Flight Test of Optimal Inputs and Comparison with Conventional Inputs
01 Mar 1999-Journal of Aircraft (American Institute of Aeronautics and Astronautics (AIAA))-Vol. 36, Iss: 2, pp 389-397
TL;DR: In this paper, a technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Alpha Research Vehicle (HARV) and compared on an equal basis for optimal input designs and conventional inputs at the same flight condition.
Abstract: A technique for designing optimal inputs for aerodynamic parameter estimation was flight tested on the F-18 High Alpha Research Vehicle. Model parameter accuracies calculated from flight-test data were compared on an equal basis for optimal input designs and conventional inputs at the same flight condition. In spite of errors in the a priori input design models and distortions of the input forms by the feedback control system, analysis of data generated by the optimal inputs revealed lower estimated parameter errors compared with conventional 3-2-1-1 and doublet inputs. In addition, the tests using optimal input designs demonstrated enhanced design flexibility, allowing the optimal input design technique to use a larger input amplitude to achieve further increases in estimated parameter accuracy without departing from the desired flight-test condition. This work validated the analysis used to develop the optimal input designs, and demonstrated the feasibility and effectiveness of the optimal input design technique.
Citations
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01 Jan 2002
TL;DR: SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output- error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization.
Abstract: A collection of computer programs for aircraft system identification is described and demonstrated. The programs, collectively called System IDentification Programs for AirCraft, or SIDPAC, were developed in MATLAB as m-file functions. SIDPAC has been used successfully at NASA Langley Research Center with data from many different flight test programs and wind tunnel experiments. SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output-error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization. An overview of SIDPAC capabilities is provided, along with a demonstration of the use of SIDPAC with real flight test data from the NASA Glenn Twin Otter aircraft. The SIDPAC software is available without charge to U.S. citizens by request to the author, contingent on the requestor completing a NASA software usage agreement.
127Â citations
TL;DR: The past, present, and future of system identification applied to aircraft at NASA Langley Research Center (LaRC) in Hampton, Virginia are discussed in this article, including some perspective on the role these developments played in the practice of identifying aircraft.
Abstract: The past, present, and future of system identification applied to aircraft at NASA Langley Research Center (LaRC) in Hampton, Virginia, are discussed. Significant research advances generated at NASA LaRC in the past are summarized, including some perspective on the role these developments played in the practice of system identification applied to aircraft. Selected recent research efforts are described, to give an idea of the type of activities currently being pursued at NASA LaRC. These efforts include real-time parameter estimation, identifying flying qualities models, advanced experiment design and modeling techniques for static wind-tunnel database development, and indicial function identification for unsteady aerodynamic modeling. Projected future developments in the area are outlined
127Â citations
TL;DR: In this paper, a trajectory reconstruction tool for the NASA X-43A/Hyper-X high-speed research vehicle and its implementation for the reconstruction and analysis of flight-test data are discussed.
Abstract: The formulation and development of a trajectory reconstruction tool for the NASA X-43A/Hyper-X high-speed research vehicle and its implementation for the reconstruction and analysis of flight-test data are discussed. Extended Kalman filtering techniques are employed to reconstruct the trajectory of the vehicle, based on numerical integration of inertial measurement data along with redundant measurements of the vehicle state provided by global positioning system measurements of position and velocity. The equations of motion are formulated to include the effects of several systematic error sources, the values of which may also be estimated by the filtering routines. Additionally, smoothing algorithms have been implemented in which the final value of the state (or an augmented state that includes other systematic error parameters to be estimated) and covariance are propagated back to the initial time to generate the best-estimated trajectory, based on all available data. The methods are applied to the problem of reconstructing the trajectory of the Hyper-X vehicle from flight data.
61Â citations
01 Jan 2004
TL;DR: The formulation and development of a trajectory reconstruction tool for the NASA X-43A/Hyper-X high-speed research vehicle and its implementation for the reconstruction and analysis of flight-test data are discussed.
Abstract: This paper discusses the formulation and development of a trajectory reconstruction tool for the NASA X{43A/Hyper{X high speed research vehicle, and its implementation for the reconstruction and analysis of ight test data. Extended Kalman ltering techniques are employed to reconstruct the trajectory of the vehicle, based upon numerical integration of inertial measurement data along with redundant measurements of the vehicle state. The equations of motion are formulated in order to include the effects of several systematic error sources, whose values may also be estimated by the ltering routines. Additionally, smoothing algorithms have been implemented in which the nal value of the state (or an augmented state that includes other systematic error parameters to be estimated) and covariance are propagated back to the initial time to generate the best-estimated trajectory, based upon all available data. The methods are applied to the problem of reconstructing the trajectory of the Hyper-X vehicle from ight data.
48Â citations
10 Aug 2009
TL;DR: In this article, the authors used the recursive Fourier transform regression (FTR) method in frequency domain to estimate the six degree of freedom (6DOF) model of an aircraft.
Abstract: In-flight identification of an aircraft’s dynamic model can benefit adaptive control schemes by providing estimates of aerodynamic stability derivatives in real time. This information is useful when the dynamic model changes severely in flight such as when faults and failures occur. Moreover a continuously updating model of the aircraft dynamics can be used to monitor the performance of onboard controllers. Flight test data was collected using a sum of sines input implemented in closed loop on a twin engine, fixed wing, Unmanned Aerial Vehicle. This data has been used to estimate a complete six degree of freedom aircraft linear model using the recursive Fourier Transform Regression method in frequency domain. The methods presented in this paper have been successfully validated using computer simulation and real flight data. This paper shows the feasibility of using the frequency domain Fourier Transform Regression method for real time parameter identification.
35Â citations
References
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TL;DR: This paper surveys the field of optimal input design for parameter estimation as it has developed over the last two decades, with a derivation of the Fisher information matrix for multiinput multioutput systems with process noise.
Abstract: This paper surveys the field of optimal input design for parameter estimation as it has developed over the last two decades. Many of the developments covered are only recent and have not appeared in the open literature elsewhere. After a brief introduction, the paper discusses the historical background of the subject both in the engineering and in the statistical literature. The concepts of optimality and input design are then discussed, followed by a derivation of the Fisher information matrix for multiinput multioutput systems with process noise. The design procedures are divided into the categories of time-domain methods and frequency-domain methods, with the former being more general, but also more time consuming (computationally). Several extensions to state constraints, continuous-time systems, etc., are discussed. A number of examples are given to illustrate the nature of optimal inputs. The results on time-domain synthesis with state constraints and their relationship to "dual control" are new.
568Â citations
01 Jun 1986
TL;DR: The primary purpose of the document is to present a comprehensive and unified picture of the entire parameter estimation process and its integration into a flight test program.
Abstract: The practical application of parameter estimation methodology to the problem of estimating aircraft stability and control derivatives from flight test data is examined. The primary purpose of the document is to present a comprehensive and unified picture of the entire parameter estimation process and its integration into a flight test program. The document concentrates on the output-error method to provide a focus for detailed examination and to allow us to give specific examples of situations that have arisen. The document first derives the aircraft equations of motion in a form suitable for application to estimation of stability and control derivatives. It then discusses the issues that arise in adapting the equations to the limitations of analysis programs, using a specific program for an example. The roles and issues relating to mass distribution data, preflight predictions, maneuver design, flight scheduling, instrumentation sensors, data acquisition systems, and data processing are then addressed. Finally, the document discusses evaluation and the use of the analysis results.
182Â citations
01 Sep 1992
TL;DR: The F-18 High Alpha Research Vehicle is the first thrust-vectoring testbed aircraft used to study the aerodynamics and maneuvering available in the poststall flight regime and to provide the data for validating ground prediction techniques.
Abstract: The F-18 High Alpha Research Vehicle is the first thrust-vectoring testbed aircraft used to study the aerodynamics and maneuvering available in the poststall flight regime and to provide the data for validating ground prediction techniques. The aircraft includes a flexible research flight control system and full research instrumentation. The capability to control the vehicle at angles of attack up to 70 degrees is also included. This aircraft was modified by adding a pitch and yaw thrust-vectoring system. No significant problems occurred during the envelope expansion phase of the program. This aircraft has demonstrated excellent control in the wing rock region and increased rolling performance at high angles of attack. Initial pilot reports indicate that the increased capability is desirable although some difficulty in judging the size and timing of control inputs was observed. The aircraft, preflight ground testing and envelope expansion flight tests are described.
56Â citations
TL;DR: A technique based on Fourier series analysis was developed to separate signal from noise for flight test data with an optimal filter designed in the frequency domain, and the theoretical analysis was shown to be sound for both simulated data andFlight test data.
Abstract: A technique based on Fourier series analysis was developed to separate signal from noise for flight test data. This was done with an optimal filter designed in the frequency domain. The method is general, and separates signal and noise based on the spectral content of the measurement time history. Smoothed time histories with no time lag were computed, and noise characteristics were accurately estimated. The technique can be used independently of other procedures, and does not require assumptions about the independence of the noise processes or the frequency content of the measurements. Simulated data was used to demonstrate the technique and to evaluate the accuracy of estimated noise characteristics. For 20 simulation cases, noise standard errors were estimated within 5% of the true values. Flight test data from a lateral maneuver of the F-18 High Alpha Research Vehicle was then analyzed. The theoretical analysis was shown to be sound for both simulated data and flight test data.
49Â citations
01 Mar 1973
TL;DR: In this paper, a new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion, which is capable of processing data from both linear and nonlinear models, both with and without process noise.
Abstract: A new method of extracting aircraft stability and control derivatives from flight test data is developed based on the maximum likelihood cirterion. It is shown that this new method is capable of processing data from both linear and nonlinear models, both with and without process noise and includes output error and equation error methods as special cases. The first application of this method to flight test data is reported for lateral maneuvers of the HL-10 and M2/F3 lifting bodies, including the extraction of stability and control derivatives in the presence of wind gusts. All the problems encountered in this identification study are discussed. Several different methods (including a priori weighting, parameter fixing and constrained parameter values) for dealing with identifiability and uniqueness problems are introduced and the results given. The method for the design of optimal inputs for identifying the parameters of linear dynamic systems is also given. The criterion used for the optimization is the sensitivity of the system output to the unknown parameters. Several simple examples are first given and then the results of an extensive stability and control dervative identification simulation for a C-8 aircraft are detailed.
46Â citations