Mitigating GPS and ADS-B Risks for UAS

This project is funded under the FAA ASSURE program. Unvalidated or unavailable GPS and “ADS-B In” data poses security and safety risks to automated UAS navigation and to Detect and Avoid operations. Erroneous, spoofed, jammed or drop outs of GPS data may result in unmanned aircraft position and navigation being incorrect. This may result in a fly away beyond radio control, flight into infrastructure or flight into controlled airspace. Erroneous, spoofed, jammed or drop outs of “ADSB-In” data may result in automated unmanned aircraft being unable to detect and avoid other aircraft or result in detecting and avoiding illusionary aircraft. In this project, the research team is investigating different strategies to mitigate such risks and proposing methodologies to increase safety of UAS operations within the National Airspace. Several topics related to this project include simulation of dynamic systems, artificial intelligence, flight testing of UAS and hardware implementation

Shielded UAS Operations Detect and Avoid

This project is funded under the FAA ASSURE program. Certain small UAS (sUAS) Beyond Visual Line of Sight (BVLOS) operations, such as structural inspection, may be in close proximity to structures that are collision hazards for manned aircraft. These types of operations that are in close proximity to manned aviation flight obstacles such that they provide significant protection from conflicts and collisions with manned aircraft are termed “shielded” operations. This effort is intended to identify risks and recommend solutions to the FAA that enable shielded UAS operations. Several topics related to this project include simulation of dynamic systems, simulation environment programming, guidance, control and dynamics, and hardware implementation

Comparison of Optimal and Bioinspired Adaptive Control Laws for Spacecraft Sloshing Dynamics

The presence of fuel slosh dynamics in a spacecraft system during a maneuver leads to attitude control system (ACS) performance degradation resulting in attitude tracking errors and instability. The dangers of fuel sloshing become more significant with close proximity operations. This paper conducts a comparative study of different optimal control techniques as well as a novel application of a model reference artificial immune system (MRAIS) adaptive controller. A linearized model of a realistic spacecraft dynamic model incorporating fuel slosh is derived using a mass-spring analogy. Simulations with both the linearized model and the full nonlinear equations of motion are performed to achieve the control objective: to suppress the fuel slosh dynamics while obtaining the desired attitude. A performance index quantifies the performance of each ACS design. The MRAIS is proven robust and capable of suppressing fuel slosh even in the presence of system failures and disturbances

Bio-Inspired Feedback Linearized Adaptive Control For a Thrust Vectoring Free-Flyer Vehicle

Intelligent unmanned robotic systems have recently gained popularity due to their ability to potentially explore inaccessible and dynamically changing environments. In these environments, these vehicles might be subjected to unique types of disturbances that may lead to mission performance degradation. This paper describes the design, development and proof of concept of a novel adaptive control that combines concepts from model reference and feedback linearization and it is augmented via nonlinear bounded functions typical in immune system responses of living organisms. Proof of stability of the proposed control law using Circle Criterion is presented. Numerical hardware in the loop simulations along with actual implementation are performed using a gimbaled mini-free flyer vehicle that uses thrust vectoring control actuation. A set of performance index metrics are used to quantify and assess the performance of the adaptive control system which shows stabilizing capabilities in the presence of system disturbances and uncertainties

Aircraft Fault Tolerance: A Biologically Inspired Immune Framework for Sub-System Failures

The capability to efficiently perform checkout, testing, and monitoring of aerospace vehicles, subsystems, and components during flight operations in the presence of uncertainties, nonlinearities, and component failures is a challenging problem. This requires intelligent systems with the ability to perform timely status determination, diagnostics, prognostics, and decision making as a key aspect to increase the safety of missions. This book describes the design, development, and flight-simulation testing of an integrated Artificial Immune System (AIS) for detection, identification, and evaluation of a wide variety of sensor, actuator, propulsion, and structural failures/damages including the prediction of the achievable states and other limitations on performance and handling qualities. The NASA IFCS F-15 research aircraft model is used and represents a supersonic fighter which include model following adaptive control laws based on non- linear dynamic inversion and artificial neural network augmentation. Flight simulation tests are described to analyze and demonstrate the performance of the immunity-based architecture