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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
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NASA NTRS2015

In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Wall, John H.↗
NASA NTRS2015

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

VanZwieten, Tannen S.↗
NASA NTRS1973

The Kalman-Yacubovich lemma in adaptive control system design.

New results are presented that pertain to the design of model-reference adaptive control systems with the aid of the Kalman-Yacubovich lemma derived in connection with the problem of Lure. It is shown that the Kalman-Yacubovich lemma is a much more powerful tool for designing model-reference adaptive control systems than was previously thought.

Monopoli, R. V.↗
NASA NTRS1979

Flight test experience with an adaptive control system using a maximum likelihood parameter estimation technique

The flight test performance of an adaptive control system for the F-8 DFBW aircraft is summarized. The adaptive system is based on explicit identification of surface effectiveness parameters which are used for gain scheduling in a command augmentation system. Performance of this control law under various design parameter variations is presented. These include variations in test signal level, sample rate, and identification channel structure. Flight performance closely matches analysis and simulation predictions from previous references.

Hartmann, G.↗
NASA NTRS1972

Design of model-reference adaptive control systems using Liapunov functions.

This paper considers the design of model-reference adaptive control systems using Liapunov functions. An adaption rule is developed analytically. This adaption rule results from the use of a Liapunov function which contains several positive semidefinite terms not included in previous references. The inclusion of these terms results in an adaption rule which is a function of the error, the plant states and the derivative and integral of these quantities. The adaption rule is applied to a simplified model of the Space Shuttle vehicle. Simulation results show that the maximum response error in the system is reduced by using this adaption rule.

Sutherlin, D. W.↗
NASA NTRS1979

Application of Model Reference Adaptive Control System to Instrument Pointing System /IPS/

A Model Reference Adaptive Controller (MRAC) is derived for a Shuttle payload called the Instrument Pointing System (IPS). The unique features of this MRAC design are that total state feedback is not required, that the internal structure of the model is independent of the internal structure of the IPS, and that the model input is of bounded variation and not required a priori. An application of Liapunov's stability theorems is used to synthesize a control signal which assures MRAC asymptotic stability. Exponential observers are used to obtain the necessary state information to implement the control synthesis. Results are presented which show how effectively the MRAC can maneuver the IPS.

Waites, H. B.↗
NASA NTRS1986

Design of adaptive control systems by means of self-adjusting transversal filters

The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.

Merhav, S. J.↗
NASA NTRS1972

Design of a digital adaptive control system for reentry vehicles.

The flying qualities of atmospheric reentry vehicles experience considerable variations due to the wide changes in flight conditions characteristic of reentry trajectories. A digital adaptive control system has been designed to modify the vehicle's dynamic characteristics and to provide desired flying qualities for all flight conditions. This adaptive control system consists of a finite-memory identifier which determines the vehicle's unknown parameters, and a gain computer which calculates feedback gains to satisfy flying quality requirements.

Picon-Jimenez, J. L.↗
NASA NTRS1977

A moving window parameter adaptive control system for the F8-DFBW aircraft

This paper describes the moving window parameter adaptive control system developed for the NASA F8-DFBW aircraft. The control system employs a parameter identification process that, iteratively, adjusts parameters of a model of the aircraft motions in a batch-processing manner so that responses generated from the model fit the outputs of sensors stored in a finite record referred to as the moving window. Tests are made on the validity of the parameter estimates before using the parameters in an on-line design process. The on-line design process is an algebraic mapping of the parameters of the model into primary control system feedback and feedforward gains. The mapping was selected to satisfy specific flying quality characteristics over the range of parameter variations expected. Results are presented from simulation studies on the identification algorithm made during the development of the system.

Dunn, H. J.↗
NASA NTRS2006

Case Study: Test Results of a Tool and Method for In-Flight, Adaptive Control System Verification on a NASA F-15 Flight Research Aircraft

Adaptive control technologies that incorporate learning algorithms have been proposed to enable autonomous flight control and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments [1-2]. At the present time, however, it is unknown how adaptive algorithms can be routinely verified, validated, and certified for use in safety-critical applications. Rigorous methods for adaptive software verification end validation must be developed to ensure that. the control software functions as required and is highly safe and reliable. A large gap appears to exist between the point at which control system designers feel the verification process is complete, and when FAA certification officials agree it is complete. Certification of adaptive flight control software verification is complicated by the use of learning algorithms (e.g., neural networks) and degrees of system non-determinism. Of course, analytical efforts must be made in the verification process to place guarantees on learning algorithm stability, rate of convergence, and convergence accuracy. However, to satisfy FAA certification requirements, it must be demonstrated that the adaptive flight control system is also able to fail and still allow the aircraft to be flown safely or to land, while at the same time providing a means of crew notification of the (impending) failure. It was for this purpose that the NASA Ames Confidence Tool was developed [3]. This paper presents the Confidence Tool as a means of providing in-flight software assurance monitoring of an adaptive flight control system. The paper will present the data obtained from flight testing the tool on a specially modified F-15 aircraft designed to simulate loss of flight control faces.

Jacklin, Stephen A.↗
NASA NTRS1976

A moving-window parameter adaptive control system for the F8-DFBW aircraft

This paper describes the moving-window parameter adaptive control system developed for the NASA F8-DFBW aircraft. The control system employs a parameter identification process that, iteratively, adjusts parameters of a model of the aircraft motions in a batch processing manner so that responses generated from the model fit the outputs of sensors stored in a finite record referred to as the moving window. Tests are made on the validity of the parameter estimates before using the parameters in an on-line design process. The on-line design process is an algebraic mapping of the parameters of the model into primary control system feedback and feedforward gains. The mapping was selected to satisfy specific flying quality characteristics over the range of parameter variations expected. Results are presented from simulation studies on the identification algorithm, tests for parameter validity, and the on-line design process.

Montgomery, R. C.↗