Abstract
Models of inverse systems are commonly encountered in control, e.g., feedforward. The aim of this paper is to address several aspects in identification of inverse models, including model order selection and dealing with unstable inverse systems that originate from inverting non-minimum phase dynamics. A kernel-based regularization framework is developed for identification of non-causal systems. It is shown that ‘unstable’ models can be viewed as bounded, but non-causal, operators. As the main contribution, a range of the required kernels for non-causal systems is developed, including non-causal stable spline kernels. Benefits of the approach are confirmed in an example, including non-causal feedforward control for non-minimum phase systems.
| Original language | English |
|---|---|
| Article number | 108830 |
| Number of pages | 7 |
| Journal | Automatica |
| Volume | 114 |
| DOIs | |
| Publication status | Published - Apr 2020 |
Funding
This work is supported by Océ Technologies, The Netherlands, P.O. Box 101, 5900 MA Venlo, the Netherlands; and is part of the research programme VIDI with project number 15698, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). The material in this paper was partially presented at: [1] the 18th IFAC Symposium on System Identification, July 9–11, 2018, Stockholm, Sweden. [2] the 15th International Workshop on Advanced Motion Control (AMC2018), March 9–11, 2018, Tokyo, Japan. This paper was recommended for publication in revised form by Associate Editor Gianluigi Pillonetto under the direction of Editor Torsten Söderström
| Funders | Funder number |
|---|---|
| Océ Technologies | 15698 |
| Nederlandse Organisatie voor Wetenschappelijk Onderzoek | AMC2018 |
Keywords
- Feedforward control
- Kernel-based regularization
- Non-causal systems
- Reproducing kernel Hilbert space
- System identification