Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System
The paper deals with the hierarchical modeling, simulation and model-based investigation of a nonlinear 1 DOF helicopter model, which is the most important building block of complex multi-rotor systems. The motivation behind the work is the increasing number of multi-rotor drones with various applic...
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2021-01-01
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Online Access: | https://hrcak.srce.hr/file/364675 |
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doaj-a5f6dc50256e453285daabfe7934bc7b2021-02-07T21:18:31ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek Tehnički Vjesnik1330-36511848-63392021-01-0128116Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor SystemZoltan Sari0Ildiko Jancskar1Adam Schiffer2Geza Varady*3Department of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, H-7624, Pécs, Boszorkány 2., HungaryDepartment of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, H-7624, Pécs, Boszorkány 2., HungaryDepartment of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, H-7624, Pécs, Boszorkány 2., HungaryDepartment of Information Technology, Faculty of Engineering and Information Technology, University of Pécs, H-7624, Pécs, Boszorkány 2., HungaryThe paper deals with the hierarchical modeling, simulation and model-based investigation of a nonlinear 1 DOF helicopter model, which is the most important building block of complex multi-rotor systems. The motivation behind the work is the increasing number of multi-rotor drones with various applications in diverse areas like smart cities and smart infrastructures, such as intelligent surveillance, autonomous (or semi-autonomous) flight, automatic object detection and recognition, carrying various payloads (sensors, packages) etc. In order to make these flying robots intelligent, a sophisticated control strategy is required, which is partly based on sensory information provided by the on-board sensors (gyroscope, accelerometer) - recently supported by visual data provided by on-board camera(s) - concerning the low-level feedback control loops. Most of the modern drones are built on an embedded computer, and the first layer of control is usually implemented in the firmware, thus the low level control commands are given per se. For the accomplishment of complex tasks (specific "behavior" of the drone) high level control strategies have to be implemented. In order to make these control loops efficient and robust, model based analysis of the controlled system had been carried out along with the real-life tests of the various control strategies. Since both the structure and the parameter set of the model system can be easily changed, model based study is a very efficient way of experimenting with novel control approaches.https://hrcak.srce.hr/file/364675fuzzy-controlmodel-based designsimulation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zoltan Sari Ildiko Jancskar Adam Schiffer Geza Varady* |
spellingShingle |
Zoltan Sari Ildiko Jancskar Adam Schiffer Geza Varady* Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System Tehnički Vjesnik fuzzy-control model-based design simulation |
author_facet |
Zoltan Sari Ildiko Jancskar Adam Schiffer Geza Varady* |
author_sort |
Zoltan Sari |
title |
Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System |
title_short |
Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System |
title_full |
Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System |
title_fullStr |
Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System |
title_full_unstemmed |
Modeling and Adaptive Hybrid Fuzzy Control of a Nonlinear Rotor System |
title_sort |
modeling and adaptive hybrid fuzzy control of a nonlinear rotor system |
publisher |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
series |
Tehnički Vjesnik |
issn |
1330-3651 1848-6339 |
publishDate |
2021-01-01 |
description |
The paper deals with the hierarchical modeling, simulation and model-based investigation of a nonlinear 1 DOF helicopter model, which is the most important building block of complex multi-rotor systems. The motivation behind the work is the increasing number of multi-rotor drones with various applications in diverse areas like smart cities and smart infrastructures, such as intelligent surveillance, autonomous (or semi-autonomous) flight, automatic object detection and recognition, carrying various payloads (sensors, packages) etc. In order to make these flying robots intelligent, a sophisticated control strategy is required, which is partly based on sensory information provided by the on-board sensors (gyroscope, accelerometer) - recently supported by visual data provided by on-board camera(s) - concerning the low-level feedback control loops. Most of the modern drones are built on an embedded computer, and the first layer of control is usually implemented in the firmware, thus the low level control commands are given per se. For the accomplishment of complex tasks (specific "behavior" of the drone) high level control strategies have to be implemented. In order to make these control loops efficient and robust, model based analysis of the controlled system had been carried out along with the real-life tests of the various control strategies. Since both the structure and the parameter set of the model system can be easily changed, model based study is a very efficient way of experimenting with novel control approaches. |
topic |
fuzzy-control model-based design simulation |
url |
https://hrcak.srce.hr/file/364675 |
work_keys_str_mv |
AT zoltansari modelingandadaptivehybridfuzzycontrolofanonlinearrotorsystem AT ildikojancskar modelingandadaptivehybridfuzzycontrolofanonlinearrotorsystem AT adamschiffer modelingandadaptivehybridfuzzycontrolofanonlinearrotorsystem AT gezavarady modelingandadaptivehybridfuzzycontrolofanonlinearrotorsystem |
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1724280553247604736 |