Missions envisioned for Micro Air Vehicles (MAVs) such as Intelligence, Surveillance and Reconnaissance (ISR) in urban environments require exceptional agility. These missions typically involve ight through or in close proximity to buildings, tunnels and trees, and require the MAVs to maneuver using sharp dives and climbs along with small-radius turns. The small size of MAVs allows such ight operations within con ned space but the control e ectors must provide su cient maneuverability, while maintaining stability, with only limited sensing capability onboard the platform. To meet these challenges, researchers have long been attracted by the amazing attributes of biological systems, such as those exhibited by birds and insects. Birds can y in dense ocks, executing rapid maneuvers with g-loads far in excess of modern ghter aircrafts, and yet never collide with each other, despite the absence of air tra c controllers. This paper introduces a novel framework for the design and control of a Micro Air Vehicle. The vehicle's conceptual design is based on biologically-inspired principles and emulates a dragon y (Odonata-Anisoptera). The author is borrowing ight mechanism features from the dragon y and merging them with indigenous designs in creating a novel version of a Flapping Wing MAV, called the QV. The QV design incorporates the simplicity of mechanical construction and the complexity of a sophisticated multi-layered Hybrid & Linear / Non-Linear controller to achieve extended ight times and improved agility compared to other Rotary and Flapping Wing MAV designs. The paper talks about the design and control features of the proposed QV design and gives an overview on the developmental e orts been made towards the prototyping of the QV yer. The potential applications for such a high endurance vehicle are numerous, including air-deployable mass surveillance in cluster and swarm formations. The disposability of the vehicle would help in battleeld deployment as well, where such a MAV would be made available to soldiers for proximity sensing and threat level assessment. Other applications would include search and rescue operations and civilian law-enforcement.

We desire a capability for the safety monitoring of complex, mixed hardware/software systems, such as a semi-autonomous car. The field of runtime verification has developed many tools for monitoring the safety of software systems in real time. However, these tools do not allow for uncertainty in the system's state or failure, both of which are essential for the problems we care about. In this thesis I propose a capability for monitoring the safety criteria of mixed hardware/sofware systems that is robust to uncertainty and hardware failure. I start by framing the problem as runtime verification of stochastic, faulty, hidden-state systems. I solve this problem by performing belief state estimation over a novel set of models that combine Biichi automata, for modeling safety requirements, with probabilistic hierarchical constraint automata, for modeling mixed hardware/software systems. This method is innovative in its melding of safety monitoring techniques from the runtime verification community with probabilistic mode estimation techniques from the field of model-based diagnosis. I have verified my approach by testing it on

An Autonomous Flight Manager (AFM) is an integral and necessary component to achieve NASA’s goal of safe and precise landing to extend exploration beyond Low Earth Orbit. This goal includes reduced interaction with mission operations unlike those present during the Apollo era. To that end, the cr ew’s situation awareness of the vehicle’s performance with regard to executing a predefined descent trajectory is critical. The Multi State Excursion Assessment (MSEA) algorithm is a central component in providing this situation awareness by monitoring designated trajectory parameters as defined in a mission profile, which is outside the scope of traditional guidance algorithm functionality . During the mission, the MSEA algorithm compares real-time updates of the current trajectory state with parameter specific thresholds dictating nominal performance. The algorithm fuses this information into a composite assessment of the traversed trajectory versus the predefined nominal trajectory. The assessment includes a projection of emergent trends leading to imminent violation of the prescribed trajectory. Through the use of the MSEA algorithm, the AFM significantly reduces the crew’s monitoring responsibilities by providing them with an assessment of current and future mission performance, which alleviates the n eed to monitor and assess the individual performance parameters manually.

Safe aircraft emergency management requires a two-stage real-time response: rst identify the problem, then alter the ight plan and constrain e ector commands to maximize chances of a safe landing. In cases of degraded aircraft performance, commands must respect new ight envelope limits to avoid loss-of-control. In previous work we have proposed an intuitive approach to adaptive ight planning and guidance in which feasible trim states are sequenced into emergency landing ight plans. Sets of feasible trim states and transitions between these states are derived from post-failure aircraft dynamics, enabling the ight planner to focus on real-time generation of landing trajectory geometry (kinematics) without risk of violating dynamics constraints. This paper integrates the guidance and planning algorithms required to autonomously discover the ight envelope and then to plan a path (in real-time) to a nearby landing runway. Our method accommodates nontraditional ight envelope constraints, including cases in which the aircraft cannot maintain straight and/or level ight. Two F-16 control surface jam failure cases are used to illustrate algorithm integration and performance.

Unmanned Aircraft Systems (UAS) face limitations on their utilization in civil airspace because they do not have the ability to Sense and Avoid (SAA) other air traffic. In recent years, there has been growing interest to provide an effective SAA solution for UAS operations. An effective SAA solution must address both cooperative as well as non-cooperative air traffic. A number of different sensor solutions are being evaluated for SAA pertaining to non-cooperative traffic. Examples of such sensors include electro-optical (EO), on-board radar, passive acoustics, laser radar and ground radar. Using one or more such sensing modalities, it is possible to track a non-cooperative aircraft in the vicinity of the own aircraft otherwise known as an intruder. However, an intruder’s future trajectory is never perfectly known and a SAA system’s performance will always be limited by these uncertainties. One of the components of an SAA system is the logic to decide whether a certain aircraft is on a collision course with the own craft. It, therefore, follows that the performance of this collision detection component will also be limited by the uncertainties in the future trajectory of the intruder. In this paper, we investigate the problem of what is the best that a SAA collision detection system can perform in spite of the future uncertainties in the intruder trajectory.

In this paper we present a feedforward adaptive control scheme with application to the control of a nonlinear aircraft model with uncertainty in the aerodynamic moment coefficients. The feedforward controller is represented by a basis function network. The learning algorithm consists of a gradient based rule plus e-modification. We prove a theorem which guarantees that the origin of the closed loop system is semi-globally uniformly bounded. We further derive lower bounds on the minimum singular values of the feedback gain matrices that ensure this stability result. Numerous simulation examples are presented that illustrate the performance during abrupt changes in the plant parameters.

This paper presents a stability analysis and an application of a recently developed Control Allocator for recovery from Pilot Induced Oscillations (CAPIO). When actuators are rate-saturated due to either aggressive pilot commands, high gain ight control systems or some anomaly in the system, the effective delay in the control loop may increase. This effective delay increase manifests itself as a phase shift between the commanded and actual system signals and can instigate Pilot induced Oscillations (PIO). CAPIO reduces the e ective time delay by minimizing the phase shift between the commanded and the actual attitude accelerations. We present a stability analysis of CAPIO for a scalar system. In addition, we present simulation results for aircraft with cross-coupling which demonstrates the potential of CAPIO serving as an effective PIO handler in adverse conditions.

Satellite mission optimization is a difficult problem that is often encountered in the space domain. However, the problem does contain many common features that can be abstracted away from mission specific requirements. This makes mission optimization a candidate for deployment in a service-oriented architecture environment where capabilities can be reused across satellites and missions. The result is a generic SOA-based genetic algorithm that can be used across missions. A solution evaluating this approach is presented along with conclusions as to its effectiveness.