In this work we investigate the use of state-of-the-art tools for
the regulation of complex, non-linear systems to improve the
methodologies currently applied to trim comprehensive virtual
prototypes of rotors and rotorcrafts.
Among the several methods that have been proposed in the
literature, the auto-pilot approach has the potential to solve
trim problems efficiently even for the large and complex vehicle
models of modern comprehensive finite element-based analysis
codes. In this approach, the trim condition is obtained by
adjusting the controls so as to virtually ``fly' the system to
the final steady (periodic) flight condition. Published
proportional auto-pilots show to work well in many practical
instances. However, they cannot guarantee good performance and
stability in all flight conditions of interest. Limit-cycle
oscillations in control time histories are often observed in
practice because of the non-linear nature of the problem and the
difficulties in enforcing the constant-in-time condition for the
controls.
To address all the above areas of concern, in this research we
propose a new auto-pilot, based on non-linear model-predictive
control (NMPC). The formulation uses a non-linear reference model
of the system augmented with an adaptive neural element, which
identifies and corrects the mismatch between reduced model and
controlled system.
The methodology is tested on the wind-tunnel trim of a rotor
multibody model and compared to an existing implementation of a
classic auto-pilot. The proposed controller shows good performance
without the need of a potentially very expensive tuning phase,
which is required in classical auto-pilots. Moreover,
model-predictive control provides a framework for guaranteeing
stability of the non-linear closed-loop system, so it seems to be
a viable approach for trimming complete rotorcraft comprehensive
models in free-flight.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/6930 |
| Date | 14 April 2005 |
| Creators | Riviello, Luca |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 1381147 bytes, application/pdf |
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