Abstract:Testing of untethered subscale models, often referred to as subscale flight testing, has traditionally had a relatively minor, yet relevant use in aeronautical research and development. As recent advances in electronics, rapid prototyping and unmanned-vehicle technologies expand its capabilities and lower its cost, this experimental method is seeing growing interest across academia and the industry. However, subscale models cannot meet all similarity conditions required for simulating full-scale flight. This l… Show more
“…3 it is possible to see the layers that composethe Neuro-Fuzzy architecture. REVISTA OBSERVATORIO DE LA ECONOMIA LATINOAMERICANA Curitiba, v.22, n.1, p. 1898-1919. 2024.…”
This study investigates different architectures of Neuro-Fuzzy applied to unsteady aerodynamic modeling based on experimental data from a reduced-scale aircraft, known as Generic Future Fighter. The comparison is made considering different fuzzy inference methods, membership function shapes, number of membership functions to describe the input variables and different output functions, in the case of Takagi-Sugeno inference method. All these comparisons are made using the differential evolution as an optimization tool. In the end, the results present the best Neuro-Fuzzy configuration applied to the system identification of the GFF. Furthermore, the conclusion presents insights about the possible future implementation of the methodology.
“…3 it is possible to see the layers that composethe Neuro-Fuzzy architecture. REVISTA OBSERVATORIO DE LA ECONOMIA LATINOAMERICANA Curitiba, v.22, n.1, p. 1898-1919. 2024.…”
This study investigates different architectures of Neuro-Fuzzy applied to unsteady aerodynamic modeling based on experimental data from a reduced-scale aircraft, known as Generic Future Fighter. The comparison is made considering different fuzzy inference methods, membership function shapes, number of membership functions to describe the input variables and different output functions, in the case of Takagi-Sugeno inference method. All these comparisons are made using the differential evolution as an optimization tool. In the end, the results present the best Neuro-Fuzzy configuration applied to the system identification of the GFF. Furthermore, the conclusion presents insights about the possible future implementation of the methodology.
“…However, in the case of UAV development, developers, especially researchers, often use existing COTS airframes that have limited parameters available. A review by Sobron et al of research platforms between 2010 and 2020 showed that at least 18 of the aircraft surveyed were developed from COTS airframes [81]. Beyond the scope of the aforementioned review, commercial (closed-design) airframes such as the UAV Factory Penguin airframe have been used as a basis by over 20 research institutions since 2010 with similarly limited parameters available [82].…”
Section: Measurementmentioning
confidence: 99%
“…There have also been studies of UAV performance through stall or upset maneuvers [11][12][13][14]. In addition, new aircraft configurations [15][16][17][18] and flight control hardware and software [19][20][21][22][23] have been tested. Research evaluating aircraft power consumption reduction through steady and dynamic soaring has also become the subject of significant attention recently [24][25][26][27][28].…”
In this work, a cyber-physical prototyping and testing framework to enable the rapid development of UAVs is conceived and demonstrated. The UAV Development Framework is an extension of the typical iterative engineering design and development process, specifically applied to the rapid development of UAVs. Unlike other development frameworks in the literature, the presented framework allows for iteration throughout the entire development process from design to construction, using a mixture of simulated and real-life testing as well as cross-aircraft development. The framework presented includes low- and high-order methods and tools that can be applied to a broad range of fixed-wing UAVs and can either be combined and executed simultaneously or be executed sequentially. As part of this work, seven novel and enhanced methods and tools were developed that apply to fixed-wing UAVs in the areas of: flight testing, measurement, modeling and emulation, and optimization. A demonstration of the framework to quickly develop an unmanned aircraft for agricultural field surveillance is presented.
“…Recent UAV flight test campaigns include [89][90][91][92][93][94][95][96][97][98][99], which also include flight testing with incremental type control laws in [100][101][102][103][104]. Subscale flight testing and system identification was performed in [105][106][107][108][109]. In [110], both system identification and control law design were performed for the longitudinal motion of a fixed wing UAV.…”
An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.
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