Neha Gandhi

Integrated Adaptive Guidance and Control for Re-Entry Vehicles with Flight Test Results

To enable autonomous operation of future reusable launch vehicles, reconfiguration technologies will be needed to facilitate mission recovery following a major anomalous event. The Air Force’s Integrated Adaptive Guidance and Control program developed such a system for Boeing’s X-40A, and the total in-flight simulator research aircraft was employed to flight test the algorithms during approach and landing. The inner loop employs a modelfollowing/dynamic-inversion approach with optimal control allocation to account for control-surface failures. Further, the reference-model bandwidth is reduced if the control authority in any one axis is depleted as a result of control effector saturation. A backstepping approach is utilized for the guidance law, with proportional feedback gains that adapt to changes in the reference model bandwidth. The trajectory-reshaping algorithm is known as the optimum-path-to-go methodology. Here, a trajectory database is precomputed off line to cover all variations under consideration. An efficient representation of this database is then interrogated in flight to rapidly find the “best” reshaped trajectory, based on the current state of the vehicle’s control capabilities. The main goal of the flight-test program was to demonstrate the benefits of integrating trajectory reshaping with the essential elements of control reconfiguration and guidance adaptation. The results indicate that for more severe, multiple control failures, control reconfiguration, guidance adaptation, and trajectory reshaping are all needed to recover the mission.

Intelligent Control of a Morphing Aircraft

A morphing aircraft is able to drastically alter its planform to optimize performance at very dissimilar flight conditions. Despite significant strides to develop wing structure and actuation systems, much work remains to effectively control both the morphing wing as well as the entire morphing aircraft. The control solution presented in this paper uses modelbased methods that provide precise, closed-loop control of the morphing planform (i.e. wing-shape control) and simultaneously enforce prescribed closed-loop aircraft dynamics (i.e. flight control). The specific planform that is the focus of this research is the N-MAS wing designed by NextGen Aeronautics. At the wing-shape control level, the authors sought to answer two questions: (1) What is the most efficient means of actuating the underlying structure of the N-MAS wing? and (2) Given a fixed set of actuators, how does one precisely manipulate a morphing structure given inherent physical limitations? At the flight-control level, the authors sought to develop a control methodology that can: (1) accommodate different planforms that result in drastically changing plant dynamics, and (2) make the transition between any two configurations while maintaining the stability of the morphing aircraft.

Hybrid Robust Control and Reinforcement Learning for Optimal Upset Recovery

Air safety and flight asset protection benefit greatly from rapid upset recovery. Autonomous recovery is of particular interest due to a recent significant increase in fielded Unmanned Aerial Vehicles (UAVs). Autonomous recovery challenges include complex nonlinear dynamics and large variation in potential upset conditions. A novel UAV upset recovery system is developed that combines the benefits of robust control with the benefits of intelligent learning techniques. Off-line, Reinforcement Learning (RL) techniques are applied to simulation data to discover recovery strategies that improve upon known strategies. When learning is complete, the strategies are provided to an online component. In the event of an upset, the online component is interrogated to determine the best control decision at each control update until the recovery is complete. The online component is designed to easily make use of the best-known recovery strategies, taking advantage of improved strategies as learning matures. The system architecture is partitioned into two components; one which focuses on recovery from high angular rate upsets and another which focuses on recovery from unusual attitude upsets. The input and output sets for both partitions are compact by design to reduce complexity, thereby ensuring the applicability of RL techniques. Two simulation variants of NASA’s Generic Transport Model (GTM) are used, one for training and initial evaluation and another for robustness testing. The results indicate that the learning process frequently finds improvements to best-known strategies, and that learned recovery strategies are robust to uncertainty.

Flight Test Results of an Adaptive Guidance System for Reusable Launch Vehicles

For next generation Reusable Launch Vehicles (RLVs), reconfigurable control, adaptive guidance, and on-line trajectory-command reshaping will often be required to recover the mission in the face of a major anomalous event such as an effector failure. An adaptive guidance system that works in conjunction with a reconfigurable controller and an autonomous trajectory command reshaping algorithm is presented. The guidance law utilizes a backstepping architecture to generate pitch rate commands that drive the inner-loop control system. Under extreme failure conditions the control surfaces can saturate in an attempt to meet commanded moments. In these cases, the guidance feedback gains are reduced to preserve stability margins in the guidance loops. In addition, simulation and flight test results of the complete reconfigurable control/adaptive guidance/trajectory reshaping system are presented for a simulated X-40A RLV. The Total In- Flight Simulator research aircraft was utilized to flight test the X-40A system under a variety of failure conditions. This work was completed in conjunction with the Air Force Research Laboratorys Integrated Adaptive Guidance & Control (IAG&C) program. Both simulation and flight test results indicate the major benefits of the new system. With on-line trajectory reshaping, the vehicle is able to achieve a safe touchdown, whereas the vehicle is lost without trajectory reshaping.

Desktop Simulator Demonstration of a Joint Human/Automated Upset Recovery System

reduce loss of life. This paper presents the development and initial testing of a joint human/automated (H/A) recovery system intended to assist the crew with recovery from upset conditions. The goal of the system is to keep pilots in the loop, leveraging their expertise while simultaneously conveying information about recovery procedures in an intuitive and unobtrusive manner. This research builds on recent work by the authors targeted at autonomous upset recovery for unmanned aerial vehicles. The authors have developed a number of crew-specic extensions to this automated system at both the architecture and interface levels. The resulting system optimizes recovery strategies oine, stores the recovery procedures in a compact manner that can easily be queried in real-time online, and communicates the procedure to the pilot through visual and haptic cues. The system was evaluated in a small-scale pilot-in-the-loop study. Three pilots with dierent backgrounds as well as dierent levels of experience were recruited to take part in the pilot-in-the-loop experiments. Metrics were dened to evaluate performance both in terms of quantitative recovery metrics (e.g. how fast did the vehicle recover nominal ight?)

Simulator Evaluation of an In-Cockpit Cueing System for Upset Recovery

For manned aircraft, loss of control in flight (LOC-I) is one of the main causes of aviation fatalities; new technologies that help to reduce LOC-I thus have the potential to significantly reduce loss of life. This paper presents refinements and expanded testing of an in-cockpit cueing system intended to assist the crew with recovery from upset conditions. The goal of the system is to keep pilots in the loop, leveraging their expertise while simultaneously conveying information about recovery procedures in an intuitive and unobtrusive manner. The system optimizes recovery strategies offline, stores the recovery procedures in a compact manner that can easily be queried in real-time online, and communicates the procedure to the pilot through visual and haptic cues. Eleven pilots with a type rating in at least one large commercial transport aircraft, regional jet/turbo-prop were recruited to evaluate the system. The overall findings were: (1) pilots were willing to follow strategies provided by the in-cockpit cueing system, (2) following strategies provided by the in-cockpit cueing system results in a final aircraft state closer to straight and level flight at the target airspeed, (3) following strategies provided by the in-cockpit cueing system significantly reduces the likelihood that the pilot will exceed structural limits, (4) following strategies provided by the in-cockpit cueing system significantly reduces excursions from the nominal angle of attack envelope during the recovery, and (5) following strategies provided by the in-cockpit cueing system significantly reduces pilot workload.

Intelligent Guidance and Trajectory Command Systems for Autonomous Space Vehicles

This effort represents continued developments of an integrated reconfigurable control, adaptive guidance, and onboard trajectory command reshaping system that was successfully flight tested in 2003. The purpose of these advanced algorithms is to recover the mission in the face of severe off-nominal conditions and control effector failures. In the flight test program, the system was developed for the missions final flight phase known as approach and landing. The current effort is furthering the technology with application to other flight phases such as re-entry and Terminal Area Energy Management (TAEM). The guidance law utilizes a backstepping architecture to generate attitude rate commands that drive the inner-loop control system. Under certain control surface failure conditions, the bandwidth of the inner-loop control system is purposely reduced to lessen the commanded moments. In these cases, the guidance feedback gains are adapted on-line to preserve stability margins in the guidance loops in the face of degraded maneuvering capabilities. During the course of the flight test program, it was shown that failure scenarios that significantly alter the energy management of the vehicle will require the commanded trajectory to be reshaped in order to achieve an acceptable touchdown - even with reconfigurable/adaptive control and guidance. The onboard trajectory reshaping algorithm is known as the Optimum-Path- To-Go (OPTG) approach. OPTG results from the flight test program will be reviewed. However, new results of trajectory command reshaping during the TAEM guidance flight phase will also be presented. In this flight phase, the altitude and velocity must be brought to acceptable values at the start of the final approach. Further, the Heading Alignment Cone, or HAC turn, is flown to align the vehicles heading with the runway centerline. It will be shown that the OPTG algorithm can successfully reshape the HAC turn due to significant changes in the vehicles lift and drag. These changes may come about due to a control effector failure or significant head or tail winds. It will be shown that the mission is able to achieve an acceptable TAEM/final approach interface with trajectory reshaping.

Improved Upset Recovery Strategies Through Explicit Consideration of Pilot Dynamic Behavior

The human pilot is a crucial component of the Pilot/Vehicle System (PVS) and many researchers have recognized that the dynamic behavior of the human should be explicitly considered when analyzing human-in-the-loop systems. This paper focuses on human behavior modeling during upset recovery and how that pilot model is included in the pilotvehicle-system to determine and evaluate recovery techniques. The research discussed herein examines and models experimentally observed (in a simulator) pilot behavior, examines the role of the pilot model in the PVS, and uses the composite PVS model to determine recovery sequences that accommodate the dynamic behavior of the pilot/vehicle couple. Subsequently, the pilot models are updated to estimate how a pilot might behave under distress and new recovery sequences are extracted to accommodate the behavior of the distressed pilot. Simulation results show that explicit consideration of pilot dynamics, particularly the dynamics of the distressed pilot, in the generation of recovery sequences leads to more desirable responses of the PVS.

Vehicle Upset Detection and Recovery for Onboard Guidance and Control

This paper discusses the development and testing of an upset recovery architecture that is applicable for both piloted and autonomous recoveries. The architecture was first developed for unmanned vehicles and intended for fully automated implementation. The architecture was extended for use in manned aircraft and, in particular, for situations in which recoveries are being manually flown by a pilot. This extension required development of display technology for presenting recommended recovery guidance to the pilot as well as modification of recovery strategies to make them suitable for execution by a human pilot. Most recently, the architecture has been extended to accommodate off-nominal vehicle dynamics (e.g., due to actuator failures) and has been structured specifically to facilitate implementation without modification and recertification of existing flight control software. The approaches have been tested in multiple pilot-in-the-loop simulation experiments, which have shown both favorable pilot opini...

Development and Pilot-In-The-Loop Evaluation of Robust Upset-Recovery Guidance

Aircraft Loss-Of-Control (LOC) has been a longstanding contributor to fatal aviation accidents. The research presented herein is structured to directly address several known contributing and causal factors associated with vehicle upset and LOC. This paper discusses the development and evaluation of an approach to improve flight safety by visually providing closed-loop guidance for upset recovery that is robust to pilot behavior variation and is able to accommodate vehicle failures and impairment. The Damage Adaptive Guidance for piloted Upset Recovery (DAGUR) system provides continuous closed-loop recovery guidance via visual cues to reduce instances of inappropriate pilot reaction and pilot inaction. Adaptation enables the recovery module to provide appropriate guidance even in cases of vehicle damage or impairment. The recovery guidance system is also specifically designed to be robust to variations in pilot dynamic behavior (including behavior associated with high-stress situations). The adaptive recovery guidance is implemented “upstream” of the pilot and provided via visual cues; therefore it does not require modifications to existing flight control software (for fly-by-wire aircraft) and is equally applicable to non-fly-by-wire aircraft. Included desktop simulation and pilot-in-the-loop evaluation results show that the upset recovery guidance system is able to provide effective guidance for recovery from a variety of post-stall and unusual attitude upsets including cases of hardover control surface failures and that the recovery guidance is robust to large variations in pilot dynamic behavior. Additionally, pilots who evaluated the system indicated that they found the guidance to be useful and intuitive, and that it provided timely and measured recovery guidance. Quantitatively, the pilot-in-the-loop evaluation revealed that the recovery guidance significantly reduced subject pilot inceptor frequency content magnitude (energy) and the associated vehicle response.