Jun 17, 2014 · In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems. An experimental case …

a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommu-tative MIMO systems. A convex optimization problem in ILC is achieved …

May 1, 2016 · The aim of this paper is to develop a novel ILC approach, by exploiting rational basis functions, that enables performance enhancement through iterative learning while …

Dec 1, 2013 · In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems.

In this paper, a new iterative. systems. An experimental case study confirms the advantages. well as the effectiveness of the proposed iterative algorithm. Index Terms—ILC, basis functions, …

In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems. An experimental case study …

A simulation example confirms (1) the advantages of rational basis functions compared to pre-existing results, and (2) the efficacy of the proposed iterative algorithm. Published in: 52nd …

In simulation (the rollout $y_k = G(q) (r + a_k)$ is simulated), this scheme is nothing other than an open-loop optimal-control strategy, while if $y_k = G(q) (r + a_k)$ amounts to performing an …

Basis function selection for ILC involves several unique requirements, including task flexibility, performance, and low-order parameterizations for implementation and noise attenuation.

Mar 1, 2015 · This brief introduces rational basis functions in ILC. Such rational basis functions have the potential to both increase performance and enhance the extrapolation properties.