804 research outputs found

    Simulation of a Machine Learning Based Controller for a Fixed-Wing UAV with Distributed Sensors

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    Recent research suggests that the information obtained from arrays of sensors distributed on the wing of a fixed-wing small unmanned aerial vehicle (UAV) can provide information not available to conventional sensor suites. These arrays of sensors are capable of sensing the flow around the aircraft and it has been indicated that they could be a potential tool to improve flight control and overall flight performance. However, more work needs to be carried out to fully exploit the potential of these sensors for flight control. This work presents a 3 degrees-of-freedom longitudinal flight dynamics and control simulation model of a small fixed-wing UAV. Experimental readings of an array of pressure and strain sensors distributed across the wing were integrated in the model. This study investigated the feasibility of using machine learning to control airspeed of the UAV using the readings from the sensing array, and looked into the sensor layout and its effect on the performance of the controller. It was found that an artificial neural network was able to learn to mimic a conventional airspeed controller using only distributed sensor signals, but showed better performance for controlling changes in airspeed for a constant altitude than holding airspeed during changes in altitude. The neural network could control airspeed using either pressure or strain sensor information, but having both improved robustness to increased levels of turbulence. Results showed that some strain sensors and many pressure sensors signals were not necessary to achieve good controller performance, but that the pressure sensors near the leading edge of the wing were required. Future work will focus on replacing other elements of the flight control system with machine learning elements and investigate the use of reinforcement learning in place of supervised learning.</p

    Bio-inspired Distributed Strain and Airflow Sensing for Small Unmanned Air Vehicle Flight Control

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    Flying animals such as birds, bats and insects all have extensive arrays of sensory or- gans distributed in their wings which provide them with detailed information about the airflow over their wings and the forces generated by this airflow. Using two small modified unmanned air vehicle platforms (UAVs), one with a distributed array of 12 strain gauge sensors and one with a chord-wise array of 4 pressure sensors, we have examined the dis- tribution of the strain and air pressure signals over the UAV wings in relation to flight conditions, including wind tunnel testing, indoor free flight and outdoor free flight. We have also characterised the signals provided by controlled gusts and natural turbulence. These sensors were then successfully used to control roll motions in the case of the strain sensor platform and pitch motions in the case of the pressure sensor platform. These results suggest that distributed mechanosensing and airflow sensing both offer advantages beyond traditional flight control based on rigid body state estimation using inertial sensing. These advantages include stall detection, gust alleviation and model-free measurement of aerodynamic forces. These advantages are likely to be important in the development of future aircraft with increasing numbers of degrees of freedom both through flexibility and active morphing.</p

    High-speed civil transport flight- and propulsion-control technological issues

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    Technology advances required in the flight and propulsion control system disciplines to develop a high speed civil transport (HSCT) are identified. The mission and requirements of the transport and major flight and propulsion control technology issues are discussed. Each issue is ranked and, for each issue, a plan for technology readiness is given. Certain features are unique and dominate control system design. These features include the high temperature environment, large flexible aircraft, control-configured empennage, minimizing control margins, and high availability and excellent maintainability. The failure to resolve most high-priority issues can prevent the transport from achieving its goals. The flow-time for hardware may require stimulus, since market forces may be insufficient to ensure timely production. Flight and propulsion control technology will contribute to takeoff gross weight reduction. Similar technology advances are necessary also to ensure flight safety for the transport. The certification basis of the HSCT must be negotiated between airplane manufacturers and government regulators. Efficient, quality design of the transport will require an integrated set of design tools that support the entire engineering design team

    Performance improvement of small Unmanned Aerial Vehicles through gust energy harvesting

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    Fixed-wing miniature aerial vehicles usually fly at low altitudes that are often exposed to turbulent environments. Gust soaring is a flight technique of energy harvesting in such a complex and stochastic domain. The presented work shows the feasibility and benefits of exploiting a nonstationary environment for a small unmanned aerial vehicle. A longitudinal dynamics trajectory is derived, showing significant benefits in extended flight with a sinusoidal wind profile. An optimization strategy for active control is performed, with the aim of obtaining the most effective set of gains for energy retrieval. Moreover, a three-dimensional multipoint model confirms the feasibility of energy harvesting in a more complex spatial wind field. The influence of unsteady aerodynamics is determined on the overall energy gain along the flight path with active proportional control. The aerodynamic derivatives describing the contribution to lift by a change in angle of attack and elevator deflection are identified as the most contributing aerodynamic parameters for energy harvesting in a gusty environment, and are therefore suggested as a basic objective function of an unmanned aerial vehicle design for such a flight strategy

    Distributed pressure sensing–based flight control for small fixed-wing unmanned aerial systems

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    Small fixed-wing unmanned aerial systems (UAS) may require increased agility when operating in turbulent wind fields. In these conditions, conventional sensor suites could be augmented with additional flow-sensing to extend the aircraft’s usable flight envelope. Inspired by distributed sensor arrays in biological systems, a UAS with a chordwise array of pressure sensors was developed. Wind-tunnel testing characterized these sensors alongside a conventional airspeed sensor and an angle-of-attack (AoA) vane, and showed a single pressure measurement gave a linear response to AoA prestall. Flight tests initially manually piloted the vehicle through pitching maneuvers, then in a series of automated maneuvers based on closed-loop feedback using an estimate of AoA from the single pressure port. The AoA estimate was successfully used to control the attitude of the aircraft. An artificial neural network (ANN) was trained to estimate the AoA and airspeed using all pressure ports in the array, and validated using flight-trial data. The ANN more accurately estimated the AoA over a single-port method with good robustness to stall and unsteady flow. Distributed flow sensors could be used to supplement conventional flight control systems, providing enhanced information about wing flow conditions with application to systems with highly flexible or morphing wings

    2004 Research Engineering Annual Report

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    Selected research and technology activities at Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts

    Reinforcement Learning to Control Lift Coefficient Using Distributed Sensors on a Wind Tunnel Model

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    Arrays of sensors distributed on the wing of fixed-wing vehicles can provide information not directly available to conventional sensor suites. These arrays of sensors have the potential to improve flight control and overall flight performance of small fixed-wing uninhabited aerial vehicles (UAVs). This work investigated the feasibility of estimating and controlling aerodynamic coefficients using the experimental readings of distributed pressure and strain sensors across a wing. The study was performed on a one degree-of-freedom model about pitch of a fixed-wing platform instrumented with the distributed sensing system. A series of reinforcement learning (RL) agents were trained in simulation for lift coefficient control, then validated in wind tunnel experiments. The performance of RL-based controllers with different sets of inputs in the observation space were compared with each other and with that of a manually tuned PID controller. Results showed that hybrid RL agents that used both distributed sensing data and conventional sensors performed best across the different tests.</p
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