Flight Test System Identification

With the demand for more advanced fighter aircraft, relying on unstable flight mechanical characteristics to gain flight performance, more focus has been put on model-based system engineering to help with the design work. The flight control system design is one important part that relies on this modeling. Therefore, it has become more important to develop flight mechanical models that are highly accurate in the whole flight envelope. For today’s modern fighter aircraft, the basic flight mechanical characteristics change between linear and nonlinear as well as stable and unstable as an effect of the desired capability of advanced maneuvering at subsonic, transonic and supersonic speeds. This thesis combines the subject of system identification, which is the art of building mathematical models of dynamical systems based on measurements, with aeronautical engineering in order to find methods for identifying flight mechanical characteristics. Here, some challenging aeronautical identification problems, estimating model parameters from flight-testing, are treated. Two aspects are considered. The first is online identification during flight-testing with the intent to aid the engineers in the analysis process when looking at the flight mechanical characteristics. This will also ensure that enough information is available in the resulting test data for post-flight analysis. Here, a frequency domain method is used. An existing method has been developed further by including an Instrumental Variable approach to take care of noisy data including atmospheric turbulence and by a sensor-fusion step to handle varying excitation during an experiment. The method treats linear systems that can be both stable and unstable working under feedback control. An experiment has been performed on a radio-controlled demonstrator aircraft. For this, multisine input signals have been designed and the results show that it is possible to perform more time-efficient flight-testing compared with standard input signals. The other aspect is post-flight identification of nonlinear characteristics. Here the properties of a parameterized observer approach, using a prediction-error method, are investigated. This approach is compared with four other methods for some test cases. It is shown that this parameterized observer approach is the most robust one with respect to noise disturbances and initial offsets. Another attractive property is that no user parameters have to be tuned by the engineers in order to get the best performance. All methods in this thesis have been validated on simulated data where the system is known, and have also been tested on real flight test data. Both of the investigated approaches show promising results.

Although many books have been written on the theory of system identification, few are available that provide a complete engineering treatment of system identification and how to successfully apply it to flight vehicles. This book presents proven methods, practical guidelines, and real-world flight-test results for a wide range of state-of-the-art flight vehicles, from small uncrewed aerial vehicles (UAVs) to large manned aircraft/rotorcraft.

This book provides a comprehensive overview of both the theoretical underpinnings and the practical application of aircraft modeling based on experimental data also known as aircraft system identification. Much of the material presented comes from the authors own extensive research and teaching activities at the NASA Langley Research Center, and is based on real-world applications of system identification to aircraft. The book uses actual flight-test and wind-tunnel data for case studies and examples, and is a valuable resource for researchers and practicing engineers, as well as a textbook for postgraduate and senior-level courses. [...] The methods and algorithms explained in the book are implemented in a NASA software toolbox called SIDPAC (System IDentification Programs for AirCraft). SIDPAC is written in MATLAB®, and is available by request from NASA Langley Research Center. SIDPAC is composed of many different tools that implement a wide variety of approaches explained fully in the book. These tools can be readily applied to solve aircraft system identification problems.

This valuable volume offers a systematic approach to flight vehicle system identification and exhaustively covers the time domain methodology. It addresses in detail the theoretical and practical aspects of various parameter estimation methods, including those in the stochastic framework and focusing on nonlinear models, cost functions, optimization methods, and residual analysis. A pragmatic and balanced account of pros and cons in each case is provided. The book also presents data gathering and model validation, and covers both large-scale systems and high-fidelity modeling. Real world problems dealing with a variety of flight vehicle applications are addressed and solutions are provided. Examples encompass such problems as estimation of aerodynamics, stability, and control derivatives from flight data, flight path reconstruction, nonlinearities in control surface effectiveness, stall hysteresis, unstable aircraft, and other critical considerations.

Downscaled physical models, also referred to as subscale models, have played an essential role in the investigation of the complex physics of flight until the recent disruption of numerical simulation. Despite the fact that improvements in computational methods are slowly pushing experimental techniques towards a secondary role as verification or calibration tools, real-world testing of physical prototypes still provides an unmatched confidence. Physical models are very effective at revealing issues that are sometimes not correctly identified in the virtual domain, and hence can be a valuable complement to other design tools. But traditional wind-tunnel testing cannot always meet all of the requirements of modern aeronautical research and development. It is nowadays too expensive to use these scarce facilities to explore different design iterations during the initial stages of aircraft development, or to experiment with new and immature technologies. Testing of free-flight subscale models, referred to as Subscale Flight Testing (SFT), could offer an affordable and low-risk alternative for complementing conventional techniques with both qualitative and quantitative information. The miniaturisation of mechatronic systems, the advances in rapid-prototyping techniques and power storage, as well as new manufacturing methods, currently enable the development of sophisticated test objects at scales that were impractical some decades ago. Moreover, the recent boom in the commercial drone industry has driven a quick development of specialised electronics and sensors, which offer nowadays surprising capabilities at competitive prices. These recent technological disruptions have significantly altered the cost-benefit function of SFT and it is necessary to re-evaluate its potential in the contemporary aircraft development context. This thesis aims to increase the comprehension and knowledge of the SFT method in order to define a practical framework for its use in aircraft design; focusing on low-cost, short-time solutions that don’t require more than a small organization and few resources. This objective is approached from a theoretical point of view by means of an analysis of the physical and practical limitations of the scaling laws; and from an empirical point of view by means of field experiments aimed at identifying practical needs for equipment, methods, and tools. A low-cost data acquisition system is developed and tested; a novel method for semi-automated flight testing in small airspaces is proposed; a set of tools for analysis and visualisation of flight data is presented; and it is also demonstrated that it is possible to explore and demonstrate new technology using SFT with a very limited amount of economic and human resources. All these, together with a theoretical review and contextualisation, contribute to increasing the comprehension and knowledge of the SFT method in general, and its potential applications in aircraft conceptual design in particular.

Identification of low order equivalent system dynamic models from flight test data was studied. Inputs were pilot control deflections, and outputs were aircraft responses, so the models characterized the total aircraft response including bare airframe and flight control system. Theoretical investigations were conducted and related to results found in the literature. Low order equivalent system modeling techniques using output error and equation error parameter estimation in the frequency domain were developed and validated on simulation data. It was found that some common difficulties encountered in identifying closed loop low order equivalent system models from flight test data could be overcome using the developed techniques. Implications for data requirements and experiment design were discussed. The developed methods were demonstrated using realistic simulation cases, then applied to closed loop flight test data from the NASA F-18 High Alpha Research Vehicle.Morelli, Eugene A.Langley Research CenterEXPERIMENT DESIGN; DYNAMIC MODELS; SYSTEM IDENTIFICATION; FLIGHT TESTS; DATA PROCESSING; FLIGHT CONTROL; DEFLECTION; AIRCRAFT PERFORMANCE; ERROR ANALYSIS; FLIGHT CHARACTERISTICS; FEEDBACK CONTROL