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Advances in sensors and avionics computation power suggest real-time structural load measurements could be used in flight control systems for improved safety and performance. A conventional transport flight control system determines the moments necessary to meet the pilot’s command while rejecting disturbances and maintaining stability of the aircraft. Control allocation is the problem of converting these desired moments into control effector commands. In this paper, a framework is proposed to incorporate real-time structural load feedback and structural load constraints in the control allocator. Constrained optimal control allocation can be used to achieve desired moments without exceeding specified limits on monitored load points. Furthermore, certain criteria can be minimized, such as loads on certain parts of the aircraft. Flight safety issues can be addressed by using system health monitoring information to change control allocation constraints during flight. The framework to incorporate structural l...

This paper presents a Takagi–Sugeno fuzzy model-based approach to modeling, controlling, and stabilizing an aircraft in both normal and damaged conditions. The major contributions of this paper are as follows. First, it introduces the Takagi–Sugeno modeling and the parallel distributed compensation control approach. Then, it demonstrates how the approach can be applied to aircraft in a systematic manner. Finally, the approach is applied to the modified NASA F-15 number 837 nonlinear model, and preliminary simulation results are presented. The benefits of using a Takagi–Sugeno model-based parallel distributed compensation control approach include a systematic controller design procedure, mathematical proof of stability for classes of failures, and intuitive insight into damage effects on an aircraft. This work is the first to present this control approach as applied to aircraft.

The performance of nonlinear flight-control algorithms, such as feedback linearization and dynamic inversion, is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the aircraft dynamics results in reduced performance and may lead to instability. A self-organizing parametrization structure is developed to augment the baseline dynamic inversion controller for a high-performance military aircraft. This algorithm is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. The controller is simulated for different situations, including control surface failures, modeling errors, and external disturbances. A performance measure of maximum tracking error is specified for the controllers a priori. Excellent tracking error minimization to a prespecified level using the adaptive component is achieved. The performance of the self-organizing radial-basis-function network-based controller is also compared with a fixed radial-basis-function network-based adaptive controller. While the fixed radial-basis-function network-based controller, which is tuned to compensate for control surface failures, fails to achieve the same performance under modeling uncertainty and disturbances, the self-organizing radial-basis-function network is able to achieve good tracking convergence under all specified error conditions.

Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the fight body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

A large, civilian, multiengine transport MD-11 airplane control system was recently modie ed to perform as an emergency backup controller using engine thrust only. The emergency backup system, referred to as the propulsion-controlled aircraft (PCA)system, would be used if a majorprimary e ight control system fails. To allow for longitudinal- and lateral-directional control, the PCA system requires at least two engines and is implemented through software modie cations. A e ight-test program was conducted to evaluate the PCA system high-altitude e ying characteristics and to demonstrate its capacity to perform safe landings. The cruise e ight conditions, several low approaches, and four landings without any aerodynamic e ight control surface movement were demonstrated; however, only one landing is presented. Results that show satisfactory performance of the PCA system in the longitudinal axis are presented. Test results indicate that the lateral-directional axis of the system performed well at high altitude but was sluggish and prone to thermal upsets during landing approaches. Flight-test experiences and test techniques are also discussed, with emphasis on the lateral-directional axis because of the dife culties encountered in e ight test.

Two X-29A aircraft were flown at the NASA Dryden Flight Research Facility over a period of eight years. The airplanes' unique features are the forward-swept wing, variable incidence close-coupled canard and highly relaxed longitudinal static stability (up to 35% negative static margin at subsonic conditions). This paper describes the primary flight control system and significant modifications made to this system, flight test techniques used during envelope expansion, and results for the low and high angle-of-attack programmes. Throughout the paper, lessons learned will be discussed to illustrate the problems associated with the implementation of complex flight control systems.

When designing control laws for systems with constraints on the tracking performance, control allocation methods can be utilized. Control allocation methods are used when there are more command inputs than controlled variables. Control allocators can be used to address surface saturation limits, structural load limits, drag reduction constraints, or actuator failures. Most transport aircraft have many actuated surfaces compared to the three control variables (such as angle of attack, roll rate, and angle of sideslip). To distribute the control effort among the redundant set of actuators, a fixed mixer approach or online control allocation techniques can be utilized. The benefit of an online allocator is that complex constraints can be considered in the design; however, an online control allocator has the disadvantage of not guaranteeing a surface schedule, which can then produce unacceptable loads on the aircraft. The load uncertainty and complexity has prevented some controller designs from using advanced online allocation techniques. This paper considers actuator redundancy management for a class of over-actuated systems with real-time structural load limits using linear quadratic tracking applied to the generic transport model (a twin-engine heavy civil transport aircraft). With the inclusion of static load constraints in the allocator, the concern of overstressing the structures should be minimized or even eliminated. The results include three test cases. The first test case shows what happens when load constraints are applied over six left- and right-wing locations, with comparison to the same roll input run without load constraints. Test case two shows what happens when a large commanded roll is executed with the same load constraints as those used in test case one; this run is intended to stress the loads allocator. Test case three shows the robustness of the linear quadratic augmented allocator system to uncertainties; a 35-percent change in the control effectiveness plant model will be shown, in which the controller is kept the same as in test cases one and two with six load constraints.

In the presence of large uncertainty, a controller needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. As the adaptive gain increases, the time delay margin for a standard model-reference adaptive control decreases, hence loss of robustness. Optimal control modification is a new adaptive control method developed recently to achieve fast adaptation with robustness. Its formulation is based on the minimization of the L2 norm of the tracking error, posed as an optimal control problem. Computer simulations as well as pilot-in-the-loop high-fidelity simulations in a motion-based flight simulator demonstrate the effectiveness of the new adaptive law. In this study, we extend the optimal control modification to include a covariance-like adjustment mechanism of a time-varying adaptive gain to prevent persistent learning which can reduce robustness. The covariance update law can also include a forgetting factor in a similar context as a standard recursive least-squares estimation algorithm. The covariance adaptive gain adjustment allows an initial large adaptive gain to be set arbitrarily and provides the ability to drive the adaptive gain to a lower value as the adaptation has achieved sufficiently the desired tracking performance. Alternatively, a normalized adaptive gain may be used to reduce adaptation when the amplitude of an input basis function becomes large. Flight control simulation results demonstrate that both approaches can achieve significant robustness as measured by the time delay margin. Furthermore, a recent flight test program of the optimal control modification with normalization on a NASA F-18 aircraft demonstrates the effectiveness of the adaptive law.

National Aeronautics and Space Administration (NASA) researchers have conducted a series of flight experiments designed to study the effects of varying levels of adaptive controller complexity on the performance and handling qualities of an aircraft under various simulated failure or damage conditions. A baseline, nonlinear dynamic inversion controller was augmented with three variations of a model reference adaptive control design. The simplest design consisted of a single adaptive parameter in each of the pitch and roll axes computed using a basic gradient-based update law. A second design was built upon the first by increasing the complexity of the update law. The third and most complex design added an additional adaptive parameter to each axis. Flight tests were conducted using NASA s Full-scale Advanced Systems Testbed, a highly modified F-18 aircraft that contains a research flight control system capable of housing advanced flight controls experiments. Each controller was evaluated against a suite of simulated failures and damage ranging from destabilization of the pitch and roll axes to significant coupling between the axes. Two pilots evaluated the three adaptive controllers as well as the non-adaptive baseline controller in a variety of dynamic maneuvers and precision flying tasks designed to uncover potential deficiencies in the handling qualities of the aircraft, and adverse interactions between the pilot and the adaptive controllers. The work was completed as part of the Integrated Resilient Aircraft Control Project under NASA s Aviation Safety Program.

Advances in sensors and avionics computation power suggest real-time structural load measurements could be used in flight control systems for improved safety and performance. A convention transport flight control system determines the moments necessary to meet the pilot’s command, while rejecting disturbances and maintaining stability of the aircraft. Control allocation is the problem of converting these desired moments into control effector commands. In this paper, a framework is proposed to incorporate real-time structural load feedback and structural load constraints in the control allocator. Constrained optimal control allocation can be used to achieve desired moments without exceeding specified limits on monitored load points. Furthermore, certain criteria can be minimized, such as loads on certain parts of the aircraft. Flight safety issues could be addressed by using system health monitoring information to change control allocation constraints during flight. The framework to incorporate structural loads in the flight control system and an optimal control allocation algorithm will be described and then demonstrated on a nonlinear simulation of a generic transport aircraft with flight dynamics and static structural loads.

This paper presents a new adaptive control approach using Chebyshev orthogonal polynomials as basis functions in a least-squares functional approximation. The use of orthogonal basis functions improves the function approximation significantly and enables better convergence of parameter estimates. Flight control simulations demonstrate the effectiveness of the proposed adaptive control approach.

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper describes flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unsealed structured singular values, scaled structured singular values, and conventional single-loop phase and gain margins. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron-command-feedback gain by ±20%. Minimum eigenvalues of the return difference matrix that bound the singular values are also presented. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

This paper presents an L(sub 1) adaptive control augmentation system design for multi-input multi-output nonlinear systems in the presence of unmatched uncertainties which may exhibit significant cross-coupling effects. A piecewise continuous adaptive law is adopted and extended for applicability to multi-input multi-output systems that explicitly compensates for dynamic cross-coupling. In addition, explicit use of high-fidelity actuator models are added to the L1 architecture to reduce uncertainties in the system. The L(sub 1) multi-input multi-output adaptive control architecture is applied to the X-29 lateral/directional dynamics and results are evaluated against a similar single-input single-output design approach.

Conventional aircraft generally employ mixing algorithms or lookup tables to determine control surface deflections needed to achieve moments commanded by the flight control system. Control allocation is the problem of converting desired moments into control effector commands. Next generation aircraft may have many multipurpose, redundant control surfaces, adding considerable complexity to the control allocation problem. These issues can be addressed with optimal control allocation. Most optimal control allocation algorithms have control surface position and rate constraints. However, these constraints are insufficient to ensure that the aircraft's structural load limits will not be exceeded by commanded surface deflections. In this paper, a framework is proposed to enable a flight control system with optimal control allocation to incorporate real-time structural load feedback and structural load constraints. A proof of concept simulation that demonstrates the framework in a simulation of a generic transport aircraft is presented.

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) was used for these algorithms. This airplane has been modified by the addition of canards and by changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals included demonstration of revolutionary control approaches that can efficiently optimize aircraft performance for both normal and failure conditions, and to advance neural-network-based flight control technology for new aerospace systems designs. Before the NF-15B IFCS airplane was certified for flight test, however, certain processes needed to be completed. This paper presents an overview of these processes, including a description of the initial adaptive controller concepts followed by a discussion of modeling formulation and performance testing. Upon design finalization, the next steps are: integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness.

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research 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 flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

This paper presents a stable discrete-time adaptive law that targets modeling errors in a direct adaptive control framework. The update law was developed in our previous work for the adaptive disturbance rejection application. The approach is based on the philosophy that without modeling errors, the original control design has been tuned to achieve the desired performance. The adaptive control should, therefore, work towards getting this performance even in the face of modeling uncertainties/errors. In this work, the baseline controller uses dynamic inversion with proportional-integral augmentation. Dynamic inversion is carried out using the assumed system model. On-line adaptation of this control law is achieved by providing a parameterized augmentation signal to the dynamic inversion block. The parameters of this augmentation signal are updated to achieve the nominal desired error dynamics. Contrary to the typical Lyapunov-based adaptive approaches that guarantee only stability, the current approach investigates conditions for stability as well as performance. A high-fidelity F-15 model is used to illustrate the overall approach.

†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.

A fault tolerant neurally augmented control scheme based on non-linear dynamic inversion is designed for the WVUL YF-22 aircraft model. The parameters of the model following adaptive flight controller are determined at a single flight condition and a neural network is used to compensate for inversion errors and changes in aircraft dynamics, including actuator failures. Three different neural networks are used: the Extended Minimal Resource Allocating Network, the Single Hidden Layer Network, and the Sigma Pi. Numerical simulations are performed at nominal flight conditions and failure conditions affecting the stabilator or the aileron. Performance assessment parameters are defined based on the angular rate tracking errors. The performance of the three neural networks is compared in terms of these parameters.

This paper proposes a methodology for the task of comparing the performance of flight control laws using flight data recorded under non-homogeneous atmospheric conditions and dissimilar pilot input. Small differences in atmospheric conditions can potentially impact significantly the evaluation of the controller performance and prevent consistent comparison especially in the case of reduced size aircraft (autonomous or remotely piloted). Consistent deterministic control inputs can only be guaranteed by some form of on-board excitation system. An algorithm is developed for flight data reduction to account for such dissimilarities. The method presented in this paper is developed for the specific purpose of comparing model following control laws based on non-linear dynamic inversion with neural network augmentation. Evaluation parameters based on angular rate tracking errors are defined and used for the comparison. The procedure is documented using flight data from the WVU YF22 research aircraft model - a platform for testing fault tolerant control laws.

This paper presents a neural network based adaptive controller with application to a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Radial Basis Function Network (RBFN) that minimizes the inversion error which may occur due to imperfect modeling, approximate inversion, or sudden changes in aircraft dynamics such as failure of control surfaces. The RBFN is trained online using a simple growing and pruning algorithm in which hidden layer neurons are added or pruned based on the input to the network. The controller is simulated for 2 types of control surface failures with and without the adaptive neural network. The comparative results presented show the ability of the adaptive controller to compensate for errors arising due to the specified control surface failures.

Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

Modern exploration missions require modern control systems that can handle catastrophic changes in behavior, compensate for slow deterioration in sustained operations, and support fast system identification. The dynamics and control of new vehicles remains a significant technical challenge. Neural network based adaptive controllers have these capabilities, but they can only be used safely if proper Verification and Validation can be done. Due to the nonlinear and dynamic nature of an adaptive control system, traditional Verification and Validation (V&V) and certification techniques are not sufficient for adaptive controllers, which is a big barrier in their deployment in the safety-critical applications. Moreover, traditional methods of V&V involve testing under various conditions which is costly to run and requires scheduling a long time in advance. We have developed specific techniques, tools, and processes to perform design time analysis, verification and validation, and dynamic monitoring of such controllers. Combined with advanced modelling tools, an integrated development or deployment methodology for addressing complex control needs in a safety- and reliability-critical mission environment can be provided.

For a research aircraft, "conventional" control laws (CLs) are implemented on a "baseline" flight computer (FC) while research CLs are typically housed on a dedicated research computer. Therefore, for an experimental aircraft used to test specific fault tolerant flight control systems, a safety logic scheme is needed to ensure a safe transition from conventional to research CLs (while at nominal conditions) as well as from research CLs at nominal conditions to conditions with "simulated" failures on specific control surfaces. This paper describes the design of such a safety scheme for the NASA Intelligent Flight Control System (IFCS) F-15 Program. The goals of the IFCS F-15 program are to investigate the performance of a set of fault tolerant CLs based on the use of dynamic inversion with neural augmentation. The different transitions are monitored using information relative to flight conditions and controller-related performance criteria. The testing of the scheme is performed with a Simulink-based flight simulation code and interface developed at West Virginia University for the NASA IFCS F-15 aircraft.

A reconfigurable control law for the full X-33 flight envelope has been designed to accommodate a failed control surface and redistribute the control effort among the remaining working surfaces to retain satisfactory stability and performance. An offline nonlinear constrained optimization approach has been used for the X-33 reconfigurable control design method. Using a nonlinear, six-degree-of-freedom simulation, three example failures are evaluated: ascent with a left body flap jammed at maximum deflection; entry with a right inboard elevon jammed at maximum deflection; and landing with a left rudder jammed at maximum deflection. Failure detection and identification are accomplished in the actuator controller. Failure response comparisons between the nominal control mixer and the reconfigurable control subsystem (mixer) show the benefits of reconfiguration. Single aerosurface jamming failures are considered. The cases evaluated are representative of the study conducted to prove the adequate and safe performance of the reconfigurable control mixer throughout the full flight envelope. The X-33 flight control system incorporates reconfigurable flight control in the existing baseline system.

Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the right body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

An emergency flight control system using only engine thrust, called Propulsion-Controlled Aircraft (PCA), has been developed and flight tested on an MD-11 airplane. In this thrust-only control system, pilot flight path and track commands and aircraft feedback parameters are used to control the throttles. The PCA system was installed on the MD-11 airplane using software modifications to existing computers. Flight test results show that the PCA system can be used to fly to an airport and safely land a transport airplane with an inoperative flight control system. In up-and-away operation, the PCA system served as an acceptable autopilot capable of extended flight over a range of speeds and altitudes. The PCA approaches, go-arounds, and three landings without the use of any non-nal flight controls have been demonstrated, including instrument landing system-coupled hands-off landings. The PCA operation was used to recover from an upset condition. In addition, PCA was tested at altitude with all three hydraulic systems turned off. This paper reviews the principles of throttles-only flight control; describes the MD-11 airplane and systems; and discusses PCA system development, operation, flight testing, and pilot comments.

This report describes how an MD-11 airplane landed using only thrust modulation, with the control surfaces locked. The propulsion-controlled aircraft system would be used if the aircraft suffered a major primary flight control system failure and lost most or all the hydraulics. The longitudinal and lateral–directional controllers were designed and flight tested, but only the longitudinal control of flightpath angle is addressed in this paper. A flight-test program was conducted to evaluate the aircraft’s high-altitude flying characteristics and to demonstrate its capacity to perform safe landings. In addition, over 50 low approaches and three landings without the movement of any aerodynamic control surfaces were performed. The longitudinal control modes include a wing engines only mode for flightpath control and a three-engine operation mode with speed control and dynamic control of the flightpath angle using the tail engine. These modes were flown in either a pilot-commanded mode or an instrument landing system coupled mode. Also included are the results of an analytical study of an autothrottle longitudinal controller designed to improve the phugoid damping. This mode requires the pilot to use differential throttles for lateral control. Nomenclature Alon longitudinal state derivative matrix Blon control input derivative matrix c.g. center of gravity *Aerospace Engineer. †Chief, Propulsion Branch. Associate Fellow AIAA. ‡Flight Control Engineer. Copyright  1996 by the American Institute of Aeronautics and Astronautics, Inc. No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. 1 American Institute of Aero Clon state output matrix Dlon control input observation matrix EPR engine pressure ratio (turbine and inlet total pressures) FADEC full-authority digital engine control computers FCC flight control computer FCP flight control panel sink rate, ft/sec ILS instrument landing system flightpath error feed-forward gain, deg pitch integrator error gain, 1/sec pitch rate feedback gain, deg/deg/sec velocity error feedback gain, deg/kn pitch angle feedback gain, deg/deg/sec center engine washout gain, lb MCDU multifunction control and display unit PCA propulsion-controlled aircraft PIO pilot induced oscillation q pitch rate, deg/sec t time, sec uu x axis velocity perturbation, ft/sec Vel velocity or airspeed, kn s Laplace transform ww z axis velocity perturbation, ft/sec xlon longitudinal state vector α angle of attack, deg γ flightpath angle, deg ḣ Kvc Kvi Kq K rs sec Kthad Kvm nautics and Astronautics flightpath angle command, deg velocity error θ pitch attitude, deg pitch attitude rate, deg/sec φ roll attitude, deg Introduction Aircraft flight control systems are designed with extensive redundancy to ensure a low probability of failure. During recent years, however, several aircraft have experienced major flight control system failures, leaving engine thrust as the only control effectors.1,2 In some of these emergency situations, the engines were used to maintain control of the airplane flightpath angle, γ. In the majority of the cases surveyed, crashes resulted, and over 1200 people have died.1 The challenge was to create a sufficient degree of control through thrust modulation to control and safely land an airplane with severely damaged or inoperative flight control surfaces. Meeting this challenge is the objective of the Propulsion-Controlled Aircraft (PCA) Emergency Backup System. The PCA emergency backup flight control system requires that the airplane have at least two engines, preferably two wing engines. In addition, the normal control surfaces can not be locked in a hardover position which could exceed the moments resulting from the thrust of the engines. The National Aeronautics and Space Administration, Dryden Flight Research Center, Edwards, California, has performed nonlinear and linear analytical studies and conducted several flight-test programs investigating the PCA concept. Results of these programs2–6 show that gross control can be obtained by manually moving the throttles. However, making a safe runway landing is exceedingly difficult because of low phugoid and dutch roll damping coupled with the high pilot work load near the ground. To improve the performance and reduce the pilot work load, the PCA program was developed. The goal was to make flying an airplane with the PCA system a viable task with minimal or no previous pilot training with this system. This report describes the longitudinal PCA control systems and flight test results of four modes: • Mode A—using the wing engines only for control of flightpath angle, γ. • Mode B—using the tail engine for speed control in conjunction with mode A. • Mode C—using all the wing and tail engines for dynamic control of γ and speed control. • Mode D—using an existing autothrottle system for γ control. The autothrottle system was developed to provide a simpler implementation that did not require changes to the engine controllers. This system was not flight tested, but simulation results are presented.§ Within control modes A, B, and C, the pilot has the option of selecting the instrument landing system (ILS)coupled with PCA for approach and landing. This option virtually eliminates the pilot work load. Two ILS landings using the wing engines (mode A) were performed, and one is presented in this report. The lateral–directional controller is described in reference 7. Test Vehicle Description The MD-11 airplane is a large, long-range, threeengine, wide-body transport. This airplane is 202 ft long, has a wing span of 170 ft, and a maximum takeoff gross weight of 618,000 lb (fig. 1). Flight Control Systems The MD-11 airplane has a mechanical flight control system with irreversible hydraulically powered actuators. The hydraulic power provided by three independent systems is intended for fail-safe capability. Essential control functions may be maintained by any one of these three systems. Pitch control is provided by dual elevators on each horizontal stabilizer, and pitch trim is provided by a moveable horizontal stabilizer. Inboard and outboard ailerons supplemented by wing spoilers provide roll control. A dual rudder mounted on a single vertical stabilizer provides yaw control. The lateral dynamics is controlled by the yaw damper. The longitudinal stability augmentation system controls the pitch dynamics. The aerodynamic surfaces are controlled by hydraulic actuators. The flight control computers (FCC) were built by Honeywell, Phoenix, Arizona, and operate at 20 samples/sec. The MD-11 airplane is equipped with a flight management system which integrates autopilot, navigation, and autoland functions. The automatic pilot control includes a thumbwheel for commanding flightpath angle, . §NASA has a patent pending for mode d. γ cmd γ err

The NASA Dryden Flight Research Center conducted flight tests of a propulsion-controlled aircraft system on an F-15 airplane. This system was designed to explore the feasibility of providing safe emergency landing capability using only the engines to provide flight control in the event of a catastrophic loss of conventional flight controls. Control laws were designed to control the flightpath and bank angle using only commands to the throttles. Although the program was highly successful, this paper highlights some of the challenges associated with using engine thrust as a control effector. These challenges include slow engine response time, poorly modeled nonlinear engine dynamics, unmodeled inlet-airframe interactions, and difficulties with ground effect and gust rejection. Flight and simulation data illustrate these difficulties.

Flight tests of a propulsion-controlled aircraft (PCA) system on an F-15 airplane have been conducted at the NASA Dryden Flight Research Center. The airplane was flown with all flight control surfaces locked both in the manual throttles-only mode and in an augmented system mode. In the latter mode, pilot thumbwheel commands and aircraft feedback parameters were used to position the throttles. Flight evaluation results showed that the PCA system can be used to land an airplane that has suffered a major flight control system failure safely. The PCA system was used to recover the F-15 airplane from a severe upset condition, descend, and land. Pilots from NASA, U.S. Air Force, U.S. Navy, and McDonnell Douglas Aerospace evaluated the PCA system and were favorably impressed with its capability. Manual throttles-only approaches were unsuccessful. This paper describes the PCA system operation and testing. It also presents flight test results and pilot comments.

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron command-feedback gain by +/- 20 percent. Minimum eigenvalues of the return difference matrix which bound the singular values are also presented. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

The application of the eigensystem synthesis technique to place the closed-loop eigenvalues and shape the closed-loop eigenvectors has not been practical for active flutter suppression, primarily because of the availability of only one control surface (aileron) for flutter suppression. The oblique-wing aircraft, because of its configuration, provides two independent surfaces (left and right ailerons), making the application of eigensystem synthesis practical. This paper presents the application of eigensystem synthesis using output feedback for the design of an active flutter suppression system for an oblique-wing aircraft. The results obtained are compared with those obtained by linear quadratic Gaussian techniques.

The U.S. Navy and NASA are currently involved in the design and development of an unsymmetric-skew-wing aircraft capable of 65° wing sweep and flight at Mach 1.6. A generic skew-wing aircraft model was developed for 45° wing skew at a flight condition of Mach 0.70 and 3048 m altitude. At this flight condition the aircraft has a wing flutter mode. An active implementable control law was developed using the linear quadratic Gaussian design technique. A method of modal residualization was used to reduce the order of the controller used for flutter suppression.

The Automated Neural Flight Controller Test (ANCT) tool i s an application that runs in the MATLAB environment, d esigned to h elp engineers perform computational t est experiments on a simulation model of a complex adaptive system for the purpose of verification and validation of system performance. Current research in Intelligent Flight Control Systems is yielding very promising results and this leads us to consider how we can apply this level of intelligence elsewhere in other vehicles. This paper describes a concept of applied intelligence in aerospace vehicles that reaches for new levels of autonomy, robustness, safety, mission success, survivability, and efficiency (1, 2, 3, 4, 14, &15) We have developed testing methods to evaluate the performance of online adaptive neural networks that are essential for the successful deployment of neural network-based adaptive controllers in safety-critical applications. Safety-critical applications require that t he system i s working correctly and safely for a variety of operating conditions. The focus of the Strategic Methodologies for Autonomous & Robust Technology Testing (SMART-T) project at NASA Dryden Flight Research Center is to develop an approach to ensure the safe application of complex system designs on aerospace vehicles. Current effort i s focused on the verification and validation of neural networks in flight control systems. The SMART-T project has developed several t ools and techniques to evaluate the performance a nd stability of online adaptive neural networks. Current tools include: The Confidence Tool that checks all neural network input and output values then determines if the outputs are reliable. The tool calculates a c onfidence interval around the outputs of the neural network based on a Bayesian statistical model. The Envelope Tool similarly uses a Bayesian statistical approach to calculate the envelope around the c urrent point of operation where the performance of the neural network would remain satisfactory. The Sensitivity Tool performs a sensitivity analysis of the adaptive neural network during training. It is helpful to consider how small changes in the weights affect the error function, and thus how fast and how well the network can be trained. The Automated Neural Controller Test (ANCT) Tool was designed to help test engineers validate neural flight controllers in various flight conditions, quantify performance, and determine regions of stability. Input parameters, minimum and maximum values, step increments, and success/failure requirements can be specified. The test tool also has a database to store input and output parameters and test case results. It also allows the test engineer to sort test cases and pick particular test cases of interest to be reevaluated. The Automated Neural Flight Controller Test (ANCT) tool was designed to h elp engineers analyze complex control systems and assist with the validation and v erification task by helping the e ngineer conduct, manage and analyze the outputs of test experiments in a simulation environment.