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Enhancements of an auto-thrustfunction using fuzzy logic
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Förbättring av en dragkraftsregulator med hjälp av suddig logik (Swedish)
Abstract [en]

This master's thesis aims to investigate how fuzzy logic can be used to adapt the tuning of a speed control law during certain conditions such as turbulence. The objective is to lower the speed overshoot caused by the auto-thrust function as well as the general engine agitation. The main modifications studied are direct lowering of the closed loop gains, hybridisation and filtering of the longitudinal acceleration estimation. Finally, saturations or limits on the control signal as well as on the coordination with the longitudinal control law are studied in order to cope with the possible consequences of a softer control law.

To detect the turbulence, an already existing turbulence detector is used. In addition, a wind gradient detector is designed in order to increase the gain during such wind conditions to counter ramp errors.

It is found that a general lowering of the closed loop gain in combination with a slow hybridisation, all proportional to the detected turbulence level, together with a limitation of the coordination gives a satisfactory result. In scenarios including severe turbulence and wind gradients, the forced limits are shown to be indispensable.

A conclusion is drawn that the fuzzy tuning is better adapted to turbulent conditions but that the wind gradient detection and the forced limits must be studied further. It is also concluded that the coupling between the closed loop gain and the acceleration hybridisation can be interesting to investigate. Moreover, additional realistic scenarios should be simulated in order to further validate the design.

For future studies on the subject; it is recommended that the controller tuning is validated with the help of expert knowledge. Alternatively, the tuning could be handled by an ANFIS (Adaptive Neuro Fuzzy Inference System). Finally the tuning of the controller should be validated for a wider range of flight points, most importantly the forced limits since the engine response varies a lot between different points in the flight envelope.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2014:59
Keyword [en]
Fuzzy logic, aircraft speed control, turbulence
National Category
URN: urn:nbn:se:kth:diva-153940OAI: diva2:761371
Subject / course
Systems Engineering
Educational program
Master of Science - Aerospace Engineering
Available from: 2014-11-06 Created: 2014-10-10 Last updated: 2014-11-12Bibliographically approved

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