Control of fixed-wing unmanned aerial vehicles in icing conditions

Background

Before unmanned aerial vehicles can operate completely autonomously in non-segregated airspace, there are many challenges that needs to be tackled related to reducing the operational risk. A major risk is icing, which significantly alters the dynamics of the vehicle, by increasing the drag and the mass while reducing lift and propulsion. The operational margins are significantly reduced, and a crash is imminent if no actions are taken.

One possible action is to actively remove the ice, by e.g. applying heat, but this takes some time and use a lot of power. It is therefore interesting to investigate another approach; can we reduce the effect of icing by changing the control loops of the UAV? In other words; can we formulate controllers that are robust to the changes in dynamics introduced by the icing, or that can adapt to this new dynamics, or that can predict the effects of icing from its model?

Adaptive, robust and model-predictive methods have already been applied to aircraft control, as illustrated by the below video showing how an adaptive controller is able to recover the UAV after 80% of one wing is ejected.

Scope

This project seek to investigate control methods that can be applied to control of fixed-wing UAVs in icing conditions, where the goal is to push the limits of what icing conditions it is possible to operate in. Previous students have worked on robust- and adaptive control, so one option is to extend this work and perform field trials. Another option is to use model predictive control.

Proposed tasks

• Familiarize with an applicable control strategy, such as Model-reference adaptive control (MRAC) (Chowdhary et al. 2013), H_∞ control (Lavretsky and Wise 2013) or model predictive control (MPC).
• Implement a robust/adaptive controller, and benchmark its performance against existing controllers under a variety of icing conditions, in a simulator.
• Conclude the work in a written report

Possible extensions for the master thesis include

• implementation on a physical UAV and experimental validation of the method
• improve the accuracy and applicability of the fault detection by improving the underlying model, based on existing data that has been recorded in an icing wind tunnel.

Prerequisites

This is a list of recommended prerequisites, more to signal what it will involve than to be used as a filter on candidates.

• Strong theoretical background and interest in statistics for estimation/sensor fusion, through e.g. TTK4250 Sensor Fusion (which also could be taken in parallel with the project, instead of the two 3.75 credit specialization courses).
• Basic familiarity with models of electro-mechanical systems, from e.g. TTK4130 Modelling and Simulation
• Ideally knowledge and interest in fluid/aerodynamics, through e.g. TEP4100 Fluid Mechanics or TEP4160 Aerodynamics

Contact

Contact supervisors Kristoffer Gryte, Richard Hann and Tor Arne Johansen

References

Chowdhary, Girish, Eric N. Johnson, Rajeev Chandramohan, M. Scott Kimbrell, and Anthony Calise. 2013. “Guidance and Control of Airplanes Under Actuator Failures and Severe Structural Damage.” Journal of Guidance, Control, and Dynamics 36 (4): 1093–1104. https://doi.org/10.2514/1.58028.

Lavretsky, Eugene, and Kevin A Wise. 2013. Robust and Adaptive Control: With Aerospace Applications. London: Springer London. https://doi.org/10.1007/978-1-4471-4396-3_11.