Data-Driven Adaptive Tuning of Iterative Learning Control
In this paper,we propose two data-driven adaptive tuning(DDAT)approaches of PID-type ILC.First,we use a compact form iterative dynamic linearization(CFIDL)method to transfer the original nonlinear system into a equivalent linear data model,and we design an objective function to dynamically tune the learning gains of ILC law.Then,by optimizing the designed objective function,a CFIDL based DDAT method is proposed.This DDAT method only uses the real I/O data and doesn't need to know any mathematical model infor-mation.On this basis,we introduce a partial form iterative dynamic linearization(PFIDL)method to extend the research results,and propose a PFIDL based DDAT method.Both the proposed DDAT methods can help the PID-type ILC have a better robustness against to the uncertainties.Finally,the effectiveness of the two proposed DDAT-based ILC methods is verified by the simulations.
data-driven methodsadaptive tuning of learning gainsiterative learning con-troloptimizing
万方数据
维普
