Optimality and flexibility in Iterative Learning Control for varying tasks

Abstract

DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

Multivariable iterative learning control: analysis and designs for engineering applications

“…To enhance the robustness to varying tasks, ILC approaches have been extended with basis functions in, e.g., [42,43,44,45,46,47,48,49]. However, since these approaches are developed in the norm-based optimization framework, the large burden imposed on the modeling requirements may prevent industrial implementation (R2).…”

“…Alternatively to the linear combination of basis functions utilized here, also rational (nonlinear) combinations of basis functions can be employed, see, e.g., [42,47,13,75]. Although these parametrizations potentially enable performance improvements, the associated optimization problem becomes non-convex, and often nonlinear algorithms are required for which, in general, convergence to the global minimum cannot be guaranteed.…”

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

“…Iterative learning control (ILC) is a useful control strategy to make systems operate in repetitive tasks and increase control precision by learning from the previous control experience. [21][22][23][24][25][26] Since ILC can achieve expected output trajectory tracking in a limited operation time and can be employed with no need to know perfect information of the target system, it is widely applied to robotic manipulators. In the work of Tayebi, 27 an adaptive ILC method is developed for rigid robot manipulators to realize trajectory tracking with unknown parameters.…”

Welding Process Tracking Control Based on Multiple Model Iterative Learning Control

“…For repetitive systems, iterative learning control (ILC) [12][13][14][15] is an effective control method. The algorithm does not rely on the accurate mathematical model of the dynamic system, and it can achieve expected output trajectory tracking in limited time and interval with little prior knowledge and computational effort.…”