Mathematical modelling and system identification of tilt-rotor tricopter
With multicopters finding more and more application in various fields, the study of different configurations to design a better controller is an active research topic among academics worldwide. This master thesis is one such attempt to study a three rotor tricopter with an objective to identify its...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/72489 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With multicopters finding more and more application in various fields, the study of different configurations to design a better controller is an active research topic among academics worldwide. This master thesis is one such attempt to study a three rotor tricopter with an objective to identify its model and estimate certain unknown physical parameters. A brief introduction to the system is covered along with the theory used and experiments performed throughout the course of this thesis. The process of system identification requires input/output data to determine the system. Identifying a model of the UAV is crucial in design of model based controllers and also estimation of the unknown physical quantities. The different algorithms used to accomplish this objective are also mentioned. Modelling and simulation are done in MATLAB and Simulink software. The aim of this thesis is to use a suitable identification method and implement on the tricopter system. The system identification can provide a more comprehensive model which is used to design advanced control systems. The modelling of the system is another major aspect presented here. A non-linear model of the tricopter will be presented in this thesis in greater depth. The significance of certain drag coefficients in the modelling of this tricopter UAV is studied here. Initially, the code is tested on the simulation data and the importance of inputs for identification process is discussed. Then identification is performed for real system, a bicopter ground setup. The unknown parameters of the model are estimated successfully such that the defined cost function of the system is minimized. The results for the real system are validated and found to be in fairly good agreement with the system response. |
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