Paper Info
Title
A receding-horizon regulator for nonlinear systems and a neural approximation
Abstract
A receding-horizon (RH) optimal control scheme for a discrete-time nonlinear dynamic system is presented. A nonquadratic cost function is considered, and constraints are imposed on both the state and control vectors. Two main contributions are reported. The first consists in deriving a stabilizing regulator by adding a proper terminal penalty function to the process cost. The control vector is generated by means of a feedback control law computed off line instead of computing it on line, as is done for existing RH regulators. The off-line computation is performed by approximating the RH regulator by means of a multilayer feedforward neural network (this is the second contribution of the paper). Bounds to this approximation are established. Simulation results show the effectiveness of the proposed approach.
Year
DOI
Venue
1995
10.1016/0005-1098(95)00044-W
Automatica
Keywords
Field
DocType
nonlinear system,neural approximation,receding-horizon regulator,feedback control,cost function,penalty function,discrete time,neural networks,neural network,nonlinear systems,optimal control
Regulator,Feedforward neural network,Mathematical optimization,Nonlinear system,Optimal control,Control theory,Artificial neural network,Mathematics,Discrete system,Computation,Penalty method
Journal
Volume
Issue
ISSN
31
10
0005-1098
Citations 
PageRank 
References 
76
19.86
5
Authors
2
Name
Order
Citations
PageRank
T Parisini1935113.17
R. Zoppoli227951.51