Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.
|Title of host publication||Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022)|
|Publication status||Published - 2022|
|Event||IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico|
Duration: 6 Dec 2022 → 9 Dec 2022
|Conference||IEEE 61st Conference on Decision and Control (CDC 2022)|
|Period||6/12/22 → 9/12/22|
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- Computational modeling
- Aerospace electronics
- Computational efficiency
- Complex systems