Real-time Neural Backstepping Control for a Helicopter Prototype

被引：0

作者：

Djilali, Larbi ^{[1
,2
]}

Suarez, Oscar J. ^{[1
]}

Sanchez, Edgar N. ^{[1
]}

Alanis, Alma Y. ^{[3
]}

Pardo Garcia, Aldo ^{[4
]}

机构：

[1] CINVESTAV, Elect Engn Dept, Guadalajara, Mexico

[2] Univ Amar Telidji, Elect Engn Dept, Laghouat, Algeria

[3] Univ Guadalajara, Comp Sci Dept, Guadalajara, Mexico

[4] Univ Pamplona, Elect Engn Dept, Pamplona, Colombia

来源：

2017 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI) | 2017年

关键词：

Quanser 2-DOF Helicopter; Backstepping Control; Recurrent High Order Neural Network; Extended Filter Kalman; Real-time; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a discrete-time backstepping controller based on a neural model for a Quanser 2-Degree Of Freedom (DOF) helicopter. The proposed controller is used to track the pitch and yaw position references independently. This controller is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF). The RHONN works as an identifier to obtain an adequate Quanser 2-DOF helicopter mathematic model, which is robust in presence of disturbances and parameter variations. To examine the robustness of the proposed controller, simulations using Matlab/Simulinkand real-time implementation are presented.

引用

页数：6

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