John Kaneshige

Flight Dynamics and Hybrid Adaptive Control of Damaged Aircraft

This paper presents a recent study to investigate flight dynamics and adaptive control methods for stability and control recovery of a damaged generic transport aircraft. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of the damaged aircraft. Flight dynamics for a general aircraft are developed to account for changes in aerodynamics, mass properties, and the center of gravity that can compromise the stability of the damaged aircraft An iterative trim analysis is developed to compute incremental trim states. A neural network hybrid direct-indirect adaptive flight control is developed for the stability augmentation control of the damaged aircraft. The proposed method performs an online estimation of damaged plant dynamics to improve the command tracking performance in conjunction with a direct adaptive controller. The plant estimation is based on two approaches: 1) an indirect adaptive law derived from the Lyapunov stability theory to ensure that the tracking error is bounded, and 2) a recursive least-squares method that minimizes the modeling error. Simulations show that the hybrid adaptive controller can provide a significant improvement in the tracking performance over a direct adaptive controller working alone.

Dynamics and Adaptive Control for Stability Recovery of Damaged Asymmetric Aircraft

This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in offnominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented: 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-inthe-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive flight control approach.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

A nonlinear aircraft model is presented and used to develop an overall unified approach to online trim and maneuverability envelope estimation with uncertainty quantification without any requirement for active input excitation. The concept of time scale separation makes this method suitable for the adaptive characterization of altered safe maneuvering limitations based on aircraft performance after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Safe maneuvering envelope estimation based on a physical approach

This paper discusses a computationally efficient algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. This approach differs from others since it is physically inspired. This more transparent approach allows interpreting data in each step, and it is assumed that these physical models based upon flight dynamics theory will therefore facilitate certification for future real life applications.

Piloted Simulator Evaluation of Maneuvering Envelope Information for Flight Crew Awareness

This paper discusses the implementation and evaluation of an efficient method for estimating safe aircraft maneuvering envelopes. A Bayesian approach is used to produce a deterministic algorithm for estimating aerodynamic system parameters from existing noisy sensor measurements, which are then used to estimate the trim envelope through efficient high-fidelity model-based computations of attainable equilibrium sets. The safe maneuverability limitations are extended beyond the trim envelope through a robust reachability analysis derived from an optimal control formulation. The trim and maneuvering envelope limits are then conveyed to pilots through three axes on the primary flight display. These display features were evaluated in the Advanced Concepts Flight Simulator at NASA Ames Research Center, as part of a larger research initiative, to investigate the impact on aircraft energy state awareness of the crew. Commercial airline crews flew multiple challenging approach and landing scenarios in a relevant environment. Results show that the additional display features have the potential to significantly improve cautiousness of the flight crew.

Implementation and Evaluation of Multiple Adaptive Control Technologies for a Generic Transport Aircraft Simulation

Presented here is the evaluation of multiple adaptive control technologies for a generic transport aircraft simulation. For this study, seven model reference adaptive control (MRAC) based technologies were considered. Each technology was integrated into an identical dynamic-inversion control architecture and tuned using a methodology based on metrics and specific design requirements. Simulation tests were then performed to evaluate each technology’s sensitivity to time-delay, flight condition, model uncertainty, and artificially induced cross-coupling. The resulting robustness and performance characteristics were used to identify potential strengths, weaknesses, and integration challenges of the individual adaptive control technologies. I. Introduction HE Integrated Resilient Aircraft Control (IRAC) project is a part of the Aviation Safety Program under the Aeronautics Research Mission Directorate (ARMD) at NASA. A key focus of this project is to research the use of adaptive control technologies as a risk-mitigating tool for off-nominal aircraft flight. In a traditional gainscheduled design approach, the flight controller is designed by treating the aircraft’s flight envelope as a discrete space. Controls engineers then use traditional linear control techniques to shape the handling qualities of the aircraft at each of these discrete locations. In an off-nominal scenario, this design approach may breakdown as a result of the

An Integrated Vehicle Modeling Environment

This paper describes an Integrated Vehicle Modeling Environment for estimating aircraft geometric, inertial, and aerodynamic characteristics, and for interfacing with a high fidelity, workstation based flight simulation architecture. The goals in developing this environment are to aid in the design of next generation intelligent fight control technologies, conduct research in advanced vehicle interface concepts for autonomous and semi-autonomous applications, and provide a value-added capability to the conceptual design and aircraft synthesis process. Results are presented for three aircraft by comparing estimates generated by the Integrated Vehicle Modeling Environment with known characteristics of each vehicle under consideration. The three aircraft are a modified F-15 with moveable canards attached to the airframe, a mid-sized, twin-engine commercial transport concept, and a small, single-engine, uninhabited aerial vehicle. Estimated physical properties and dynamic characteristics are correlated with those known for each aircraft over a large portion of the flight envelope of interest. These results represent the completion of a critical step toward meeting the stated goals for developing this modeling environment.

Intelligent mission management for uninhabited aerial vehicles

The National Aeronautics and Space Administration (NASA), Aeronautics Research Mission Directorate, is developing Intelligent Mission Management (IMM) technology for Uninhabited Aerial Vehicles (UAV’s) under the Vehicle Systems Program’s Autonomous Robust Avionics Project. The objective of the project is to develop air vehicle and associated ground element technology to enhance mission success by increasing mission return and reducing mission risk. Unanticipated science targets, uncertain conditions and changing mission requirements can all influence a flight plan and may require human intervention during the flight; however, time delays and communications bandwidth limit opportunities for operator intervention. To meet these challenges, we will develop UAV-specific technologies enabling goal-directed autonomy, i.e. the ability to redirect the flight in response to current conditions and the current goals of the flight. Our approach divides goal-directed autonomy into two components, an on-board Intelligent Agent Architecture (IAA) and a ground based Collaborative Decision Environment (CDE). These technologies cut across all aspects of a UAV system, including the payload, inner- and outer-loop onboard control, and the operator’s ground station.

Implementation of a Trajectory Prediction Function for Trajectory Based Operations

This paper describes the implementation and evaluation of a trajectory prediction function. This function is a critical component of tactical flight management, a new concept that can increase the resiliency and robustness of trajectory based operations through a paradigm shift that improves Flight Management System (FMS) compatibility with tactical operations. The trajectory prediction function generates and continually updates the fourdimensional flight path that will be flown by the FMS. This motion-based trajectory represents an extension of the aircraft’s current state, and incorporates control laws, mode transition logic, and drag estimation as part of the prediction. The predicted trajectory is then displayed on navigation and vertical situation displays in an effort to reduce mode confusion occurrences and increase situational awareness of what the automation is doing now and what it will do in the future. These display features were evaluated in the Advanced Concepts Flight Simulator at NASA Ames Research Center to investigate the impact on flight crew energy state awareness when operating in the highly constrained and dynamic environment of the Next Generation Air Transportation System. Commercial airline crews flew multiple optimized profile descents under two conditions. In one condition, crews were presented with standard navigation displays, including a Vertical Situation Display (VSD). In the second condition, trajectory predictions were added to both the lateral map display and the VSD. Results show that predictive trajectory displays have the potential to improve situational awareness of the future automation mode and energy state of the aircraft, and that prediction accuracy and computational times are sufficient to support more advanced use in tactical flight management.

Enhancements to a Neural Adaptive Flight Control System for a Modified F-15 Aircraft

†This paper presents enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This is a follow-on effort to flight tests performed on the NASA F-15 aircraft, as part of the Intelligent Flight Control System research effort. Previous results demonstrated the potential for improving performance under simulated damage conditions. However, little improvement was provided under simulated control surface failures, and the adaptive system tended to be prone to pilot induced oscillations. This paper presents an analysis of the previous flight tests and proposes an alternate input selection criterion, a technique for improving robustness through normalized learning rates, and a method for adaptively retrofitting a classical yaw damping controller. Simulation results demonstrate significant improvement in performance and robustness over the neural network implementation used in the previous flight tests.