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The advent of third aviation revolution that is seeking to enable transportation where users have access to immediate and flexible air travel. The users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithm together to assess vehicle capabilities, project these into the future and make decision on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicle before the final state of autonomy is reached.

This paper considers an approach for modelling transport aircraft trajectories that can facilitate their rapid evaluation and modification, either en route or in terminal control areas, with the goal of efficiently making use of airspace and runways by a large population of vehicles without pairwise violation of separation criteria.

This paper develops an easily implemented model for simulating random horizontal wind profiles over the Kennedy Space Center (KSC) at Cape Canaveral, Florida. The model is intended for use in Monte Carlo launch vehicle simulations of the type employed in mission planning. In this type of simulation, the large number of profiles needed for statistical fidelity of such simulation experiments makes the use of actual wind measurements impractical. The model is based on measurements made at KSC and represents vertical correlations by a decaying exponential model that is parameterized via least-squares parameter fit to the sample data. The model is demonstrated by comparing two open-loop Monte Carlo simulations of an asymmetric, heavy-lift launch vehicle. In the first simulation, measured wind profiles are used, whereas in the second, the wind profiles are generated using the stochastic model. The simulations indicate that the use of either the measured or simulated wind field results in similar launch vehicle performance. Although the model documented here is based on winter data, it can easily be adapted to other seasons.

The coherent launch-site atmospheric wind sounder (CLAWS) is a lidar atmospheric wind sensor designed to measure the winds above space launch facilities to an altitude of 20 km. In our development studies, lidar sensor requirements are defined, a system to meet those requirements is defined and built, and the concept is evaluated, with recommendations for the most feasible and cost-effective lidar system for use as an input to a guidance and control system for missile or spacecraft launches. The ability of CLAWS to meet NASA goals for increased safety and launch/mission flexibility is evaluated in a field test program at Kennedy Space Center (KSC) in which we investigate maximum detection range, refractive turbulence, and aerosol backscattering efficiency. The Nd:YAG coherent lidar operating at 1.06 µm with 1-J energy per pulse is able to make real-time measurements of the three-dimensional wind field at KSC to an altitude of 26 km, in good agreement with our performance simulations. It also shows the height and thickness of the volcanic layer caused by the volcanic eruption of Mount Pinatubo in the Philippines.

Airbreathing hypersonic systems offer distinct performance advantages over rocket-based systems for space access vehicles. Realization of this potential performance gain will require optimum integration of many different technology areas, including vehicle dynamics and controls. Typically detailed dynamic analysis and controls design are performed after a vehicle has completed the conceptual design phase. However, due to the complex interactions in an airbreathing hypersonic vehicle, delaying application of detailed dynamics analysis and controls design could result in suboptimal vehicle designs. The Guidance, Navigation, and Controls discipline of NASA's Fundamental Aeronautics Program Hypersonics area is applying dynamic analysis and controls design early in the vehicle conceptual design. This activity includes development of controls focused dynamic models, creation of an integrated design environment for dynamics analysis and controls design, and early evaluation of vehicle controllability aspects. This effort will provide valuable insight into the vehicle multidisciplinary design and optimization phase.

Angular bias momentum offers significant stability augmentation for hovering flight vehicles. The reliance of the vehicle on thrust vectoring for agility and disturbance rejection is greatly reduced with significant levels of stored angular momentum in the system. A methodical procedure for bias momentum sizing has been developed in previous studies. This current study provides groundwork for experimental validation of that method using an experimental vehicle called the Dual-Spin Test Device, a thrust-levitated platform. Using measured data the vehicle's thrust vectoring units are modeled and a gust environment is designed and characterized. Control design is discussed. Preliminary experimental results of the vehicle constrained to three rotational degrees of freedom are compared to simulation for a case containing no bias momentum to validate the simulation. A simulation of a bias momentum dominant case is presented.

This paper describes two attitude control laws suitable for atmospheric flight vehicles with a steady angular momentum bias in the vehicle yaw axis. This bias is assumed to be provided by an internal flywheel, and is introduced to enhance roll and pitch stiffness. The first control law is based on Lyapunov stability theory, and stability proofs are given. The second control law, which assumes that the angular momentum bias is large, is based on a classical PID control. It is shown that the large yaw-axis bias requires that the PI feedback component on the roll and pitch angle errors be cross-fed. Both control laws are applied to a vehicle simulation in the presence of disturbances for several values of yaw-axis angular momentum bias. It is seen that both control laws provide a significant improvement in attitude performance when the bias is sufficiently large, but the nonlinear control law is also able to provide improved performance for a small value of bias. This is important because the smaller bias corresponds to a smaller requirement for mass to be dedicated to the flywheel.

This paper describes an approach based on using both bias momentum and multiple control moment gyros for controlling the attitude of statically unstable thrust-levitated vehicles in hover or slow translation. The stabilization approach described in this paper uses these internal angular momentum transfer devices for stability, augmented by thrust vectoring for trim and other outer loop control functions, including CMG stabilization/ desaturation under persistent external disturbances. Simulation results show the feasibility of (1) improved vehicle performance beyond bias momentum assisted vector thrusting control, and (2) using control moment gyros to significantly reduce the external torque required from the vector thrusting machinery.

A methodology for improving attitude stability and control for low-speed and hovering air vehicle is under development. In addition to aerodynamically induced control forces such as vector thrusting, the new approach exploits the use of bias momenta and torque actuators, similar to a class of spacecraft system, for its guidance and control needs. This approach will be validated on a free-flying research platform under development at NASA Langley Research Center. More broadly, this platform also serves as an in-house testbed for research in new technologies aimed at improving guidance and control of a Vertical Take-Off and Landing (VTOL) vehicle. 1 Research Motivation This paper gives an overview of the ongoing research in precision guidance and robust control based on the NASA Flying Test Platform (NFTP) research vehicle currently under development at NASA Langley Research Center. The research is motivated by core GN&C objectives that include optimal guidance and navigation, and robust attitude and position stabilization under uncertain exogenous disturbances and model variations. A key goal of this research is to investigate novel technologies to improve attitude stability particularly during hovering or at low airspeed flight wherein conventional control effectors become ineffective. This particular need arises from current limitations in attitude stabilization performance for ∗Senior Research Engineer, Guidance and Controls Branch, k.b.lim@larc.nasa.gov †Staff Scientist, NIA, j.y.shin@larc.nasa.gov ‡Senior Research Engineer, Systems Integration Branch, e.g.cooper@larc.nasa.gov §Senior Research Engineer, Guidance and Controls Branch, d.d.moerder@larc.nasa.gov ¶Research Engineer, Guidance and Controls Branch, t.h.khong@larc.nasa.gov ‖Technician, Guidance and Controls Branch, m.f.smith@larc.nasa.gov vector-thrusted air vehicles such as Osprey, Harrier VTOL, and for helicopters with sling loads. The basic and common limitation in the above control problem appears to be a lack of an accurate dynamical model suited for response prediction and controller design to attain precision and reliable performance under unsteady aerodynamics. In retrospect, this apparent performance limitation in the use of a vector-thrusting approach for dynamical stabilization of the vehicle is not surprising since stability is fundamentally an unsteady aerodynamics phenomenon. This phenomenon is the current limiting factor in predicting loads and responses on the vehicle (see for example, [1], [2]), which are necessary ingredients for robust and precise stabilizing feedback control. 2 Test Platform Description 2.1 System Configuration Figure 1 shows the NFTP system, which consists of a square rigid platform levitated and propelled by a set of four battery-powered ducted fans each with a pair of control vanes. The vehicle employs a sensing system that fuses Inertial Measurement Unit (IMU) sensors and an optically based 6-DOF target tracking inertial position/attitude sensing system. The rigid platform is about 1.2 meters wide and weighs about 12 kg. The NFTP is a free flying vehicle designed to fly within a flight envelope box which is approximately 5 meters wide, indoors. Figure 2 is a schematic of the hardware architecture and major components for the basic NFTP system. The PC104 is used as the onboard flight control computer, which will implement an inner-loop controller for stability augmentation and has the capability of a wireless datalink to a ground control computer. A dSPACE system is used as the ground computer whose primary function is guidance from an operator, data logging, and communication with a vision system which tracks the flying vehicle. The flight control system will be capable of using all sensor measurements which include inertial measure

A method for interpolating multidimensional tabular data is developed herein. The main goals of the multidimensional interpolation scheme are t o obtain second-derivative continuity across data points, and t o have a computationally efficient method for obtaining function evaluations. These goals are accomplished by using multilinear interpolating functions (bilinear, trilinear, etc.) over most of the data set, and using higher-order polynomial surfaces in the vicinity of the data points to connect the multilinear surfaces in a smooth way. This provides second-derivative continuity, and since the majority of the data table is modeled with multilinear functions, the function evaluations are faster than when using higher-order polynomials. An additional feature of this method is that the interpolating functions are found locally rather than globally. This allows for N-dimensional tables to be handled very efficiently. For the special case of one-dimensional data interpolation, it is shown that the interpolating function preserves convexity of the data ( i . e . , no spurious oscillations are introduced) and is monotonic between data points (z.e., all local extrema occur at data points).

The problem of constant gain output feedback regulator design for linear systems with ill-conditioned dynamics is considered in the context of singular perturbation theory. A design approach is developed in which gains can be separately calculated to stabilize reduced-order slow and fast subsystem models. By employing the notion of combined control and observation spillover suppression, conditions are derived assuring that these gains will stabilize the full-order system, assuming sufficient frequency separation between the slow and fast subsystems. An LQ design procedure is described in which the spillover suppression conditions are satisfied by adjoining penalty functions to the subsystem performance indices. The theory is demonstrated in a controller design for a flexible space structure.

A self-repairing flight control system concept in which the control law is reconfigured after actuator and/or control surface damage to preserve stability and pilot command tracking is described. A key feature of the controller is reconfigurable multivariable feedback. The feedback gains are designed off-line and scheduled as a function of the aircraft control impairment status so that reconfiguration is performed simply by updating the gain schedule after detection of an impairment. A novel aspect of the gain schedule design procedure is that the schedule is calculated using a linear quadratic optimization-based simultaneous stabilization algorithm in which the scheduled gain is constrained to stabilize a collection of plant models representing the aircraft in various control failure modes. A description and numerical evaluation of a controller design for a model of a statically unstable high-performance aircraft are given.

This paper will describe a real time piloted simulation of algorithms designed for on-board computation of time-optimal intercept trajectories for an F-8 aircraft. The algorithms, which were derived using singular perturbation theory, generate commands that are displayed to the pilot on flight director needles on the 8-ball. By flying the airplane so as to zero the horizontal and vertical needles, the pilot flies an approximation to a time-optimal intercept trajectory. The various display and computation modes that are available will be described and results will be presented illustrating the performance of the algorithms with a pilot in the loop.

Issues pertaining to the well-posedness of a two time scale approach to the optimal output feedback regulator problem are examined. An approximate quadratic performance index is derived, reflecting a two time scale decomposition of the system dynamics. It is shown that, under mild assumptions, minimization of this cost leads to feedback gains providing a second-order approximation of the full system optimal performance. This is verified with a numerical example. A convergent sequential numerical algorithm for calculating these gains is described.