TY - JOUR
T1 - Iterative Learning Control for uncertain systems : robust monotonic convergence analysis
AU - Wijdeven, van de, J.J.M.
AU - Donkers, M.C.F.
AU - Bosgra, O.H.
PY - 2009
Y1 - 2009
N2 - In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on mu analysis. As a result, we can handle additive and multiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC.
AB - In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on mu analysis. As a result, we can handle additive and multiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC.
U2 - 10.1016/j.automatica.2009.06.033
DO - 10.1016/j.automatica.2009.06.033
M3 - Article
SN - 0005-1098
VL - 45
SP - 2383
EP - 2391
JO - Automatica
JF - Automatica
IS - 10
ER -