Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights

We address the theoretical and practical problems related to the trajectory generation and tracking control of tail-sitter UAVs. Theoretically, we focus on the differential flatness property with full exploitation of actual UAV aerodynamic models, which lays a foundation for generating dynamically feasible trajectory and achieving high-performance tracking control. We have found that a tail-sitter is differentially flat with accurate aerodynamic models within the entire flight envelope, by specifying coordinate flight condition and choosing the vehicle position as the flat output. This fundamental property allows us to fully exploit the high-fidelity aerodynamic models in the trajectory planning and tracking control to achieve accurate tail-sitter flights. Particularly, an optimization-based trajectory planner for tail-sitters is proposed to design high-quality, smooth trajectories with consideration of kinodynamic constraints, singularity-free constraints and actuator saturation. The planned trajectory of flat output is transformed to state trajectory in real-time with consideration of wind in environments. To track the state trajectory, a global, singularity-free, and minimally-parameterized on-manifold MPC is developed, which fully leverages the accurate aerodynamic model to achieve high-accuracy trajectory tracking within the whole flight envelope. The effectiveness of the proposed framework is demonstrated through extensive real-world experiments in both indoor and outdoor field tests, including agile SE(3) flight through consecutive narrow windows requiring specific attitude and with speed up to 10m/s, typical tail-sitter maneuvers (transition, level flight and loiter) with speed up to 20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical Eight and Cuban Eight) with acceleration up to 2.5g

Adaptive backstepping controller design of quadrotor biplane for payload delivery

Performance of the UAVs for a particular application can be enhanced by hybrid design, where take-off, hover, and landing happen like rotary-wing UAVs, and flies like fixed-wing UAVs. A backstepping controller and an adaptive backstepping controller are designed for trajectory tracking and payload delivery in a medical emergency or medical substance delivery like vaccine delivery in the presence of wind gust. Simulation results show that the backstepping controller effectively tracks the trajectory during the entire flight envelope, including take-off, hovering, the transition phase, level flight mode, and landing. A comparison between Backstepping, Integral Terminal Sliding Mode (ITSMC) and Adaptive Backstepping controllers for payload delivery show that the adaptive backstepping controller effectively tracks the altitude and attitude. ITSMC is capable of tracking the desired trajectory for a change in the mass but has sluggish response. The backstepping controller generates a steady-state error in altitude during the mass change in biplane-quadrotor.The publication of this article was funded by Qatar National Library.Scopu

Flight control of hybrid drones towards enabling parcel relay manoeuvres

This work addresses the modeling and controlling process of a hybrid UAV, aimed for parcel relay maneuvers. Hybrid UAVs bring big advantages with the capability of flying in two flight modes, rotary and fixed wing. But with them comes added complexity both in modeling and controlling. This work is based on a popular airframe, a tilt tri-rotor UAV, containing all the specific system dynamics such vehicle category provides. The model is then validated by designing two separate controllers for both flight modes, capable of trajectory tracking in eachmode,makinguseofacustomhybridcontrolallocationtechniquethatdifferentiates the control in three parts: vertical, horizontal, and transitional flight modes. Finally, a hybrid controller is proposed, using a finite state machine capable of handling logical events, with the aim to provide control logic to perform autonomous mid flight transitions. All the designs system are simulated using a mathematical framework and a power-full simulation tool.Este trabalho aborda o processo de modelação e controlo de um veículo aéreo não tripulado híbrido com o objetivo de proporcionar manobras de transição de carga. Drones híbridos trazem grandes vantagem com a sua capacidade de voar em dois modos de voo, de asa rotativa e asa fixa. Por outro lado, estas vantagens adicionam complexidade ao sistema dificultando o processo de modulação e controlo. Nestetrabalhoestápresenteummodelodeumdronetrirotortendodoisrotoresmovíveis. Este contém todas as dinâmicas especificas que um sistema deesta categoria de UAV obriga. O modelo é posteriormente validado com dois controladores separados em modo de voo, capazes de proporcionar medidas de seguimento de trajetória em cada modo, usando uma técnica de alocação de controlo personalizada que diferencia o controlo em três partes: vertical, horizontal e de transição. Por fim, é proposto um controlador híbrido contento uma máquina de estados capaz de tratar de eventos lógicos, de modo a proporcionar transições de modo de voo autónomas em pleno voo. Todos os sistemas propostos são devidamente simulados usando ferramentas matemáticas e também poderosos sistemas de simulação

Transition flight control system design for fixed-wing VTOL UAV: a reinforcement learning approach

Tilt-rotor vertical takeoff and landing aerial vehicles have been gaining popularity in urban air mobility applications because of their ability in performing both hover and forward flight regimes. This hybrid concept leads energy efficiency which is quite important to obtain a profitable and sustainable operation. However, inherent dynamical nonlinearities of the aerial platform requires adaptation capability of the control systems. In addition, transition flight phase should be planned carefully not only for a profitable operation but also for a safe transition between flight regimes in the urban airspace. In this paper, transition flight phase of a tilt-rotor vertical-takeoff-and-landing unmanned aerial vehicle (UAV) is studied. Low-level flight control systems are designed based on adaptive dynamic inversion methodology to compensate aerodynamic effects during the transition phase. Reinforcement learning method is utilized to provide safety and energy efficiency during the transition flight phase. An actor-critic agent is utilized and trained by using deep deterministic policy gradient algorithm to augment the collective channel of the UAV. This augmentation on the collective input is used to adjust flight path angle of the UAV which results in adjusting the angle of attack when pitch angle is zero. By using this relationship, it is proposed to generate aerodynamic lift force and perform transition flight with minimum altitude change and energy usage. Simulation results show that the agent reduces the collective signal level as the aerodynamic lift force is created in the descent flight phase. This affects overall system efficiency, reduces operational costs and makes the enterprise more profitable

CapsuleBot: A Novel Compact Hybrid Aerial-Ground Robot with Two Actuated-wheel-rotors

This paper presents the design, modeling, and experimental validation of CapsuleBot, a compact hybrid aerial-ground vehicle designed for long-term covert reconnaissance. CapsuleBot combines the manoeuvrability of bicopter in the air with the energy efficiency and noise reduction of ground vehicles on the ground. To accomplish this, a structure named actuated-wheel-rotor has been designed, utilizing a sole motor for both the unilateral rotor tilting in the bicopter configuration and the wheel movement in ground mode. CapsuleBot comes equipped with two of these structures, enabling it to attain hybrid aerial-ground propulsion with just four motors. Importantly, the decoupling of motion modes is achieved without the need for additional drivers, enhancing the versatility and robustness of the system. Furthermore, we have designed the full dynamics and control for aerial and ground locomotion based on the bicopter model and the two-wheeled self-balancing vehicle model. The performance of CapsuleBot has been validated through experiments. The results demonstrate that CapsuleBot produces 40.53% less noise in ground mode and consumes 99.35% less energy, highlighting its potential for long-term covert reconnaissance applications.Comment: 7 pages, 10 figures, submitted to 2024 IEEE International Conference on Robotics and Automation (ICRA). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl