for the self-tuning of an existing feedforward controller with fixed structure of a nonlinear system is proposed. Compared to the standard ILC-based approaches, the proposed method consists of two main steps: the first step is the calculation of an input variable, based on an ILC algorithm, and the second step is the optimization of the given parameters of the feedforward controller. The performance and effectiveness of the proposed method are shown using a simulation model of a one stage turbocharged gasoline motor with wastegate.

Keywords

adaptive control; feedforward; iterative methods; learning systems; nonlinear control systems; optimisation; data-driven ILC-based approach; data-driven iterative learning control-based approach; data-driven self-tuning feedforward control; fixed structure; input variable; nonlinear system; one-stage turbocharged gasoline motor; parameter optimization; simulation model; wastegate; Feedforward neural networks; Iterative methods; Optimization; Petroleum; Standards; Tuning; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics (ISIE), 2014 IEEE 23rd International Symposium on

Conference_Location

Istanbul

Type

conf

DOI

10.1109/ISIE.2014.6865002

Filename

6865002