DocumentCode
701985
Title
A neural approximation to the explicit solution of constrained linear MPC
Author
Haimovich, H. ; Seron, M.M. ; Goodwin, G.C. ; Aguero, J.C.
Author_Institution
Centre for Integrated Dynamics and Control, The University of Newcastle, Callaghan, NSW 2308, Australia
fYear
2003
fDate
1-4 Sept. 2003
Firstpage
1081
Lastpage
1086
Abstract
The solution to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise affine (PWA) state feedback law defined on polyhedral regions of the state space. Even though real-time optimization is avoided, implementation of the PWA state-feedback law may still require a significant amount of computation due to the problem of determining which polyhedral region the state lies in. In this paper, a neural network approach to this problem is investigated.
Keywords
Approximation methods; Biological neural networks; Hypercubes; Neurons; Training; Trajectory; Neural networks; approximation; constrained linear control; explicit solution; model predictive control;
fLanguage
English
Publisher
ieee
Conference_Titel
European Control Conference (ECC), 2003
Conference_Location
Cambridge, UK
Print_ISBN
978-3-9524173-7-9
Type
conf
Filename
7085103
Link To Document