Abstract
The increasing demands for motion control result in a situation where Linear Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model feedforward control for LPV motion systems is challenging, since the inverse of an LPV system is often dynamically dependent on the scheduling sequence. The aim of this paper is to develop an identification approach that directly identifies dynamically scheduled feedforward controllers for LPV motion systems from data. In this paper, the feedforward controller is parameterized in basis functions, similar to, e.g., mass-acceleration feedforward, and is identified by a kernel-based approach such that the parameter dependency for LPV motion systems is addressed. The resulting feedforward includes dynamic dependence and is learned accurately. The developed framework is validated on an example.
Original language | English |
---|---|
Pages (from-to) | 6063-6068 |
Number of pages | 6 |
Journal | IFAC-PapersOnLine |
Volume | 56 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jul 2023 |
Event | 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/ |
Funding
This work is part of the research programme VIDI with project number 15698, which is (partly) financed by the Netherlands Or-ganisation for Scientific Research (NWO). In addition, this research has received funding from the ECSEL Joint Undertaking under grant agreement 101007311 (IMOCO4.E). The Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme.
Funders | Funder number |
---|---|
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | |
European Union's Horizon 2020 - Research and Innovation Framework Programme | |
Electronic Components and Systems for European Leadership | 101007311 |
Keywords
- Bayesian methods
- Linear parameter-varying systems
- Mechatronics
- Motion control systems
- data-driven control