Experimentally verified point-to-point iterative learning control for highly coupled systems
Iterative learning control (ILC) is a well established approach for precision tracking control of systems which perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework accompanied by a wide array of application studies, comparatively little attention...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
2014.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Summary: | Iterative learning control (ILC) is a well established approach for precision tracking control of systems which perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework accompanied by a wide array of application studies, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) systems. Here the presence of interacting dynamics often correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and establishes links between interaction dynamics and reduced robustness to modelling uncertainty, and slower convergence. It then shows how these and other limitations can be addressed by relaxing the tracking requirement to include only a subset of time points along the time duration. This is the first analysis to show how so-called 'point-to-point' ILC can address performance limitations associated with highly coupled systems. Theoretical observations are tested using a novel MIMO experimental test facility which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results compare experimental observations with theoretical predictions over a wide range of interaction levels and with varying levels of injected noise. |
|---|