Christine M. Belcastro

AirSTAR: A UAV Platform for Flight Dynamics and Control System Testing

As part of the NASA Aviation Safety Program at Langley Research Center, a dynamically scaled unmanned aerial vehicle (UAV) and associated ground based control system are being developed to investigate dynamics modeling and control of large transport vehicles in upset conditions. The UAV is a 5.5% (seven foot wingspan), twin turbine, generic transport aircraft with a sophisticated instrumentation and telemetry package. A ground based, real-time control system is located inside an operations vehicle for the research pilot and associated support personnel. The telemetry system supports over 70 channels of data plus video for the downlink and 30 channels for the control uplink. Data rates are in excess of 200 Hz. Dynamic scaling of the UAV, which includes dimensional, weight, inertial, actuation, and control system scaling, is required so that the sub-scale vehicle will realistically simulate the flight characteristics of the full-scale aircraft. This testbed will be utilized to validate modeling methods, flight dynamics characteristics, and control system designs for large transport aircraft, with the end goal being the development of technologies to reduce the fatal accident rate due to loss-of-control.

Aircraft Loss-of-Control Accident Analysis

Loss of control remains one of the largest contributors to fatal aircraft accidents worldwide. Aircraft loss-of-control accidents are complex in that they can result from numerous causal and contributing factors acting alone or (more often) in combination. Hence, there is no single intervention strategy to prevent these accidents. To gain a better understanding into aircraft loss-of-control events and possible intervention strategies, this paper presents a detailed analysis of loss-of-control accident data (predominantly from Part 121), including worst case combinations of causal and contributing factors and their sequencing. Future potential risks are also considered.

Performance analysis on fault tolerant control system

In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured according to fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. In this brief, an FTC analysis framework is provided to calculate the upper bound of an induced-L2 norm of an FTC system in the presence of false identification and detection time delay. The upper bound is written as a function of a duration time interval and exponential decay rates and has been used to determine which FTC law produces less performance degradation (tracking error) due to false identification. The analysis framework is applied for an FTC system of a highly maneuverable aircraft technology (HiMAT) vehicle

Adaptive Linear Parameter Varying Control Synthesis for Actuator Failure

A robust linear parameter varying (LPV) control synthesis is carried out for a Highly Maneuverable Aircraft Technology (HiMAT) vehicle subject to loss of control effectiveness. The scheduling parameter is selected to be a function of the estimates of the control effectiveness factors. The estimates are provided online by a two-stage adaptive Kalman filter estimator. The inherent conservatism of the LPV design is reduced through the use of a scaling factor on the uncertainty block that represents the estimation errors of the effectiveness factors. Simulations of the controlled system with the online estimator show that a superior fault tolerance can be achieved.

Aircraft Accident Prevention: Loss-of-Control Analysis

The majority of fatal aircraft accidents are associated with ‘loss-of-control’. Yet the notion of loss-of-control is not well-deflned in terms suitable for rigorous control systems analysis. Loss-of-control is generally associated with ∞ight outside of the normal ∞ight envelope, with nonlinear in∞uences, and with an inability of the pilot to control the aircraft. The two primary sources of nonlinearity are the intrinsic nonlinear dynamics of the aircraft and the state and control constraints within which the aircraft must operate. In this paper we examine how these nonlinearities afiect the ability to control the aircraft and how they may contribute to loss-of-control. Examples are provided using NASA’s Generic Transport Model.

Application of failure detection, identification, and accommodation methods for improved aircraft safety

This paper provides an overview of technologies being developed under NASAs Aviation Safety Program to reduce aircraft accidents due to vehicle loss of control and system failures.

Parametric uncertainty modeling: an overview

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. This paper provides an overview of the parametric uncertainty modeling process for nonlinear parameter-dependent systems.

Preliminary Analysis of Aircraft Loss of Control Accidents: Worst Case Precursor Combinations and Temporal Sequencing

Aircraft loss of control (LOC) is a leading cause of fatal accidents across all transport airplane and operational classes, and can result from a wide spectrum of hazards, often occurring in combination. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of conditions and uncertainties, including multiple hazards, and their validation must provide a means of assessing system effectiveness and coverage of these hazards. This requires the definition of a comprehensive set of LOC test scenarios based on accident and incident data as well as future risks. This paper defines a comprehensive set of accidents and incidents over a recent 15 year period, and presents preliminary analysis results to identify worst-case combinations of causal and contributing factors (i.e., accident precursors) and how they sequence in time. Such analyses can provide insight in developing effective solutions for LOC, and form the basis for developing test scenarios that can be used in evaluating them. Preliminary findings based on the results of this paper indicate that system failures or malfunctions, crew actions or inactions, vehicle impairment conditions, and vehicle upsets contributed the most to accidents and fatalities, followed by inclement weather or atmospheric disturbances and poor visibility. Follow-on research will include finalizing the analysis through a team consensus process, defining future risks, and developing a comprehensive set of test scenarios with correlation to the accidents, incidents, and future risks. Since enhanced engineering simulations are required for batch and piloted evaluations under realistic LOC precursor conditions, these test scenarios can also serve as a high-level requirement for defining the engineering simulation enhancements needed for generating them.

Closed-Loop Evaluation of An Integrated Failure Identification And Fault Tolerant Control System for A Transport Aircraft

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems developed for failure detection, identification, and reconfiguration, as well as upset recovery, need to be evaluated over broad regions of the flight envelope or under extreme flight conditions, and should include various sources of uncertainty. To apply formal robustness analysis, formulation of linear fractional transformation (LFT) models of complex parameter-dependent systems is required, which represent system uncertainty due to parameter uncertainty and actuator faults. This paper describes a detailed LFT model formulation procedure from the nonlinear model of a transport aircraft by using a preliminary LFT modeling software tool developed at the NASA Langley Research Center, which utilizes a matrix-based computational approach. The closed-loop system is evaluated over the entire flight envelope based on the generated LFT model which can cover nonlinear dynamics. The robustness analysis results of the closed-loop fault tolerant control system of a transport aircraft are presented. A reliable flight envelope (safe flight regime) is also calculated from the robust performance analysis results, over which the closed-loop system can achieve the desired performance of command tracking and failure detection.

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.