A comparison of model‐based and data‐driven controller tuning. (30th May 2013)

• Record Type: Journal Article
• Authors: Formentin, Simone; van Heusden, Klaske; Karimi, Alireza
• Is Part Of: International journal of adaptive control and signal processing. Volume 28:Number 10(2014:Oct.)
• Page Start: 882
• Page End: 897
• Publication Date: 2013-05-30
• Subjects: Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2415
• Languages: English
• ISSNs: 0890-6327

Abstract

<abstract abstract-type="main" id="acs2415-abs-0001"> <title>SUMMARY</title> <p id="acs2415-para-0001">In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time‐consuming undertaking. When a set of input/output measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the 'model‐based' approach, a model of the system is first derived from data and then a controller is computed‐based on the model. In the 'data‐driven' approach, the controller is directly computed from data. In this work, the previous approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model‐based approach is always statistically more efficient, the data‐driven controller might outperform the model‐based solution for what concerns the final control cost. Copyright © 2013 John Wiley &amp; Sons, Ltd.</p> </abstract>