A optimal control method with Hopfield neural networks

Author

Zhang, Shao-bai ; Cheng, Xie-feng

Author_Institution

Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China

Volume

3

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1349

Lastpage

1352

Abstract

An optimal control method based on continuous-time, continuous-state Hopfield neural network (CTCSHNN) is proposed for multivariable time-varying systems. The equivalence is built theoretically between moving-horizon linear quadratic (LQ) performance index and energy function of CTCSHNN, and the CTCSHNN is constructed to solve the above LQ optimization control problems. Moreover, the rolling optimization strategy is adopted to form closed-loop control structure that includes CTCSHNN. So, the dynamic optimization control for multivariable time-varying systems is realized in infinite-horizon. As an example, a second order time-varying system is simulated. Simulation results show the effectiveness of proposed method.

Keywords

Hopfield neural nets; closed loop systems; continuous time systems; dynamic programming; equivalence classes; infinite horizon; multivariable control systems; neurocontrollers; optimal control; time-varying systems; closed-loop control structure; continuous-time continuous-state Hopfield neural network; dynamic optimization control; energy function; equivalence; infinite horizon; moving-horizon linear quadratic performance index; multivariable time-varying systems; optimal control; rolling optimization strategy; Cost function; Hopfield neural networks; Neurons; Optimal control; Time varying systems; Hopfield neural network; infinite-horizon; multivariable time-varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5583580

Filename

5583580