A pragmatic approach to distributed nonlinear model predictive control: Application to a hydrostatic drivetrain. (10th September 2014)

• Record Type: Journal Article
• Authors: Dutta, Abhishek; Ionescu, Clara; De Keyser, Robain; Camacho, Eduardo F.; Bordons, Carlos
• Is Part Of: Optimal control applications and methods. Volume 36:Number 3(2015:May/Jun.)
• Page Start: 369
• Page End: 380
• Publication Date: 2014-09-10
• Subjects: Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2141
• Languages: English
• ISSNs: 0143-2087

Abstract

<abstract abstract-type="main" id="oca2141-abs-0001"> <title>SUMMARY</title> <p id="oca2141-para-0001">The global control of large‐scale production machines composed of interacting subsystems is a challenging problem due to the intrinsic presence of high coupling, constraints, nonlinearity, and communication limitations. In this work, a pragmatic approach to distributed nonlinear model predictive control (DNMPC) is presented with guaranteed decrease in cost. Furthermore, in order to tackle time‐varying process dynamics, a learning algorithm is developed, thereby improving the performance of the global control. The proposed control framework is experimentally validated on a hydrostatic drivetrain, which exhibits nonlinear dynamics, strongly interacting subsystems. The experimental results indicate that good tracking performance and disturbance rejection can be obtained by the proposed DNMPC. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>