Pareto iterative learning control: Optimized control for multiple performance objectives

In-Gyu Lim

doi:10.1016/j.conengprac.2014.01.011

Pareto iterative learning control: Optimized control for multiple performance objectives

2014-05-01

15

PRE

AI

DOI：10.1016/j.conengprac.2014.01.011

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Iterative learning control (ILC) is a 2-degree-of-freedom technique that seeks to improve system performance along the time and iteration domains. Traditionally, ILC has been implemented to minimize trajectory-tracking errors across an entire cycle period. However, there are applications in which the necessity for improved tracking performance can be limited to a few specific locations. For such systems, a modified learning controller focused on improved tracking at the selected points can be leveraged to address multiple performance metrics, resulting in systems that exhibit significantly improved behaviors across a wide variety of performance metrics. This paper presents a pareto learning control framework that incorporates multiple objectives into a single design architecture. (C) 2014 Elsevier Ltd. All rights reserved.

Keyword:

Learning control

Pareto optimization

Multi-objective control

Pareto iterative learning control: Optimized control for multiple performance objectives

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