Empirical Evaluation of Ground, Ceiling, and Wall Effect for Small-Scale Rotorcraft

Ground effect refers to the apparent increase in lift that an aircraft experiences when it flies close to the ground. For helicopters, this effect has been modeled since the 1950\u27s based on the work of Cheeseman and Bennett, perhaps the most common method for predicting hover performance due to ground effect. This model, however, is based on assumptions that are often not realistic for small-scale rotorcraft because it was developed specifically for conventional helicopters. It is clear that the Cheeseman-Bennett model cannot be applied to today\u27s multirotor UAVs. Experimental findings suggest that some of the conventional thinking surrounding helicopters cannot be applied directly to rotorcraft using fixed propellers at variable speeds (e.g. multirotors). A parametric multirotor-specific ground effect model is developed and presented to overcome some of the limitations in classical helicopter theory. Likewise, ceiling effect refers to the apparent increase in lift that a rotorcraft experiences when flying close to a ceiling or any similar surface that is present above the rotor(s). Ceiling effect is similar in principle to ground effect, and can be explained using a similar equation. Ceiling effect, however, was never explored in detail for conventional helicopters because large manned aircraft do not operate in enclosed spaces. For multirotors, the work presented in this dissertation suggests that the classical helicopter theory adequately describes ceiling effect performance. Wall effect is the phenomena that occurs when a rotorcraft flies near a vertical wall, and has the tendency to pitch towards the wall and be drawn into it. Wall effect is the least-understood of these three areas of interest. Wall effect has not been explored in great detail for any aircraft, and is addressed in detail in this dissertation. The recent widespread use of small-scale UAVs and the demand for increased autonomy when flying in enclosed environments has created a need for detailed studies of ground effect, ceiling effect and wall effect. Ultimately, this work provides foundations for the development of an improved UAV flight controller that can accurately account for various aerodynamic disturbances that occur near surfaces and structures to improve flight stability

Design and Implementation of an Artificial Neural Network Controller for Quadrotor Flight in Confined Environment

Quadrotors offer practical solutions for many applications, such as emergency rescue, surveillance, military operations, videography and many more. For this reason, they have recently attracted the attention of research and industry. Even though they have been intensively studied, quadrotors still suffer from some challenges that limit their use, such as trajectory measurement, attitude estimation, obstacle avoidance, safety precautions, and land cybersecurity. One major problem is flying in a confined environment, such as closed buildings and tunnels, where the aerodynamics around the quadrotor are affected by close proximity objects, which result in tracking performance deterioration, and sometimes instability. To address this problem, researchers followed three different approaches; the Modeling approach, which focuses on the development of a precise dynamical model that accounts for the different aerodynamic effects, the Sensor Integration approach, which focuses on the addition of multiple sensors to the quadrotor and applying algorithms to stabilize the quadrotor based on their measurements, and the Controller Design approach, which focuses on the development of an adaptive and robust controller. In this research, a learning controller is proposed as a solution for the issue of quadrotor trajectory control in confined environments. This controller utilizes Artificial Neural Networks to adjust for the unknown aerodynamics on-line. A systematic approach for controller design is developed, so that, the approach could be followed for the development of controllers for other nonlinear systems of similar form. One goal for this research is to develop a global controller that could be applied to any quadrotor with minimal adjustment. A novel Artificial Neural Network structure is presented that increases learning efficiency and speed. In addition, a new learning algorithm is developed for the Artificial Neural Network, when utilized with the developed controller. Simulation results for the designed controller when applied to the Qball-X4 quadrotor are presented that show the effectiveness of the proposed Artificial Neural Network structure and the developed learning algorithm in the presence of variety of different unknown aerodynamics. These results are confirmed with real time experimentation, as the developed controller was successfully applied to Quanser’s Qball-X4 quadrotor for the flight control in confined environment. The practical challenges associated with the application of such a controller for quadrotor flight in confined environment are analyzed and adequately resolved to achieve an acceptable tracking performance

Empirical Evaluation of Ground, Ceiling, and Wall Effect for Small-Scale Rotorcraft

Ground effect refers to the apparent increase in lift that an aircraft experiences when it flies close to the ground. For helicopters, this effect has been modeled since the 1950\u27s based on the work of Cheeseman and Bennett, perhaps the most common method for predicting hover performance due to ground effect. This model, however, is based on assumptions that are often not realistic for small-scale rotorcraft because it was developed specifically for conventional helicopters. It is clear that the Cheeseman-Bennett model cannot be applied to today\u27s multirotor UAVs. Experimental findings suggest that some of the conventional thinking surrounding helicopters cannot be applied directly to rotorcraft using fixed propellers at variable speeds (e.g. multirotors). A parametric multirotor-specific ground effect model is developed and presented to overcome some of the limitations in classical helicopter theory. Likewise, ceiling effect refers to the apparent increase in lift that a rotorcraft experiences when flying close to a ceiling or any similar surface that is present above the rotor(s). Ceiling effect is similar in principle to ground effect, and can be explained using a similar equation. Ceiling effect, however, was never explored in detail for conventional helicopters because large manned aircraft do not operate in enclosed spaces. For multirotors, the work presented in this dissertation suggests that the classical helicopter theory adequately describes ceiling effect performance. Wall effect is the phenomena that occurs when a rotorcraft flies near a vertical wall, and has the tendency to pitch towards the wall and be drawn into it. Wall effect is the least-understood of these three areas of interest. Wall effect has not been explored in great detail for any aircraft, and is addressed in detail in this dissertation. The recent widespread use of small-scale UAVs and the demand for increased autonomy when flying in enclosed environments has created a need for detailed studies of ground effect, ceiling effect and wall effect. Ultimately, this work provides foundations for the development of an improved UAV flight controller that can accurately account for various aerodynamic disturbances that occur near surfaces and structures to improve flight stability

Aerodynamic Power Control for Multirotor Aerial Vehicles

In this paper, a new motor control input and controller for small-scale electrically powered multirotor aerial vehicles is proposed. The proposed scheme is based on controlling aerodynamic power as opposed to the rotor speed of each motor-rotor system. Electrical properties of the brushless direct current motor are used to both estimate and control the mechanical power of the motor system which is coupled with aerodynamic power using momentum theory analysis. In comparison to current state-of-the-art motor control for multirotor aerial vehicles, the proposed approach is robust to unmodelled aerodynamic effects such as wind disturbances and ground effects. Theory and experimental results are presented to illustrate the performance of the proposed motor control.This work was supported by Australian Research Council through Discovery Grant DP120100316 and National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (MSIP) (No. 2009-0083495, 2013-013911)

Improving MAV control by predicting aerodynamic effects of obstacles

Abstract — Building on our previous work [1], in this paper we demonstrate how it is possible to improve flight control of a MAV that experiences aerodynamic disturbances caused by objects on its path. Predictions based on low resolution depth images taken at a distance are incorporated into the flight control loop on the throttle channel as this is adjusted to target undisrupted level flight. We demonstrate that a statistically significant improvement (p << 0.001) is possible for some common obstacles such as boxes and steps, compared to using conventional feedback-only control. Our approach and results are encouraging toward more autonomous MAV exploration strategies. I

Baseline Assumptions and Future Research Areas for Urban Air Mobility Vehicles

NASA is developing Urban Air Mobility (UAM) concepts to (1) create first-generation reference vehicles that can be used for technology, system, and market studies, and (2) hypothesize second-generation UAM aircraft to determine high-payoff technology targets and future research areas that reach far beyond initial UAM vehicle capabilities. This report discusses the vehicle-level technology assumptions for NASAs UAM reference vehicles, and highlights future research areas for second-generation UAM aircraft that includes deflected slipstream concepts, low-noise rotors for edgewise flight, stacked rotors/propellers, ducted propellers, solid oxide fuel cells with liquefied natural gas, and improved turbo shaft and reciprocating engine technology. The report also highlights a transportation network-scale model that is being developed to understand the impact of these and other technologies on future UAM solutions

NeuroBEM: Hybrid Aerodynamic Quadrotor Model

Quadrotors are extremely agile, so much in fact, that classic first-principle-models come to their limits. Aerodynamic effects, while insignificant at low speeds, become the dominant model defect during high speeds or agile maneuvers. Accurate modeling is needed to design robust high-performance control systems and enable flying close to the platform's physical limits. We propose a hybrid approach fusing first principles and learning to model quadrotors and their aerodynamic effects with unprecedented accuracy. First principles fail to capture such aerodynamic effects, rendering traditional approaches inaccurate when used for simulation or controller tuning. Data-driven approaches try to capture aerodynamic effects with blackbox modeling, such as neural networks; however, they struggle to robustly generalize to arbitrary flight conditions. Our hybrid approach unifies and outperforms both first-principles blade-element theory and learned residual dynamics. It is evaluated in one of the world's largest motion-capture systems, using autonomous-quadrotor-flight data at speeds up to 65km/h. The resulting model captures the aerodynamic thrust, torques, and parasitic effects with astonishing accuracy, outperforming existing models with 50% reduced prediction errors, and shows strong generalization capabilities beyond the training set.Comment: 9 pages + 1 pages reference