Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System

This research considers the problem of identifying safety constraints and developing Run Time Assurance (RTA) for Deep Reinforcement Learning (RL) Tactical Autopilots that use neural network control systems (NNCS). This research studies a specific use case of an NNCS performing autonomous formation flight while an RTA system provides collision avoidance and geofence assurances. First, Systems Theoretic Accident Models and Processes (STAMP) is applied to identify accidents, hazards, and safety constraints as well as define a functional control system block diagram of the ground station, manned flight lead, and surrogate unmanned wingman. Then, Systems Theoretic Process Analysis (STPA) is applied to the interactions of the the ground station, manned flight lead, surrogate unmanned wingman, and internal elements of the wingman aircraft to identify unsafe control actions, scenarios leading to each, and safety requirements to mitigate risks. This research is the first application of STAMP and STPA to an NNCS bounded by RTA

Dynamic Transfer of Control between Manned and Unmanned Simulation Actors

This thesis continues the ongoing research at the Air Force Institute of Technology\u27s Virtual Environments Laboratory in the area of distributed simulation. As the relevance and interest of interactive simulation as a training medium continues to grow, there is a pressing need to provide more realistic and numerous intelligent autonomous agents for simulations. As those autonomous agents mature and become more realistic, the need exists to be able to handle individual agents by taking control of them and operating them as manned agents at certain points within the simulation. The author started with a protocol proposed in a working draft of the Distributed Interactive Simulation (DIS) Protocol Standard 2.1.1 (Draft). He demonstrates how this protocol can be improved by swapping control between two entities involved in a distributed simulation. The new protocol provides simultaneous transfer while being compatible with the one proposed in the draft standard. The protocol is implemented on two applications developed in the Virtual Environments Laboratory, the Virtual Cockpit (VC) and the Automated Wingman (AW). The anticipated flow of execution begins with the AW requesting assistance. The operator of the VC then can reply by assuming control of the AW. Once the required human operation has been performed, the operator may switch back to the lead aircraft, completing the full cycle of execution

Conceptualization and Application of Deep Learning and Applied Statistics for Flight Plan Recommendation

The Air Forces Pilot Training Next (PTN) program seeks a more efficient pilot training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The PTN program wants to accelerate the training pace and progress in undergraduate pilot training compared to traditional undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to auto-generate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for evaluation in a student’s next training exercise to improve their progress toward fully qualified status

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

ON-BOARD ARTIFICIAL INTELLIGENCE FOR FAILURE DETECTION AND SAFE TRAJECTORY GENERATION

The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. Adequate commanded trajectory generation and modification, as well as high-performance trajectory tracking control laws have been an essential focus of researchers given that integration into the National Air Space (NAS) is becoming a primary need. However, the operational safety of these systems can be easily affected if abnormal flight conditions are present, thereby compromising the nominal bounds of design of the system\u27s flight envelop and trajectory following. This thesis focuses on investigating methodologies for modeling, prediction, and protection of autonomous vehicle trajectories under normal and abnormal flight conditions. An Artificial Immune System (AIS) framework is implemented for fault detection and identification in combination with the multi-goal Rapidly-Exploring Random Tree (RRT*) path planning algorithm to generate safe trajectories based on a reduced flight envelope. A high-fidelity model of a fixed-wing unmanned aerial vehicle is used to demonstrate the capabilities of the approach by timely generating safe trajectories as an alternative to original paths, while integrating 3D occupancy maps to simulate obstacle avoidance within an urban environment

Linear and Non-Linear Control of a Quadrotor UAV

This thesis describes two controllers designed specifically for a quadrotor helicopter unmanned aerial vehicle (UAV). A linear controller and a non-linear controller are discussed for use on the quadrotor helicopter using feedback that is obtained from microelectromechanical systems and GPS. The linear controller is an orientation based PID controller that controls the angles of the quadrotor UAV. The controller was first simulated and the results displayed graphically using FlightGear. Experiments were conducted using this controller on a DraganFlyer X-Pro quadrotor helicopter to prove the proposed method used for closing the feedback loop. The non-linear controller is developed using Lyapunov stability methods. The design goal for this controller is to add a two degree-of-freedom camera postioner to the quadrotor for a total of six degree-of-freedom camera actuator. The UAV will track three desired translational velocities and three angular velocities using only translational and rotational velocities for feedback

Simplex Control Methods for Robust Convergence of Small Unmanned Aircraft Flight Trajectories in the Constrained Urban Environment

Constrained optimal control problems for Small Unmanned Aircraft Systems (SUAS) have long suffered from excessive computation times caused by a combination of constraint modeling techniques, the quality of the initial path solution provided to the optimal control solver, and improperly defining the bounds on system state variables, ultimately preventing implementation into real-time, on-board systems. In this research, a new hybrid approach is examined for real-time path planning of SUAS. During autonomous flight, a SUAS is tasked to traverse from one target region to a second target region while avoiding hard constraints consisting of building structures of an urban environment. Feasible path solutions are determined through highly constrained spaces, investigating narrow corridors, visiting multiple waypoints, and minimizing incursions to keep-out regions. These issues are addressed herein with a new approach by triangulating the search space in two-dimensions, or using a tetrahedron discretization in three-dimensions to define a polygonal search corridor free of constraints while alleviating the dependency of problem specific parameters by translating the problem to barycentric coordinates. Within this connected simplex construct, trajectories are solved using direct orthogonal collocation methods while leveraging navigation mesh techniques developed for fast geometric path planning solutions. To illustrate two-dimensional flight trajectories, sample results are applied to flight through downtown Chicago at an altitude of 600 feet above ground level. The three-dimensional problem is examined for feasibility by applying the methodology to a small scale problem. Computation and objective times are reported to illustrate the design implications for real-time optimal control systems, with results showing 86% reduction in computation time over traditional methods

Development of a flight control architecture for rotary wing UAVs with model based design approach

This thesis describes the design and implementation of various autopilot software architectures for mini/micro rotary-wing unmanned aerial vehicles by exploiting the modelbased design approach. Nowadays in fact, the tendency for software development is changing from manual coding to automatic code generation, in other words, it is becoming model-based. In general, models can be described as abstractions of systems, they are created to serve particular purposes, for example, to present a user-understandable description of the system or to present information in a more intuitive form. Model-based techniques for software design enables the engineer to reduce drastically development time required for software corrections or modi�cations. Under the various chapters, di�erent flight control techniques are presented with theoretical background and tested via simulations and experimental campaigns. All the navigation and control problems presented below arise in development of embedded software that exploits the innovative model-based design technology. In order to provide validations of the proposed solutions, software for simulation and implementation is specialized for the case of multirotor vehicles, which are becoming very helpful systems for many and varied civil operations. This is the reason why part of the text is devoted to multirotor vehicle dynamics

Cognitive Issues Related to Advanced Cockpit Displays: Supporting the Transition Between

A critical issue in military aviation is the pilot’s ability to transition between primarily internal (head-down, instrument-driven) and external (head-up, out of the cockpit) guidance. Experimental cockpit displays were designed and tested for how well they might support this transition phase for military pilots performing time-critical air-to-ground targeting missions such as Forward Air Control and Close Air Support. Twelve subjects performed three sets of experiments using a flight simulator (with simulated heads-up display in the forward field of view) connected to a moving-map display. The experiments were designed to help explain which visual cues in the displays might best help a pilot 1) navigate to a given target area (the “flight guidance” phase of a mission) and 2) search for, find and identify a target (the “target acquisition” phase).This work was sponsored by the Naval Research Laboratory Select Graduate Training Program