Nonlinear Controller Design for UAVs with Time-Varying Aerodynamic Uncertainties

Unmanned Aerial Vehicles (UAVs) are here and they are here to stay. Unmanned Aviation has expanded significantly in recent years and research and development in the field of navigation and control have advanced beyond expectations. UAVs are currently being used for defense programs around the world but the range of applications is expected to grow in the near future, with civilian applications such as environmental and aerial monitoring, aerial surveillance and homeland security being some representative examples. Conventional and commercially available small-scale UAVs have limited utilization and applicability to executing specific short-duration missions because of limitations in size, payload, power supply and endurance. This fact has already marked the dawn of a new era of more powerful and versatile UAVs (e.g. morphing aircraft), able to perform a variety of missions. This dissertation presents a novel, comprehensive, step-by-step, nonlinear controller design framework for new generation, non-conventional UAVs with time-varying aerodynamic characteristics during flight. Controller design for such UAVs is a challenging task mainly due to uncertain aerodynamic parameters in the UAV mathematical model. This challenge is tackled by using and implementing μ-analysis and additive uncertainty weighting functions. The technique described herein can be generalized and applied to the class of non-conventional UAVs, seeking to address uncertainty challenges regarding the aircraft\u27s aerodynamic coefficients

Modeling and flight testing of differential thrust and thrust vectoring on a small UAV

The primary objectives of this research are to mathematically model the propulsion forces applied to the aircraft during nominal, differential thrust, and thrust vectored flight configurations, and verify this modeling through simulation and flight testing experiments. This thesis outlines the modeling process, simulator development, design, and implementation of a propulsion assisted control system for the WVU Flight Control Systems Lab (FCSL) research aircraft. Differential thrust and thrust vectoring introduce additional propulsive terms in the aircraft force equations that are not present when the thrust line passes through the center of gravity. These additional forces were modeled and incorporated into a simulator of the research aircraft. The effects from differential thrust were small and difficult to quantify. The thrust vectoring effects were also found to be small with the elevator having significantly more pitch control over the vectored motors at the simulated flight conditions.;Differential thrust was implemented using the on-board computer to command a different thrust level to each motor. The desired thrust differential was programed into a flight scheme based on simulation data, and activated during flight via a control switch on the transmitter. The thrust vectoring mechanism was designed using SolidWorksRTM, built and tested outside of the aircraft, and finally incorporated into the aircraft. A high torque servo was used to rotate the motor mounting bar and vector the motors to a desired deflection. Utilizing this mechanism, the thrust vectoring was flight tested, mimicking scenarios tested in simulation. The signal to noise ratio was very low, making it difficult to identify the small changes in the aircraft parameters caused by the vectored thrust

Development of Fault Tolerant Adaptive Control Laws for Aerospace Systems

The main topic of this dissertation is the design, development and implementation of intelligent adaptive control techniques designed to maintain healthy performance of aerospace systems subjected to malfunctions, external parameter changes and/or unmodeled dynamics. The dissertation is focused on the development of novel adaptive control configurations that rely on non-linear functions that appear in the immune system of living organisms as main source of adaptation. One of the main goals of this dissertation is to demonstrate that these novel adaptive control architectures are able to improve overall performance and protect the system while reducing control effort and maintaining adequate operation outside bounds of nominal design. This research effort explores several phases, ranging from theoretical stability analysis, simulation and hardware implementation on different types of aerospace systems including spacecraft, aircraft and quadrotor vehicles. The results presented in this dissertation are focused on two main adaptivity approaches, the first one is intended for aerospace systems that do not attain large angles and use exact feedback linearization of Euler angle kinematics. A proof of stability is presented by means of the circle Criterion and Lyapunov’s direct method. The second approach is intended for aerospace systems that can attain large attitude angles (e.g. space systems in gravity-less environments), the adaptation is incorporated on a baseline architecture that uses partial feedback linearization of quaternions kinematics. In this case, the closed loop stability was analyzed using Lyapunov’s direct method and Barbalat’s Lemma. It is expected that some results presented in this dissertation can contribute towards the validation and certification of direct adaptive controllers

An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers

Developing optimal controllers for aggressive high-speed quadcopter flight is a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning can achieve real-time optimal control in some specific scenarios. In these methods, the networks (termed G&CNets) are trained to learn the optimal state feedback from a dataset of optimal trajectories. An important problem with these methods is the reality gap encountered in the sim-to-real transfer. In this work, we trained G&CNets for energy-optimal end-to-end control on the Bebop drone and identified the unmodeled pitch moment as the main contributor to the reality gap. To mitigate this, we propose an adaptive control strategy that works by learning from optimal trajectories of a system affected by constant external pitch, roll and yaw moments. In real test flights, this model mismatch is estimated onboard and fed to the network to obtain the optimal rpm command. We demonstrate the effectiveness of our method by performing energy-optimal hover-to-hover flights with and without moment feedback. Finally, we compare the adaptive controller to a state-of-the-art differential-flatness-based controller in a consecutive waypoint flight and demonstrate the advantages of our method in terms of energy optimality and robustness.Comment: 7 pages, 11 figure

Incremental twisting fault tolerant control for hypersonic vehicles with partial model knowledge

A passive fault tolerant control scheme is proposed for the full reentry trajectory tracking of a hypersonic vehicle in the presence of modelling uncertainties, external disturbances, and actuator faults. To achieve this goal, the attitude error dynamics with relative degree two is formulated first by ignoring the nonlinearities induced by the translational motions. Then, a multivariable twisting controller is developed as a benchmark to ensure the precise tracking task. Theoretical analysis with the Lyapunov method proves that the attitude tracking error and its first-order derivative can simultaneously converge to the origin exponentially. To depend less on the model knowledge and reduce the system uncertainties, an incremental twisting fault tolerant controller is derived based on the incremental nonlinear dynamic inversion control and the predesigned twisting controller. Notably, the proposed controller is user friendly in that only fixed gains and partial model knowledge are required

Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

2004 Research Engineering Annual Report

Selected research and technology activities at Dryden Flight Research Center are summarized. These activities exemplify the Center's varied and productive research efforts