A diagonal recurrent neural network-based hybrid direct adaptive SPSA control system

Author

Ji, Xiao D. ; Familoni, Babajide O.

Author_Institution

Dept. of Electr. Eng., Memphis Univ., TN, USA

Volume

44

Issue

7

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

1469

Lastpage

1473

Abstract

A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN controller. It results in a steady-state error and is sensitive to SPSA coefficients and termination condition. For trajectory control purpose, a hybrid control system scheme with a conventional PID controller is proposed

Keywords

adaptive control; approximation theory; neurocontrollers; nonlinear systems; recurrent neural nets; three-term control; PID controller; adaptive control; convergence; diagonal recurrent neural network; nonlinear systems; simultaneous perturbation stochastic approximation; termination condition; trajectory control; Adaptive control; Adaptive systems; Control systems; Feedforward neural networks; Fuzzy control; Neural networks; Programmable control; Recurrent neural networks; Size control; Stochastic systems;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.774125

Filename

774125