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
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;
Conference_Titel :
European Control Conference (ECC), 2003
Conference_Location :
Cambridge, UK
Print_ISBN :
978-3-9524173-7-9
