A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

Hdl Handle:
http://hdl.handle.net/10545/337911
Title:
A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks
Authors:
Poyi, Gwangtim Timothy
Abstract:
Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.
Advisors:
Wu, Mian Hong; Bousbaine, Amar ( 0000-0001-9288-0495 ) ; Hu, Huosheng ( 0000-0001-5797-1412 )
Publisher:
University of Derby
Issue Date:
1-Dec-2014
URI:
http://hdl.handle.net/10545/337911
Type:
Thesis or dissertation
Language:
en
Appears in Collections:
Faculty of Art Design & Technology

Full metadata record

DC FieldValue Language
dc.contributor.advisorWu, Mian Hongen
dc.contributor.advisorBousbaine, Amaren
dc.contributor.advisorHu, Huoshengen
dc.contributor.authorPoyi, Gwangtim Timothyen
dc.date.accessioned2015-01-08T08:38:35Zen
dc.date.available2015-01-08T08:38:35Zen
dc.date.issued2014-12-01en
dc.identifier.urihttp://hdl.handle.net/10545/337911en
dc.description.abstractQuad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.en
dc.language.isoenen
dc.publisherUniversity of Derbyen
dc.subjectQuad-rotor helicoptersen
dc.subjectUnmanned Aerial Vehiclesen
dc.subjectIntelligent Controlen
dc.subjectFuzzy-Neural Networksen
dc.subjectComputational Fluid Dynamics (CFD)en
dc.subjectArtificial neural networksen
dc.subjectFuzzy Logicen
dc.subjectQuad-rotor Dynamic Modellingen
dc.subjectMatlab/Simulinken
dc.subjectSolidWorksen
dc.subject3-D Quad-rotor Modelen
dc.subjectError Compensationen
dc.subjectWind Disturbanceen
dc.subjectFlight Control Simulationen
dc.titleA novel approach to the control of quad-rotor helicopters using fuzzy-neural networksen
dc.typeThesis or dissertationen
dc.publisher.departmentCollege of Engineering and Technologyen
dc.type.qualificationnamePhDen
dc.type.qualificationlevelDoctoralen
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