Fine-Tuning of Feedback Gain Control for Hover Quad Copter Rotors by Stochastic Optimization Methods
| Autor: | Gurkan Kavuran, Baris Baykant Alagoz, Celaleddin Yeroglu, Abdullah Ates |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
020301 aerospace & aeronautics
0209 industrial biotechnology Fine-tuning Computer Networks and Communications Computer science Energy Engineering and Power Technology 02 engineering and technology Linear-quadratic regulator 020901 industrial engineering & automation 0203 mechanical engineering Control theory Test platform Signal Processing Automatic gain control Stochastic optimization Computer Vision and Pattern Recognition Electrical and Electronic Engineering Macro Divergence (statistics) Scale down |
| Zdroj: | Iranian Journal of Science and Technology, Transactions of Electrical Engineering. 44:1663-1672 |
| ISSN: | 2364-1827 2228-6179 |
| DOI: | 10.1007/s40998-020-00323-7 |
| Popis: | Three degree of freedom (3 DOF) Hover Quad Copter (HQC) platforms are implemented for various missions in diverse scales from the micro to macro platforms. As HQC platforms scale down, micro platform requires rather robust and effective control techniques. This study investigates applicability of some stochastic optimization methods for tuning feedback gain control of HQC rotors and compares optimization results with results of linear quadratic regulator (LQR) method that has been widely used analytical method for optimal feedback gain control of HQCs. This study considers the utilization of two stochastic methods for tuning of HQCs. These methods are stochastic multi-parameter divergence optimization method (SMDO) and discrete stochastic optimization method (DSO). These methods are employed to optimize feedback gain coefficients of an experimental HQC test platform. Simulation and experimental results of SMDO and DSO methods are reported and compared with results of LQR method. |
| Databáze: | OpenAIRE |
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