Artificial Neural Network-Based Flight Control Using Distributed Sensors on Fixed-Wing Unmanned Aerial Vehicles

Conventional control systems for autonomous aircraft use a small number of precise sensors in combination with classical control laws to maintain flight. The sensing systems encode center of mass motion and generally are set-up for flight regimes where rigid body assumptions and linear flight dynamics models are valid. Gain scheduling is used to overcome some of the limitations from these assumptions, taking advantage of well-tuned controllers over a range of design points. In contrast, flying animals achieve efficient and robust flight control by taking advantage of highly non-linear structural dynamics and aerodynamics. It has been suggested that the distributed arrays of flow and force sensors found in flying animals could be behind their remarkable flight control. Using a wind tunnel aircraft model instrumented with distributed arrays of load and flow sensors, we developed Artificial Neural Network flight control algorithms that use signals from the sensing array as well as the signals available in conventional sensing suites to control angle-of-attack. These controllers were trained to match the response from a conventional controller, achieving a level of performance similar to the conventional controller over a wide range of angle-of-attack and wind speed values. Wind tunnel testing showed that by using an ANN-based controller in combination with signals from a distributed array of pressure and strain sensors on a wing, it was possible to control angle-of-attack. The End-to-End learning approach used here was able to control angle-of-attack by directly learning the mapping between control inputs and system outputs without explicitly estimating or being given the angle-of-attack.</p

Aerodynamic State and Loads Estimation Using Bio-Inspired Distributed Sensing

Flying animals exploit highly nonlinear dynamics to achieve efficient and robust flight control. It appears that the distributed flow and force sensor arrays found in flying animals are instrumental in enabling this performance. Using a wind-tunnel wing model instrumented with distributed arrays of strain and pressure sensors, we characterized the relationship between the distributed sensor signals and aerodynamic and load-related variables. Estimation approaches based on nonlinear artificial neural networks (ANNs) and linear partial least squares were tested with different combinations of sensor signals. The ANN estimators were accurate and robust, giving good estimates for all variables, even in the stall region when the distributed array pressure and strain signals became unsteady. The linear estimator performed well for load estimates but was less accurate for aerodynamic variables such as angle of attack and airspeed. Future applications based on distributed sensing could include enhanced flight control systems that directly use measurements of aerodynamic states and loads, allowing for increase maneuverability and improved control of unmanned aerial vehicles with high degrees of freedom such as highly flexible or morphing wings.</p

Quantifying avian inertial properties using calibrated computed tomography

Estimating centre of mass and mass moments of inertia is an important aspect of many studies in biomechanics. Characterising these parameters accurately in three dimensions is challenging with traditional methods requiring dissection or suspension of cadavers. Here, we present a method to quantify the three-dimensional centre of mass and inertia tensor of birds of prey using calibrated computed tomography (CT) scans. The technique was validated using several independent methods, providing body segment mass estimates within approximately 1% of physical dissection measurements and moment of inertia measurements with a 0.993 R(2) correlation with conventional trifilar pendulum measurements. Calibrated CT offers a relatively straightforward, non-destructive approach that yields highly detailed mass distribution data that can be used for three-dimensional dynamics modelling in biomechanics. Although demonstrated here with birds, this approach should work equally well with any animal or appendage capable of being CT scanned

Fine-scale flight strategies of gulls in urban airflows indicate risk and reward in city living

Birds modulate their flight paths in relation to regional and global airflows in order to reduce their travel costs. Birds should also respond to fine-scale airflows, although the incidence and value of this remains largely unknown. We resolved the 3-dimensional trajectories of gulls flying along a built up coastline, and used computation fluid dynamic models to examine how gulls reacted to airflows around buildings. Birds systematically altered their flight trajectories with wind conditions to exploit updraughts over features as small as a row of low-rise buildings. This provides the first evidence that human activities can change patterns of space-use in flying birds by altering the profitability of the airscape. At finer scales still, gulls varied their position to select a narrow range of updraught values, rather than exploiting the strongest updraughts available, and their precise positions were consistent with a strategy to increase their velocity control in gusty conditions. Ultimately, strategies such as these could help unmanned aerial vehicles negotiate complex airflows. Overall, airflows around fine-scale features have profound implications for flight control and energy use, and consideration of this could lead to a paradigm-shift in the way ecologists view the urban environment

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

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

A method for continuous study of soaring and windhovering birds

Avian flight continues to inspire aircraft designers. Reducing the scale of autonomous aircraft to that of birds and large insects has resulted in new control challenges when attempting to hold steady flight in turbulent atmospheric wind. Some birds, however, are capable of remarkably stable hovering flight in the same conditions. This work describes the development of a wind tunnel configuration that facilitates the study of flapless windhovering (hanging) and soaring bird flight in wind conditions replicating those in nature. Updrafts were generated by flow over replica “hills” and turbulence was introduced through upstream grids, which had already been developed to replicate atmospheric turbulence in prior studies. Successful flight tests with windhovering nankeen kestrels (Falco cenchroides) were conducted, verifying that the facility can support soaring and wind hovering bird flight. The wind tunnel allows the flow characteristics to be carefully controlled and measured, providing great advantages over outdoor flight tests. Also, existing wind tunnels may be readily configured using this method, providing a simpler alternative to the development of dedicated bird flight wind tunnels such as tilting wind tunnels, and the large test section allows for the replication of orographic soaring. This methodology holds promise for future testing investigating the flight behaviour and control responses employed by soaring and windhovering birds

Raptor wing morphing with flight speed

In gliding flight, birds morph their wings and tails to control their flight trajectory and speed. Using high-resolution videogrammetry, we reconstructed accurate and detailed three-dimensional geometries of gliding flights for three raptors (barn owl, Tyto alba; tawny owl, Strix aluco, and goshawk, Accipiter gentilis). Wing shapes were highly repeatable and shoulder actuation was a key component of reconfiguring the overall planform and controlling angle of attack. The three birds shared common spanwise patterns of wing twist, an inverse relationship between twist and peak camber, and held their wings depressed below their shoulder in an anhedral configuration. With increased speed, all three birds tended to reduce camber throughout the wing, and their wings bent in a saddle-shape pattern. A number of morphing features suggest that the coordinated movements of the wing and tail support efficient flight, and that the tail may act to modulate wing camber through indirect aeroelastic control