TY - CHAP
T1 - Multivariable iterative learning control
T2 - analysis and designs for engineering applications
AU - Blanken, Lennart
AU - van Zundert, Jurgen
AU - de Rozario, Robin
AU - Strijbosch, Nard
AU - Oomen, Tom
PY - 2019
Y1 - 2019
N2 - Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.
AB - Iterative Learning Control (ILC) enables high control performance through learning from measured data, using limited model knowledge, typically in the form of a nominal parametric model. Robust stability requires robustness to modeling errors, often due to deliberate undermodeling. The aim of this chapter is to outline a range of design approaches for multivariable ILC that is suited for engineering applications, with specific attention to addressing interaction using limited model knowledge. The proposed methods either address the interaction in the nominal model, or as uncertainty, i.e., through robust stability. The result is a range of techniques, including the use of the structured singular value (SSV) and Gershgorin bounds, that provide a different trade-off between modeling requirements, i.e., modeling effort and cost, and achievable performance. This allows control engineers to select the approach that fits best the modeling budget and control requirements. This trade-off is demonstrated in case studies on industrial printers. Additionally, two learning approaches are presented that are compatible with, and provide extensions to, the developed multivariable design framework: model-free iterative learning, and ILC for varying tasks.
KW - Control engineers
KW - Control performance
KW - Control requirements
KW - Engineering computing
KW - Gershgorin bounds
KW - ILC
KW - Interpolation and function approximation (numerical analysis)
KW - Iterative learning control
KW - Model knowledge
KW - Model-free iterative learning
KW - Modeling budget
KW - Nominal parametric model
KW - Self-adjusting control systems
KW - Structured singular value
UR - http://www.scopus.com/inward/record.url?scp=85093993174&partnerID=8YFLogxK
U2 - 10.1049/PBCE123E_ch7
DO - 10.1049/PBCE123E_ch7
M3 - Chapter
SN - 9781785617126
T3 - IET control, robotics and sensors series
SP - 109
EP - 143
BT - Data-driven modeling, filtering and control
A2 - Novara, Carlo
A2 - Formentin, Simone
PB - Institution of Engineering and Technology
CY - Stevenage
ER -