5 research outputs found

    Flight controller synthesis via deep reinforcement learning

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    Traditional control methods are inadequate in many deployment settings involving autonomous control of Cyber-Physical Systems (CPS). In such settings, CPS controllers must operate and respond to unpredictable interactions, conditions, or failure modes. Dealing with such unpredictability requires the use of executive and cognitive control functions that allow for planning and reasoning. Motivated by the sport of drone racing, this dissertation addresses these concerns for state-of-the-art flight control by investigating the use of deep artificial neural networks to bring essential elements of higher-level cognition to bear on the design, implementation, deployment, and evaluation of low level (attitude) flight controllers. First, this thesis presents a feasibility analyses and results which confirm that neural networks, trained via reinforcement learning, are more accurate than traditional control methods used by commercial uncrewed aerial vehicles (UAVs) for attitude control. Second, armed with these results, this thesis reports on the development and release of an open source, full solution stack for building neuro-flight controllers. This stack consists of a tuning framework for implementing training environments (GymFC) and firmware for the world’s first neural network supported flight controller (Neuroflight). GymFC’s novel approach fuses together the digital twinning paradigm with flight control training to provide seamless transfer to hardware. Third, to transfer models synthesized by GymFC to hardware, this thesis reports on the toolchain that has been released for compiling neural networks into Neuroflight, which can be flashed to off-the-shelf microcontrollers. This toolchain includes detailed procedures for constructing a multicopter digital twin to allow the research and development community to synthesize flight controllers unique to their own aircraft. Finally, this thesis examines alternative reward system functions as well as changes to the software environment to bridge the gap between simulation and real world deployment environments. The design, evaluation, and experimental work summarized in this thesis demonstrates that deep reinforcement learning is able to be leveraged for the design and implementation of neural network controllers capable not only of maintaining stable flight, but also precision aerobatic maneuvers in real world settings. As such, this work provides a foundation for developing the next generation of flight control systems

    Intelligent Control for Fixed-Wing eVTOL Aircraft

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    Urban Air Mobility (UAM) holds promise for personal air transportation by deploying "flying cars" over cities. As such, fixed-wing electric vertical take-off and landing (eVTOL) aircraft has gained popularity as they can swiftly traverse cluttered areas, while also efficiently covering longer distances. These modes of operation call for an enhanced level of precision, safety, and intelligence for flight control. The hybrid nature of these aircraft poses a unique challenge that stems from complex aerodynamic interactions between wings, rotors, and the environment. Thus accurate estimation of external forces is indispensable for a high performance flight. However, traditional methods that stitch together different control schemes often fall short during hybrid flight modes. On the other hand, learning-based approaches circumvent modeling complexities, but they often lack theoretical guarantees for stability. In the first part of this thesis, we study the theoretical benefits of these fixed-wing eVTOL aircraft, followed by the derivation of a novel unified control framework. It consists of nonlinear position and attitude controllers using forces and moments as inputs; and control allocation modules that determine desired attitudes and thruster signals. Next, we present a composite adaptation scheme for linear-in-parameter (LiP) dynamics models, which provides accurate realtime estimation for wing and rotor forces based on measurements from a three-dimensional airflow sensor. Then, we introduce a design method to optimize multirotor configuration that ensures a property of robustness against rotor failures. In the second part of the thesis, we use deep neural networks (DNN) to learn part of unmodeled dynamics of the flight vehicles. Spectral normalization that regulates the Lipschitz constants of the neural network is applied for better generalization outside the training domain. The resultant network is utilized in a nonlinear feedback controller with a contraction mapping update, solving the nonaffine-in-control issue that arises. Next, we formulate general methods for designing and training DNN-based dynamics, controller, and observer. The general framework can theoretically handle any nonlinear dynamics with prior knowledge of its structure. Finally, we establish a delay compensation technique that transforms nominal controllers for an undelayed system into a sample-based predictive controller with numerical integration. The proposed method handles both first-order and transport delays in actuators and balances between numerical accuracy and computational efficiency to guarantee stability under strict hardware limitations.</p

    Experimental Investigation of Shrouded Rotor Micro Air Vehicle in Hover and in Edgewise Gusts

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    Due to the hover capability of rotary wing Micro Air Vehicles (MAVs), it is of interest to improve their aerodynamic performance, and hence hover endurance (or payload capability). In this research, a shrouded rotor conguration is studied and implemented, that has the potential to oer two key operational benets: enhanced system thrust for a given input power, and improved structural rigidity and crashworthiness of an MAV platform. The main challenges involved in realising such a system for a lightweight craft are: design of a lightweight and stiff shroud, and increased sensitivity to external flow disturbances that can affect flight stability. These key aspects are addressed and studied in order to assess the capability of the shrouded rotor as a platform of choice for MAV applications. A fully functional shrouded rotor vehicle (disk loading 60 N/m2) was designed and constructed with key shroud design variables derived from previous studies on micro shrouded rotors. The vehicle weighed about 280 g (244 mm rotor diameter). The shrouded rotor had a 30% increase in power loading in hover compared to an unshrouded rotor. Due to the stiff, lightweight shroud construction, a net payload benefit of 20-30 g was achieved. The different components such as the rotor, stabilizer bar, yaw control vanes and the shroud were systematically studied for system efficiency and overall aerodynamic improvements. Analysis of the data showed that the chosen shroud dimensions was close to optimum for a design payload of 250 g. Risk reduction prototypes were built to sequentially arrive at the nal conguration. In order to prevent periodic oscillations in flight, a hingeless rotor was incorporated in the shroud. The vehicle was successfully flight tested in hover with a proportional-integral-derivative feedback controller. A flybarless rotor was incorporated for efficiency and control moment improvements. Time domain system identification of the attitude dynamics of the flybar and flybarless rotor vehicle was conducted about hover. Controllability metrics were extracted based on controllability gramian treatment for the flybar and flybarless rotor. In edgewise gusts, the shrouded rotor generated up to 3 times greater pitching moment and 80% greater drag than an equivalent unshrouded rotor. In order to improve gust tolerance and control moments, rotor design optimizations were made by varying solidity, collective, operating RPM and planform. A rectangular planform rotor at a collective of 18 deg was seen to offer the highest control moments. The shrouded rotor produced 100% higher control moments due to pressure asymmetry arising from cyclic control of the rotor. It was seen that the control margin of the shrouded rotor increased as the disk loading increased, which is however deleterious in terms of hover performance. This is an important trade-off that needs to be considered. The flight performance of the vehicle in terms of edgewise gust disturbance rejection was tested in a series of bench top and free flight tests. A standard table fan and an open jet wind tunnel setup was used for bench top setup. The shrouded rotor had an edgewise gust tolerance of about 3 m/s while the unshrouded rotor could tolerate edgewise gusts greater than 5 m/s. Free flight tests on the vehicle, using VICON for position feedback control, indicated the capability of the vehicle to recover from gust impulse inputs from a pedestal fan at low gust values (up to 3 m/s)

    Aerial Vehicles

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    This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
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