Paper Info
Title
Approximate Model Predictive Control Laws For Constrained Nonlinear Discrete-Time Systems: Analysis And Offline Design
Abstract
The objective of this work consists in the offline approximation of possibly discontinuous model predictive control laws for nonlinear discrete-time systems, while enforcing hard constraints on state and input variables. Obtaining an offline approximation of the receding horizon control law may lead to a very significant reduction of the online computational burden with respect to algorithms based on iterated optimization, thus allowing the application to fast dynamics plants. The proposed approximation scheme allows to cope with discontinuous control laws, such as those arising from constrained nonlinear finite horizon optimal control problems. A detailed stability analysis of the closed-loop system driven by the approximated state-feedback controller shows that the devised technique guarantees the input-to-state practical stability with respect to the (non-fading) approximation-induced errors. Two examples are provided to show the effectiveness of the method when the approximator is chosen either as a discontinuous nearest point function or as a smooth neural network.
Year
DOI
Venue
2013
10.1080/00207179.2012.762121
INTERNATIONAL JOURNAL OF CONTROL
Keywords
Field
DocType
nonlinear model predictive control, neural networks
Control theory,Mathematical optimization,Nonlinear system,Optimal control,Control theory,Model predictive control,Systems analysis,Discrete time and continuous time,Artificial neural network,Law,Iterated function,Mathematics
Journal
Volume
Issue
ISSN
86
5
0020-7179
Citations 
PageRank 
References 
5
0.57
34
Authors
5
Name
Order
Citations
PageRank
Gilberto Pin113617.21
Filippo, M.2101.37
Felice Andrea Pellegrino38415.99
Gianfranco Fenu4186.72
Thomas Parisini591.38