Title :
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
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;
Conference_Titel :
Industrial Electronics (ISIE), 2014 IEEE 23rd International Symposium on
Conference_Location :
Istanbul
DOI :
10.1109/ISIE.2014.6865002
