A neurodynamic approach to model predictive control of piecewise linear systems

Author

Yan, Zheng ; Wang, Jun

Author_Institution

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

fYear

2012

fDate

15-17 July 2012

Firstpage

463

Lastpage

468

Abstract

This paper presents a neurodynamic approach to model predictive control (MPC) of constrained piecewise linear systems. A novel procedure for estimating uncertain system parameters of piecewise linear systems is proposed. The model predictive control problem is then formulated as a quadratic optimization problem. To realize the real-time optimization in MPC, a one-layer recurrent neural network is employed for solving the quadratic optimization problem during each sampling interval. The overall MPC approach is of low computational complexity. Simulation results are included to substantiate the effectiveness and usefulness of the proposed approach.

Keywords

computational complexity; linear systems; neurocontrollers; parameter estimation; piecewise linear techniques; predictive control; quadratic programming; recurrent neural nets; sampling methods; uncertain systems; MPC approach; computational complexity; constrained piecewise linear systems; model predictive control; neurodynamic approach; one-layer recurrent neural network; quadratic optimization problem; sampling interval; uncertain system parameter estimation; Biological neural networks; Linear systems; Neurodynamics; Optimization; Predictive control; Recurrent neural networks; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4577-2144-1

Type

conf

DOI

10.1109/ICICIP.2012.6391405

Filename

6391405