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This paper presents a runtime assurance (RTA) framework for an unmanned aerospace application. RTA systems hold the promise of protecting feedback systems that contain advanced or experimental elem...

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...

For more than a decade, researchers have been developing novel adaptive flight control systems that provide a means to safely operate an air vehicle in the presence of damage or failures. Although these systems show great promise, integration of exemplar systems in flight‐test aircraft at NASA has revealed the potential for unfavorable pilot–vehicle coupling, including pilot‐induced oscillations. To address this issue directly, the Smart Adaptive Flight Effective Cue system was developed that features an adaptive command path gain to mitigate oscillation tendencies and an inceptor force‐feedback cue to alert the pilot that the system is active. To evaluate the system, a representative adaptive controller that had been successfully evaluated in previous flight‐test programs and the Smart Adaptive Flight Effective Cue system were implemented in a Calspan Learjet in‐flight simulator. Flight‐test evaluations consisted of three test pilots conducting two sorties each to evaluate the effectiveness of the combin...

The human pilot is a crucial component of the pilot/vehicle system. This article focuses on the in-flight ability of an adaptive receding horizon optimal controller to specify favorable flying qualities. Moreover, in the case of a deteriorated vehicle, the controller serves to protect the desirable flying qualities by automatically compensating for the new aircraft characteristics. Although the receding horizon optimal controller is able to protect the handling qualities as long as adequate physical control authority exists, particularly severe failures can result in heavily limited authority where encounters with actuator rate limits may occur. The controller itself is robust to rate-limit encounters, however, the handling qualities suffer because the authority no longer exists to deliver the performance expected by the pilot. To protect handling qualities and mitigate the risk of pilot-induced oscillation past this exhaustion of control authority, the controller is integrated into the Smart Adaptive Fli...

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.

This report was developed under a SBIR contract.This report describes the technical progress made by Barron Associates, Inc. and its partners in runtime assurance (RTA) systems, which hold the promise of protecting advanced systems that cannot be fully certified at design time due to their inherent complexity. A number of technical hurdles remain in the implementation of RTA systems for highly complex safety-critical systems, and the main objective of this effort was to further address these issues. One main focus of this project was to investigate the necessary structure of RTA frameworks for multi-level interacting feedback systems. As such, a challenge problem was constructed for a fleet of unmanned aircraft systems (UASs) performing a surveillance mission. The demonstration platform consisted of RTA systems for the inner-loop control, outer-loop guidance, ownship flight management, and fleet mission planning elements. The framework design and certification requirements for such a system were explored in this program. For the inner-loop, the concept of employing multiple transition controllers in the reversionary control system was studied. For all feedback levels, the required RTA checks were developed and the critical reversionary switching conditions defined. The interactions between the RTA protected systems and certified collision avoidance systems were also investigated. A safety case argument for design-time certification of the RTA protected systems was constructed using subsystem requirements contracts that were developed from a compositional reasoning approach explored over the course of the project.

*‡ § ** For more than a decade, researchers have been developing novel adaptive flight control systems that provide a means to safely operate an air vehicle in the presence of damage or failures. While these systems show great promise, integration of exemplar systems in flight test aircraft at NASA have revealed the potential for unfavorable pilot-vehicle coupling including pilot-induced oscillations. To address this issue directly, the Smart Adaptive Flight Effective Cue or SAFE-Cue system was developed. The SAFE-Cue system features an adaptive command path gain to mitigate oscillation tendencies and an inceptor force feedback cue to alert the pilot that the system is active. To evaluate the system, a representative adaptive controller that had been successfully evaluated in previous flight test programs and the SAFE-Cue system were implemented in a Calspan Learjet In-Flight Simulator. Three test pilots conducted two sorties each to evaluate the effectiveness of the combined system in the presence of pitch and roll axis failures. The resulting pilot opinion ratings and quantitative assessments of performance indicated that the SAFE-Cue system successfully eliminated the oscillation tendencies that can lead to loss of control. Furthermore, the system restored a more linear vehicle response that allowed the pilots to successfully complete the evaluation task with performance that approaches that of the baseline healthy aircraft. While the SAFE-Cue system was integrated with an adaptive controller in this application, the system is completely general and can be applied to any flyby-wire design.

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.

To improve pilot situational awareness, the authors have designed an energy-state protection system to monitor the aircraft energy state and generate crew alerts through multiple modalities when the energy state deviates from a prescribed energy pro le. These alerts indicate whether the energy state of the aircraft is high or low as well as the magnitude of the deviation from the prescribed pro le. The system was evaluated for the approach ight phase in a xed-base ight simulator using ve ATP-rated test pilots. The overall ndings were: (1) the energy-state protection system improved energy tracking in a statistically signi cant manner without signi cantly impacting altitude, airspeed, or localizer tracking, (2) all test pilots found the energy-state protection system useful, and (3) the majority of test pilots found that the energy-state protection system reduced workload.

Loss of control has been identified as the leading cause of jet transport accidents. Recent high profile accidents have illustrated the need to address pilot response to upsets and unusual attitudes. Another issue no less important is that associated with pilot-vehicle system loss of control. This refers to those incidents that result from the pilot’s active manual control of the aircraft and most commonly occur in the form of so called pilot-induced oscillations. Regardless of the implied “fault” of the pilot, these events result from issues with the airframe and flight control system and not the pilot. Examples described in this paper include events with modern fly-by-wire transports in commercial operation and flight test events associated with the evaluation of new adaptive control schemes under various failure conditions. While not always the root cause, flight control system nonlinearities such as control surface rate and position limits are key components in these events. To mitigate loss of control events of this type, force feedback via an active control inceptor and corresponding command path gain adjustments have been investigated using piloted simulation and flight test evaluations. This paper describes the development of effective pilot cueing techniques that make up the Smart-Cue/Smart-Gain system that addresses control surface actuator rate limiting and the Smart Adaptive Flight Effective Cue or SAFE-Cue that addresses more general flight control system nonlinear behavior, particularly that behavior associated with adaptive controllers.

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?)

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.

As a means to enhance aviation safety, numerous adaptive control techniques have been developed to maintain aircraft stability and performance in the presence of failures or damage. The techniques apply a wide array of adaptations from simple gain scheduling to online learning algorithms. While the ready availability of low cost, reduced scale unmanned systems have allowed for many successful flight test demonstrations, applications to piloted aircraft have been more limited. Flight evaluations of various adaptive control applications conducted by NASA and others have shown great promise. In some cases, however, unfavorable pilot-vehicle interactions including pilot-induced oscillations have occurred. Susceptibility to such interactions is more likely when the pilot interacts with a highly nonlinear vehicle that may no longer have predictable response characteristics due to the intervention of the adaptive controller and/or presence of flight control system nonlinearities such as actuator rate and position limits. To address the challenge of adverse pilot interactions with an adaptive control system, the Smart Adaptive Flight Effective Cue or SAFE-Cue system is introduced. This innovative cueing system provides force feedback to the pilot via an active control inceptor with corresponding command path gain adjustments. The SAFE-Cue will alert the pilot that the adaptive control system is active, provide guidance via force feedback cues, and attenuate commands, thus ensuring pilot-vehicle system stability and performance in the presence of damage or failures. Piloted simulation results found that the SAFE-Cue system was effective at suppressing pilot-vehicle system oscillations for failure/damage scenarios, allowing the pilots to focus on the task at hand rather than simply maintaining control.

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.

This document describes a robustness enhancing modification to an existing joint state and parameter estimation technique as well as an intuitive and practical method to enhance on-line parameter identification by reducing correlations amongst the identification inputs. Results from the application of these ideas to a supercavitating torpedo model are included. The background of the robust joint estimation enabling technologies as well as their inte- gration is described first followed by an example demonstrating the enhanced robustness to noise and insucient excitation. Subsequently, the robust joint estimation approach is employed to provide state and parameter estimates to a nonlinear controller which gener- ates eector commands for the torpedo model. Next, the correlation reduction approach to enhance on-line parameter identification is introduced and analyzed followed by simulation results to demonstrate the ecacy.

substantially from the prescribed or expected characteristics. To accommodate failures, the baseline inner-loop control is assisted by a fault detection and isolation algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop guidance is augmented with an adaptive neural network forming an adaptive outer-loop guidance system. These adaptive subsystems have been formulated for, and applied to, a transport aircraft model in a nonlinear, high-fidelity aircraft simulation environment to demonstrate the concept. Included results demonstrate that the fault detection and isolation and adaptive outer-loop functionality consistently improve tracking performance during both piloted maneuvers and autonomous waypoint path following. In some cases, these retrofit subsystems enable tasks to be completed that were not previously feasible. Adaptive enhancement of the robustness of existing non-adaptive systems has been an area of significant interest for many years. Although application in the aircraft community is of great benefit for maximizing performance and safety for unexpected changes in the dynamics due to flight control failures, damage, and adverse environmental conditions, certification of these technologies for safety-critical flight systems is challenging. The approach described here is developed under the constraint of maintaining a retrofit architecture thereby allowing the baseline, previously certified, software to remain. The augmented modules serve to assist systems already in place. This paper discusses the development and application of independent and modular adaptive subsystems that are retrofitted to existing guidance and control architectures. The baseline inner-loop control is assisted by a fault detection and isolation (FDI) algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop (BOL) guidance is augmented with an adaptive neural network collectively forming an adaptive outer-loop (AOL) guidance system. In general, an adequate inner-loop feedback control law is necessary to faithfully track guidance commands, and various inner-loop control design techniques can provide the requisite performance. Even in the case of degraded inner-loop tracking capability, the outer-loop guidance system may still meet its objectives by inflating inner-loop commands to compensate for inner-loop tracking errors. Nonetheless, faithful innerloop tracking is desirable to reduce the burden on other subsystems, and this is especially the case for piloted

Autonomous path planning capability is a critical requirement for future unmanned vehicle operations. This paper presents a novel path planning approach for rapid construction of feasible, yet agile, trajectories. The approach relies upon separating the planning task into an on-line and an o-line component. The o-line component consists of building a library of motion primitives using nonlinear simulation, the primitives are then sequenced on-line using an A* search. The main benefit of this separation is that it shifts the computationally intensive work to the o-line component and leaves the on-line task relatively light. A major benefit of using A* is that the search algorithm is unaected by the changes to the library of trim primitives. Thus in the event that a UAVs capabilities are degraded, e.g. due to damage, any unavailable motion primitives are simply removed from the library and A* will not included them in the path construction. To speed up the on-line search time, a sub-optimal modification is made to the standard A* algorithm that delivers a feasible sub-optimal path quickly with relatively small increase in total path cost. Examples of waypoint planning and obstacle avoidance are given to illustrate the path planning approach.

This article presents an approach to control of the aerodynamic angles („; fi; fl) of an aircraft using backstepping control techniques and on-line function approximation. Backstepping allows the control signal to be constructed without separating the dynamics into slow and fast states, as has been done in the past, and also allows easy handling of unmatched uncertainties in the plant dynamics. On-line function approximators are used to learn the aircraft’s aerodynamic forces and moments in-∞ight and provide robustness to parametric plant uncertainties. These uncertainties can stem from various sources such as modelling errors introduced at design time, in-∞ight failures/damage, or even modelling mismatch caused by applying the controller to an aircraft with a difierent conflguration than which it was designed for. The advantages of this control approach are that it deals with the complete plant dynamics directly, provides bounded tracking in the face of signiflcant nonlinear plant uncertainty, and that the functions learned on-line are components of the aerodynamic forces and moments and thus have physical meaning. Numerical simulation results from the application of this approach to a nonlinear tailless aircraft are also presented.