A neurodynamic approach to bicriteria model predictive control of nonlinear affine systems based on a Goal Programming formulation

Author

Yan, Zheng ; Wang, Jun

Author_Institution

Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, China

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

7

Abstract

This paper presents a neurodynamic approach to bicriteria model predictive control (MPC) of nonlinear affine systems based on a goal programming formulation. Bicriteria MPC refers to finding optimal control inputs that minimizes two performance indexes corresponding to tracking errors and control efforts. The bicriteria MPC is formulated as the solution to a nonlinear optimization problem via goal programming technique and is solved by using a two-layer recurrent neural network. Simulation results are included to illustrate the effectiveness of the proposed approach.

Keywords

errors; minimisation; neurocontrollers; nonlinear control systems; nonlinear programming; optimal control; performance index; predictive control; recurrent neural nets; bicriteria MPC; bicriteria model predictive control; goal programming; neurodynamic approach; nonlinear affine systems; nonlinear optimization; optimal control input; performance index minimization; tracking errors; two-layer recurrent neural network; Optimal control; Optimization; Performance analysis; Recurrent neural networks; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252629

Filename

6252629