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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.

Small fixed wing uncrewed air vehicles (UAVs) are often required to fly at low speeds and high angles of attack, particularly when operating in urban environments. This study focuses on the potential of combining two bio-inspired flight technologies to improve maneuverability under these conditions. The outstanding flight agility of birds is believed to be enabled by the capability to sense the airflow over their wings and morph their wing surfaces accordingly. To test the benefits of combining these abilities a wind tunnel model able to perform an avian-inspired wing sweep motion incorporating two arrays of pressure sensors was developed. Aerodynamic load results highlight strong changes to the pitching moment produced by the change in wing sweep angle. This suggests that wing sweep can be an alternative or complementary mechanism for pitch attitude control, improving control authority at high angles of attack. On the other hand, pressure sensing data shows the ability of these sensors to detect the fine details of the onset of aerodynamic stall. The combination of these two novel technologies is suggested as a potential method to improve UAV pitch control when flying at low speeds, when the aircraft is most susceptible to environmental disturbances.

The high incidence aerodynamics of a lightweight jet trainer aircraft model has been investigated using a novel five degree-of-freedom (DoF) dynamic manoeuvre rig, recently updated with improved actuation and data acquisition systems, in the 7' x 5' closed-section low-speed wind tunnel at the University of Bristol. The major focus was to identify the nonlinear and unsteady aerodynamic characteristics specific to the stall region and which affect free-to-move aircraft model behaviour. First, the unstable equilibrium states in the limit cycle regions were stabilized, and so observed, over a wide range of angles of attack using a simple elevator feedback control law based on pitch angle and pitch-rate sensor measurements.Tests with two degrees-of-freedom, namely the aircraft model and rig arm pitch angles, revealed the existence of static hysteresis in the normal force acting on the aircraft model in the stall region. Unlocking the aircraft model in roll and yaw accompanied by feedback stabilization of the lateral-directional modes of motion demonstrated onset of asymmetric aerodynamic rolling and yawing moments in this four degree-of-freedom configuration. This observation implicitly indicates a link between the static hysteresis in the normal aerodynamic force with an onset of aerodynamic asymmetry. The experimental results show the efficiency of the updated multi-degree-of-freedom actively controlled manoeuvre rig in providing insight into complicated aerodynamic effects within the stall region.

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.

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.

Small unmanned aerial vehicles (SUAVs) are suitable for many low-altitude operations in urban environments due to their manoeuvrability; however, their flight performance is limited by their on-board energy storage and their ability to cope with high levels of turbulence. Birds exploit the atmospheric boundary layer in urban environments, reducing their energetic flight costs by using orographic lift generated by buildings. This behaviour could be mimicked by fixed-wing SUAVs to overcome their energy limitations if flight control can be maintained in the increased turbulence present in these conditions. Here, the control effort required and energetic benefits for a SUAV flying parallel to buildings whilst using orographic lift was investigated. A flight dynamics and control model was developed for a powered SUAV and used to simulate flight control performance in different turbulent wind conditions. It was found that the control effort required decreased with increasing altitude and that the mean throttle required increased with greater radial distance to the buildings. However, the simulations showed that flying close to the buildings in strong wind speeds increased the risk of collision. Overall, the results suggested that a strategy of flying directly over the front corner of the buildings appears to minimise the control effort required for a given level of orographic lift, a strategy that mirrors the behaviour of gulls in high wind speeds.

This paper considers a novel multi-degree-of-freedom dynamic manoeuvre rig,with the aim of assessing its potential for capturing aircraft model nonlinear time dependent dynamics in the wind tunnel. The dynamic manoeuvre rig capabilities are demonstrated via a series of experiments involving a model aircraft in a closed section low-speed wind tunnel. A series of open loop experiments show that the aircraft model exhibits nonlinear time dependent dynamics. This nonlinear behaviour manifests itself as limit cycle oscillations that increase in complexity with the number of degrees-of-freedom in which the aircraft is allowed to move. Two real-time closed loop control experiments further illustrate the manoeuvre rig potential: first, using a pitch motion configuration, an experiment is conducted to investigate the limit cycle behaviour in more detail, allowing the stability properties of the pitch oscillations to be assessed; secondly, using a 5-DOF motion configuration, the test motion envelope is extended by using a compensating feedback control law to track the aircraft’s roll motion. Together, these experiments demonstrate the manoeuvre rig potential to reveal aircraft nonlinear and unsteady phenomena.

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.

Flying animals such as birds, bats and insects all have extensive arrays of sensory organs 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 distribution 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.

This paper presents recent progress in the development of a novel multi-degree-of-freedom dynamic manoeuvre rig aimed at investigation of aircraft model nonlinear and time dependent aerodynamics in the wind tunnel. The purpose and characteristics of the rig are first described, along with a description of the data acquisition, processing and presentation system. The dynamic manoeuvre rig capabilities are demonstrated via a series of experiments involving a wind tunnel model aircraft in a closed section low-speed wind tunnel. First, an experiment illustrating low-speed wind tunnel aerodynamic model identification is presented. Then, examples of experiments involving real-time control of the rig/aircraft model are shown; these are evaluated in terms of testing productivity with a focus on the development and design of aircraft control laws.

This paper discusses the development of a novel multi-degree-of-freedom dynamic manoeuvre rig aimed at investigation of aircraft upset/LOC-related behaviour in the wind tunnel. The motivation behind the development and characteristics of the rig are first described, along with example behaviour of an aircraft model exhibiting nonlinear time-dependent aerodynamics in an open-jet low-speed wind tunnel. Test objectives for assessment of upset onset scenarios - both for parameter estimation purposes and to 'physiccally simulate' the behaviour - are then described, as is the design of the upgraded instrumentation system to facilitate experimental investigation. Finally, examples of relevant behaviour involving real-time control of the rig to explore nonlinear conditions leading to upset are presented; these are evaluated in terms of prospects for such testing in aircraft development and analysis projects.