Optimal neurocontrollers for discretized distributed parameter systems

Author

Prokhorov, Danil V.

Author_Institution

Res. & Adv. Eng., Ford Motor Co., Dearborn, MI, USA

Volume

1

fYear

2003

fDate

4-6 June 2003

Firstpage

549

Abstract

We propose to use the framework of backpropagation through time (BPTT) to create optimal feedback neurocontrollers for distributed parameter systems (DPS). DPS are systems distributed in space while evolving in time. Unlike the lumped parameter systems, DPS are represented by a set of partial differential equations in the state space. Our neurocontrollers obtained for discretized DPS in the infinite-horizon regulator setting are applicable to a broad set of initial states (an envelope of initial state profiles). We compare our technique and results with another approach to synthesizing optimal DPS neurocontrollers introduced.

Keywords

backpropagation; control system synthesis; distributed parameter systems; neurocontrollers; optimal control; partial differential equations; DPS; backpropagation; controllers synthesis; distributed parameter systems; infinite horizon regulator; initial state profiles; lumped parameter systems; optimal control; optimal neurocontrollers; partial differential equations; Backpropagation; Computer networks; Distributed parameter systems; Neural networks; Neurocontrollers; Neurofeedback; Partial differential equations; Regulators; State feedback; State-space methods;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2003. Proceedings of the 2003

ISSN

0743-1619

Print_ISBN

0-7803-7896-2

Type

conf

DOI

10.1109/ACC.2003.1239074

Filename

1239074