Nonlinear Controller for a UAV using Echo State Network

被引：0

作者：

Pugach, Bogdan ^{[1
]}

Beallo, Brian ^{[1
]}

Bement, Dave ^{[1
]}

McGough, Sean ^{[1
]}

Miller, Noah ^{[2
]}

Morgan, Justin ^{[1
]}

Rodriguez, Luie ^{[2
]}

Winterer, Kyle ^{[2
]}

Sherman, Tristan ^{[2
]}

Bhandari, Subodh ^{[2
]}

Aliyazicioglu, Zekeriya ^{[1
]}

机构：

[1] Cal Poly Pomona, Dept Elect & Comp Engn, Pomona, CA 91768 USA

[2] Cal Poly Pomona, Dept Aerosp Engn, Pomona, CA USA

来源：

2017 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS'17) | 2017年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A nonlinear adaptive controller for an unmanned aerial vehicle (UAV) has been developed using Echo State Network (ESN), which is a form of three-layered recurrent neural network (RNN). Online learning is used to train the ESN in real-time starting from randomized weights. The ESN is integrated into ArduPilot, an open source autopilot, for complex flight simulations. Software-in-the-loop and hardwarein-the-loop simulations are performed using the FlightGear Flight Simulator. The response of the UAV using the controller based on the ESN has surpassed the performance of the traditional controllers. Noise and external disturbances are added to show the effectiveness of the controllers. A UAV test platform is designed and built to gather aircraft flight data and test the ESN.

引用

页码：124 / 132

页数：9

共 50 条

[1] Controller Design Based on Echo State Network with Delay Output for Nonlinear System
Yao, Xianshuang
Fan, Siyuan
Zhao, Bo
Cao, Shengxian
[J]. COMPLEXITY, 2020, 2020
[2] A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network
Peng, Haoran
Chen, Chao
Lai, Chuan-Chi
Wang, Li-Chun
Han, Zhu
[J]. 2019 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2019,
[3] Wind Speed Forecasting Using an Echo State Network with Nonlinear Output Functions
Chitsazan, Mohammad Amin
Fadali, M. Sami
Nelson, Amanda K.
Trzynadlowski, Andrzej M.
[J]. 2017 AMERICAN CONTROL CONFERENCE (ACC), 2017, : 5306 - 5311
[4] Practical Nonlinear Model Predictive Control Using an Echo State Network Model
Schwedersky, Bernardo Barancelli
Costa Flesch, Rodolfo Cesar
Sirino Dangui, Hiago Antonio
Iervolino, Lucas Arrigoni
[J]. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
[5] A COMPLEX ECHO STATE NETWORK FOR NONLINEAR ADAPTIVE FILTERING
Xia, Yili
Mandic, Danilo P.
Van Hulle, Marc M.
Principe, Jose C.
[J]. 2008 IEEE WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2008, : 404 - +
[6] Echo state network models for nonlinear Granger causality
Duggento, Andrea
Guerrisi, Maria
Toschi, Nicola
[J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2021, 379 (2212):
[7] Wind speed and wind direction forecasting using echo state network with nonlinear functions
Chitsazan, Mohammad Amin
Fadali, M. Sami
Trzynadlowski, Andrzej M.
[J]. RENEWABLE ENERGY, 2019, 131 : 879 - 889
[8] Nonlinear Internal Model Control Using Echo State Network for Pneumatic Muscle System
Wu, Jun
Wang, Yongji
Huang, Jian
Zhou, Hanying
[J]. JOURNAL OF COMPUTERS, 2012, 7 (12) : 3060 - 3067
[9] A nonlinear digital robust controller for UAV
Sobhani, R.
[J]. 2007 IEEE AEROSPACE CONFERENCE, VOLS 1-9, 2007, : 3240 - 3245
[10] An echo state network with interacting reservoirs for modeling and analysis of nonlinear systems
Jianming Liu
Xu Xu
Eric Li
[J]. Nonlinear Dynamics, 2024, 112 : 8341 - 8361

← 1 2 3 4 5 →