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Descripción
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| Unmanned Aerial Vehicles have widely demonstrated their utility in military applications. Different vehicle types - airplanes in particular - have been used for surveillance and reconnaissance missions. Civil use of UAVs, as applied to early alert, inspection and aerialimagery systems, among others, is more recent (OSD, 2005). For many of these applications, the most suitable vehicle is the helicopter because it offers a good balance between manoeuvrability and speed, as well as for its hovering capability. A mathematical model of a helicopter¿s flight dynamics is critical for the development of controllers that enable autonomous flight. Control strategies are first tested within simulators where an accurate identification process guarantees good performance under real conditions. The model, used as a simulator, may also be an excellent output predictor for cases in which data cannot be collected by the embedded system due to malfunction (e.g. transmission delay or lack of signal). With this technology, more robust fail-safe modes are possible. The state of a helicopter is described by its attitude and position and the characteristics of its dynamics system correspond to those of a non-linear, multivariable, highly coupled and unstable system (Lopez, 1993). The identification process can be performed in different ways, on analytical, empirical or hybrid models, each with its advantages and disadvantages. This Chapter describes how to model the dynamic of a mini-helicopter using different kinds of supervised neural networks, an empirical model. Specifically, the networks are used for the identification of both attitude and position of a radio controlled mini helicopter. Different hybrid supervised neural network architectures, as well as different training strategies, will be discussed and compared on different flight stages. The final aim of the identification process is to build a realistic flight model to be incorporated in a flight simulator. Although several neural network-based controllers for UAVs can be found in the literature, there is little work on flight simulator models. Simulators are valuable tools for in-lab testing and experimenting of different control algorithms and techniques for autonomous flight. A model of a helicopter¿s flight dynamics is critical for the development of good a simulator. Moreover, a model may also be used during flight as predictor for anticipating the behaviour of the helicopter in response to control inputs. The Chapter first focuses on two neural-network architectures that are well suited for the particular case of mini-helicopters, and describes two algorithms for the training of such neural-network models. These architectures can be used for both multi-layer and radial-based hybrid networks. The advantages and disadvantages of using neural networks will also be discussed. Then, a methodology for acquiring the training patterns and training the networks for different flight stages is presented, and an algorithm for using the networks during simulations is described. The methodology is result of several years of experience in UAVs. Finally, the two architectures and training methods are tested on real flight data and simulation data, and the results are compared and analysed. | |
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Internacional
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Si |
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DOI
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Edición del Libro
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0 |
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Editorial del Libro
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IN-TECH |
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ISBN
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978-953-7619-41-1 |
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Serie
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Título del Libro
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Aerial Vehicles |
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Desde página
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602 |
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Hasta página
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620 |