Title :
A neural network approach to nonlinear model predictive control
Author :
Yan, Zheng ; Wang, Jun
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
Abstract :
This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach.
Keywords :
Jacobian matrices; convex programming; feedforward neural nets; learning (artificial intelligence); nonlinear control systems; predictive control; recurrent neural nets; Jacobain linearization; NMPC; convex programming problem; feedforward neural network; nonlinear model predictive control; recurrent neural network; supervised learning; unknown high-order term; Biological neural networks; Magnetic materials; Optimization; Photonic crystals; Recurrent neural networks; Testing; Vectors;
Conference_Titel :
IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-61284-969-0
DOI :
10.1109/IECON.2011.6119669
