Nonlinear Autopilot Design for Extended Flight Envelopes and Operation of Fixed-Wing UAVs in Extreme Conditions

Prosjektet har utviklet autopilotalgoritmer for vingeformede ubemannede fly (droner). Autopilotsystemene har utviklet seg signifikant over tid, fra tidlige systemer som så vidt kunne holde en konstant orientering til moderne systemer for automatisk landing og komplekse manøvrer. Imidlertid, konvensjonelle autopiloter opererer innenfor et lite arbeidsområde og den dynamiske oppførselen av farkosten er begrenset slik at lineære designmetoder kan brukes. Dette legger unødvendige begrensinger på dronens operasjonelle kapasitet så vell som krav til hvilken vind det er trygt å fly i. Droner kan også opereres i autonome modi, hvilket krever at autopiloten er feiltolerang, rekonfigurerbar og intelligent for å takle uforutsette hendelser. Som en konsekvens av dette har vi utviklet autopilotalgoritmer hvor hovedmålet var å øke dronens arbeidsområde slik at dronen kan utføre mer avanserte manøvrer samt operere i ekstremt vær. Denne funksjonaliteten er viktig for mange kommersielle anvendelser. I løpet av de første årene av prosjektet har flere nye resultater blitt publisert. Dette inkluderer en ikke-lineær modellpredikitv regulator for vingeformede ubemannede fly. Deep reinforcement learning har blitt brukt til å lage algoritmer for attituderegulering ved hjelp av optimaliseringsmetoder. En komplett aerodynamisk model har blitt utviklet og testet for Skywalker X8 UAV. De aerodynamiske koeffisientene i modellen har blitt kalibrert ved hjelp av vindtunneltester. Modellen kan brukes av forskere til å simulerer aggressive manøvre og teste ikke-lineære styresystemer.

The overall objective of the project was to address fundamental research questions related to the nonlinear control of unmanned aerial vehicles (UAVs). More than 10 master's students have graduated so far and the two doctoral candidates will graduate in 2021. The project has demonstrated that: - Nonlinear autopilots can increase the flight envelopes of UAVs significantly compared to conventional linear designs. - Nonlinear autopilots can operate in extreme wind conditions and thus increase the number of days UAVs can be operated safely. - Nonlinear designs can recover control after severe failures and unforeseen events. The main results have been published at international conferences and in journals.

The main motivation for the proposal is summarized below: - The main goal is to increase the flight envelop of fixed-wing UAVs such that they can perform more advanced maneuvers and operate in harsh weather conditions. This functionality is needed in many commercial applications. - Mathematically prove global asymptotic/exponential stability of the closed-loop system using nonlinear control theory. - Achieve fault tolerance and robustness of the nonlinear autopilot system. - Experimentally verify and demonstrate the nonlinear autopilot algorithms and their real-time implementation in challenging applications and case studies at NTNU's UAV Lab in cooperation with NTNU AMOS (NTNU Center of Excellence). Demonstrate the following UAV operations and maneuvers: 1. Agile flight: Track a path with large curvature (course- and climb-rates) using nonlinear control. 2. Operation in turbulent wind conditions: In strongly turbulent winds, the autopilot must be designed to handle large roll and pitch angles that could result from these disturbances also when not attempting any high-curvature maneuvers. 3. Autonomous precision landing using large angles of attack (AOA): We intend to land an aircraft autonomously at a given landing target using deep stall to reduce the impact speed. 4. Stabilization of camera: The goal is to operate a small UAV with camera without using a gimbal to stabilize the camera. The aircraft autopilot should stabilize the camera to avoid large bank angles during turning. The project targets education of two PhD and eight MSc candidates as well as training of one postdoctoral researcher through Working Packages 1-3 dealing with: WP1: Nonlinear control allocation and attitude control WP2: Nonlinear speed and path-following control WP3: System integration, testing and case studies The results will be published at conferences and in the top ranked journals in the field.