Aircraft and rotorcraft system identification : engineering by Mark Tischler, Robert Remple

By Mark Tischler, Robert Remple

Even if many books were written at the concept of method identity, few can be found that supply an entire engineering therapy of process identity and the way to effectively use it on flight automobiles. This ebook presents the original point of view of over two decades of flight-test purposes to either airplane and rotorcraft and is a helpful source for college students, operating engineers, and others drawn to atmospheric flight mechanics, modeling and simulation, and try out and overview. It offers confirmed equipment, sensible directions, and real-world flight-test effects for quite a lot of cutting-edge flight cars, from small uncrewed aerial cars (UAVs) to giant manned aircraft/rotorcraft.

Beginning with the elemental thoughts of procedure id, each one bankruptcy strains an easy simulation instance and genuine flight examples in the course of the step by step approach from instrumentation and knowledge checking to version extraction and version verification. The frequency-response procedure, that's precise to this publication, is mainly like minded for process id of plane and rotorcraft dynamics versions from flight-test facts. an entire bankruptcy is dedicated to higher-order modeling of helicopters. Many functions are integrated to illustrate how the goods as a result of approach id are used. particular functions contain flight mechanics and handling-qualities analyses, balance margin selection, structural mode choice, and simulation version constancy assessment.

The publication assumes wisdom of the elemental ideas of aeronautics, Laplace transforms, and flight dynamics and classical keep watch over. Emphasis is put on engineering equipment and interpretation of flight-test effects and every key procedure or research program is illustrated with pics received from the process identity software program (CIFERÆ) supplied with the booklet. Case reviews in line with actual flight-test tasks are incorporated besides! as difficulties for college students to resolve utilizing the supplied CIFERÆ software.

A ideas handbook, written via Christina M. Ivler and Mark B. Tischler is out there from the writer.

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6 Nonparametric system-identification modeling provides excellent insight into the key aspects of the aircraft dynamics before moving on to the more complex parametric modeling stage. For example, the nonparametric results can be used to great benefit in selecting the appropriate order of the transfer-function and/or statespace models. The frequency-response identification method presented in this book is an integrated two-stage process of nonparametric followed by parametric modeling. There are many applications based solely on the frequency-response characterizations, including handling-qualities analysis in terms of the bandwidth and time-delay parameters, pilot-in-the-loop analysis, stability margin determination, classical control system design, and validation and tuning of physics-based simulation models.

A typical example is the Bode plot for aircraft sideslip response to rudder β ⁄ δ r obtained from flight-test data shown in the solid 2 curve of Fig. 5. The bottom plot is the associated coherence function γ xy , which is an excellent indicator of the frequency-response accuracy. 6 ) , which in this example is true for all but the highest frequencies, indicates an accurate frequency-response identification result. A frequency-response data curve that characterizes a single-input/single-output (SISO) subsystem such as an actuator can easily be obtained from the measurement data of the excitation (electrical input) and response (actuator displacement).

Applications requiring identification of state-space models include the determination of simulation models for piloted simulations, MIMO control system design, the comparison of wind-tunnel vs flight values of stability and control derivatives, and the direct determination of correction factors for tuning physics-based simulation models. 2 Retaining extraneous parameters in a model can incrementally reduce the fitting error, but result in a higher variability of the estimated parameters. ” Considerable emphasis is given in this book to systematic methods of model structure reduction that achieve this goal.

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