QFT controller for nonlinear system application to 3-DOF flight control module
In this paper we proposed a new control method based on Quantitative Feedback Theory (QFT) to design practical controller methodology for uncertain characterized with three degree of freedom flight control module. Again linearly least phase systems must sacrifice to desirable feedback control benef...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/51105/ http://irep.iium.edu.my/51105/ http://irep.iium.edu.my/51105/1/ARPN2016jeas_0316_3939.pdf |
| Summary: | In this paper we proposed a new control method based on Quantitative Feedback Theory (QFT) to design practical
controller methodology for uncertain characterized with three degree of freedom flight control module. Again linearly least phase systems must sacrifice to desirable feedback control benefits to avoid an excessive uncertain disturbance. While preserving the robust stability Quantitative Feedback Theory (QFT) controller is proposed to control highly uncertain plants. A 3-DOF flight control system is intrinsically nonlinear, unstable and totally uncertain because of the nature of three individual angles known as pitch, elevation and travel. Most controllers which are designed for 3-DOF helicopter flight systems are base on a minimal linearized model where system variants and uncertainties are not accommodated. Again, the controllers are mostly designed to gratify the gains and phase margin specifications that may not guarantee to
handle the sensitivity. In proposed controller QFT may explicitly deal with uncertainty, where large plant parameter uncertainties with lower bandwidth can be achieved by QFT controller. Pre-filter technique may improve both robust stability and robust tracking performance within a desired precision of the individual uncertain parameters of 3-DOF module. This controller may handle large parameter uncertainties and disturbance with rugged stability. The random optimization technique is engaged in the design to optimize the overall performance of the controller. Simulation results and equations are used to show effective result of the proposed control methodology. |
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