The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education

In this paper, we introduce the Phoenix drone: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes. The drone has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking. Our hope is that the open-source Phoenix reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'19), Montreal, Canada, May 20-24, 201

Modelling, estimation and control of a twin-helicopter slung load transportation system

The development of a control system to transport and assemble cargo using two helicopters is presented in this thesis. It is more economical to use multiple lower cost helicopters in a coordinated manner to carry cargo than to use a single high performance helicopter for the transportation task. The reason for the generally higher cost of hiring high performance helicopters, is because they are not required often, and so, remain idle for most of their lifetime. Thus, using less specialised, lower performing helicopters to share the load is cheaper. Beyond just sharing the load of the cargo, the objective in this investigation is to control the attitude such that precise placement of the cargo can be made. This objective cannot be achieved using a single helicopter, unless a sophisticated tethering mechanism is developed. The installation of wind-turbine blades, powerline towers and radio masts in remote locations, are examples of where the application of this technology may be useful. The investigation of this thesis is around modelling, estimation and control of the twinhelicopter slung load transportation system. The title reflects the investigation that was required to be done to determine whether a scheme could be realisable. To test the concept, an experimental platform was developed. A small, light-weight and high performance avionics system was designed and interfaced to the helicopters. The experimentation was done indoors, and hence, the flying volume was limited. For the purpose of feedback and analysis, a motion capture system was developed to track the position and attitude of the helicopters. A high-fidelity mathematical model of a small-scale helicopter was developed. Estimation algorithms were then developed to optimally fuse the data from the instrumentation designed. The data was then used in a system identification exercise to find the parameters that capture the dynamics of the helicopter. The full constrained model of the twin-helicopter slung load dynamics was then developed. The high-fidelity multivariable, interacting system was then linearised to generate a set of uncertain plants. Unexpected resonant modes were investigated using modal analysis to understand their source. Robust controllers were designed using Quantitative Feedback Theory (QFT) for the individual helicopter attitude and altitude loops. A solution was found for the twin-helicopter load transportation system by decoupling the plant with a static pre-compensator and then designing a decentralised QFT controller for the 6 × 6 plant. The effort of this thesis is towards the (practical) realisation of a twin-helicopter aerial crane capable of attitude control; the architecture for the industrialisation of the twin-helicopter load transportation system is proposed

CONTROL STRATEGY OF MULTIROTOR PLATFORM UNDER NOMINAL AND FAULT CONDITIONS USING A DUAL-LOOP CONTROL SCHEME USED FOR EARTH-BASED SPACECRAFT CONTROL TESTING

Over the last decade, autonomous Unmanned Aerial Vehicles (UAVs) have seen increased usage in industrial, defense, research, and academic applications. Specific attention is given to multirotor platforms due to their high maneuverability, utility, and accessibility. As such, multirotors are often utilized in a variety of operating conditions such as populated areas, hazardous environments, inclement weather, etc. In this study, the effectiveness of multirotor platforms, specifically quadrotors, to behave as Earth-based satellite test platforms is discussed. Additionally, due to concerns over system operations under such circumstances, it becomes critical that multirotors are capable of operation despite experiencing undesired conditions and collisions which make the platform susceptible to on-board hardware faults. Without countermeasures to account for such faults, specifically actuator faults, a multirotors will experience catastrophic failure. In this thesis, a control strategy for a quadrotor under nominal and fault conditions is proposed. The process of defining the quadrotor dynamic model is discussed in detail. A dual-loop SMC/PID control scheme is proposed to control the attitude and position states of the nominal system. Actuator faults on-board the quadrotor are interpreted as motor performance losses, specifically loss in rotor speeds. To control a faulty system, an additive control scheme is implemented in conjunction with the nominal scheme. The quadrotor platform is developed via analysis of the various subcomponents. In addition, various physical parameters of the quadrotor are determined experimentally. Simulated and experimental testing showed promising results, and provide encouragement for further refinement in the future

Experimental Investigation of a MAV-Scale Cyclocopter

The development of an efficient, maneuverable, and gust tolerant hovering concept with a multi-modal locomotion capability is key to the success of micro air vehicles (MAVs) operating in multiple mission scenarios. The current research investigated performance of two unconventional cycloidal-rotor-based (cyclocopter) configurations: (1) twin-cyclocopter and (2) all-terrain cyclocopter. The twin-cyclocopter configuration used two cycloidal rotors (cyclorotors) and a smaller horizontal edge-wise nose rotor to counteract the torque produced by the cyclorotors. The all-terrain cyclocopter relied on four cyclorotors oriented in an H-configuration. Objectives of this research include the following: (1) develop control strategies to enable level forward flight of a cyclocopter purely relying on thrust vectoring, (2) identify flight dynamics model in forward flight, (3) experimentally evaluate gust tolerance strategies, and (4) determine feasibility and performance of multi-modal locomotion of the cyclocopter configuration. The forward flight control strategy for the twin-cyclocopter used a unique combination of independent thrust vectoring and rotational speed control of the cyclorotors. Unlike conventional rotary-winged vehicles, the cyclocopter propelled in forward flight by thrust vectoring instead of pitching the entire fuselage. While the strategy enabled the vehicle to maintain a level attitude in forward flight, it was accompanied by significant yaw-roll controls coupling and gyroscopic coupling. To understand these couplings and characterize the bare airframe dynamics, a 6-DOF flight dynamics model of the cyclocopter was extracted using a time-domain system identification technique. Decoupling methods involved simultaneously mixing roll and yaw inputs in the controller. After implementing the controls mixing strategy in the closed-loop feedback system, the cyclocopter successfully achieved level forward flight up to 5 m/s. Thrust vectoring capability also proved critical for gust mitigation. Thrust vectoring input combined with flow feedback and position feedback improved gust tolerance up to 4 m/s for a twin-cyclocopter mounted on a 6-DOF test stand. Flow feedback relied on a dual-axis flowprobe attached to differential pressure sensors and position feedback was based on data recorded by the VICON motion capture system. The vehicle was also able to recover initial position for crosswind scenarios tested at various side-slip angles up to 30 degrees. Unlike existing multi-modal platforms, the all-terrain cyclocopter solely relied on its four cyclorotors as main source of propulsion, as well as wheels. Aerial and aquatic modes used aerodynamic forces generated by modulating cyclorotor rotational speeds and thrust vectors while terrestrial mode used motor torque. In aerial mode, cyclorotors operated at 1550 rpm and consumed 232 W to sustain hover. In terrestrial mode, forward translation at 2 m/s required 28 W, which was an 88% reduction in power consumption required to hover. In aquatic mode, cyclorotors operated at 348 rpm to achieve 1.3 m/s translation and consumed 19 W, a 92% reduction in power consumption. With only a modest weight addition of 200 grams for wheels and retractable landing gear, the versatile cyclocopter platform achieved sustained hover, efficient translation and rotational maneuvers on ground, and aquatic locomotion

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach

Flight controller synthesis via deep reinforcement learning

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

Real-time fault identification for developmental turbine engine testing

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing.An automated data validation and fault identification approach employing enginecycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady state and transient engine operation. Automated steady-state, transient, and real-time model calibration processes are also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in real-time.The result is a capability to detect data and engine anomalies in real-time during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance. The component-level model-based engine performance and fault identification technique of the present research is capable of: identifying measurement errors on the order of 0.5 percent (e.g., sensor bias, drift,level shift, noise, or poor response) in facility fuel flow, airflow, and thrust measurements; identifying measurement errors in engine aerothermodynamic measurements (rotorspeeds, gas path pressures and temperatures); identifying measurement errors in engine control sensors (e.g., leaking/biased pressure sensor, slowly responding pressure measurement) and variable geometry rigging (e.g., misset guide vanes or nozzle area) that would invalidate a test or series of tests; identifying abrupt faults (e.g., faults due to domestic object damage, foreign object damage, and control anomalies); identifying slow faults (e.g., component or overall engine degradation, and sensor drift). Specifically, the technique is capable of identifying small changes in compressor (or fan) performance on the order of 0.5 percent; and being easily extended to diagnose secondary failure modes and to verify any modeling assumptions that may arise for developmental engine tests (e.g., increase in turbine flow capacity, inaccurate measurement of facility bleed flows, horsepower extraction, etc.).The component-level model-based engine performance and fault identification method developed in the present work brings together features which individually and collectively advance the state-of-the-art. These features are separated into three categories: advancements to effectively quantify off-nominal behavior, advancements to provide a fault detection capability that is practical from the viewpoint of the analysis,implementation, tuning, and design, and advancements to provide a real-time fault detection capability that is reliable and efficient

Flight evaluations of sliding mode fault tolerant controllers

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThis paper considers the development of fault tolerant controllers (FTC) and their application to aerospace system. In particular, given the extensive and growing literature in this area, this paper focusses on methods where the schemes have been implemented and flight tested. One thread of the fault tolerant control literature has involved sliding mode controllers. This paper considers a specific class of sliding mode FTC which incorporates control allocation to exploit over-actuation (which is typically present in aerospace systems). The paper describes implementations of these ideas on a small quadrotor UAV and also piloted flight tests on a full-scale twin-engined aircraft