Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV
Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/6/563 |
| _version_ | 1797596648690417664 |
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| author | Ahsan Tanveer Sarvat Mushtaq Ahmad |
| author_facet | Ahsan Tanveer Sarvat Mushtaq Ahmad |
| author_sort | Ahsan Tanveer |
| collection | DOAJ |
| description | Pitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller. |
| first_indexed | 2024-03-11T02:53:09Z |
| format | Article |
| id | doaj.art-bda6f6ec8f7741b09f25b5958f51ed36 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T02:53:09Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-bda6f6ec8f7741b09f25b5958f51ed362023-11-18T08:50:21ZengMDPI AGAerospace2226-43102023-06-0110656310.3390/aerospace10060563Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAVAhsan Tanveer0Sarvat Mushtaq Ahmad1Department of Mechanical & Aerospace Engineering, Institute of Avionics & Aeronautics, Air University, Islamabad 44000, PakistanControl & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaPitch control of an unmanned aerial vehicle (UAV) using fluidic thrust vectoring (FTV) is a relatively novel technique requiring no moving control surfaces, such as elevators. In this paper, the authors’ previous work on the characterization of a static co-flow FTV rig is further validated using the free to pitch dynamic test bench. The deflection of a primary jet after injection of a high-velocity secondary jet was captured using the tuft flow visualization technique, along with the experimental recording of subsequent normal force impinged on the Coanda surface resulting in the pitching moment. The effect of primary and secondary flow velocities on exhaust jet deflection, as well as on the pitch angle of the aircraft, is examined. Aerodynamic gain as well as the inertia of a delta wing UAV test bench are computed through experiments and fed into the equation of motion (e.o.m). The e.o.m developed aided in the design of a model-based PID controller for pitch motion control using the multi-parameter root locus technique. The root locus tuned controller serves as a benchmark controller for performance evaluation of the genetic algorithm (GA) and particle swarm optimization (PSO) tuned controllers. Furthermore, the frequency domain metric of gain and phase margins were also employed to reach a robust control design. Experiments conducted in a simulation environment reveal that PSO-PID results in a better response of the UAV in comparison to the baseline pitch controller.https://www.mdpi.com/2226-4310/10/6/563unmanned aerial vehicledynamic modelfluidic thrust vectoring controlgenetic algorithm optimizationPID controllerparticle swarm optimization |
| spellingShingle | Ahsan Tanveer Sarvat Mushtaq Ahmad Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV Aerospace unmanned aerial vehicle dynamic model fluidic thrust vectoring control genetic algorithm optimization PID controller particle swarm optimization |
| title | Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV |
| title_full | Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV |
| title_fullStr | Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV |
| title_full_unstemmed | Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV |
| title_short | Mathematical Modelling and Fluidic Thrust Vectoring Control of a Delta Wing UAV |
| title_sort | mathematical modelling and fluidic thrust vectoring control of a delta wing uav |
| topic | unmanned aerial vehicle dynamic model fluidic thrust vectoring control genetic algorithm optimization PID controller particle swarm optimization |
| url | https://www.mdpi.com/2226-4310/10/6/563 |
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