Simulation of two-rate adaptive neural network and fuzzy logic hybrid control for stochastic model of an experimental aircraft

Author

Astrov, Igor ; Pedai, Andrus ; Rüstern, Emu

Author_Institution

Dept. of Comput. Control, Tallinn Univ. of Technol., Estonia

Volume

1

fYear

2004

Firstpage

125

Abstract

The nature of the multirate dynamics of a process makes it very attractive for applications, since the multirate phenomena are complex. This paper presents a research methodology for describing a two-rate stochastic control system as state-space (SS) type decomposed models of multi-input/multi-output (MIMO) stochastic control subsystems with "fast" and "slow" adaptive neural networks (NNs), and with neuro-fuzzy networks (NFNs) and fuzzy logic (FL) control structures. The block diagrams for both the original system with a linear-quadratic-Gaussian (LQG) regulator and the decomposed subsystems with two-rate adaptive NNs and FL hybrid control for the stochastic model of a tracking system for an experimental aircraft were designed. The simulation results demonstrate that this research technique would work for real-time MIMO stochastic systems.

Keywords

MIMO systems; adaptive systems; aircraft control; fuzzy control; fuzzy neural nets; state-space methods; stochastic systems; MIMO stochastic control subsystems; aircraft tracking system; experimental aircraft stochastic model; fuzzy logic control; fuzzy logic hybrid control; linear-quadratic-Gaussian regulator; neuro-fuzzy networks; process multirate dynamics; state-space type decomposed models; stochastic control system; two-rate adaptive neural network; Adaptive control; Adaptive systems; Aerospace control; Aircraft; Fuzzy logic; MIMO; Neural networks; Programmable control; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2004. Canadian Conference on

ISSN

0840-7789

Print_ISBN

0-7803-8253-6

Type

conf

DOI

10.1109/CCECE.2004.1344972

Filename

1344972