| Switched Reluctance Machine (SRM) boasts lots of excellent characteristics like high fault tolerance, high reliability, high power density, wide-speed operation ability and being easy to cool etc. The distinctive advantages of SRM lead to its better suitability for aerospace applications than the conventional drives. As a matter of fact, since More Electric Aircraft (MEA) and All Electric Aircraft (AEA) become the trend of future aircraft power system, SRM is facing great opportunities in aerospace applications. Big aviation countries like USA have selected SRM as the first scheme of the electrical power system for future MEA/AEA, and main research institutions of aircraft engine like GE, Rolls-Roys and Pratt & Whitney etc have carried out researches on SRM as potential aircraft engine Starter/Generator (S/G). In the foreseeable future, SRM system should be one of the potential important subsystems in the aircraft power systems.An aircraft power system is a complex, multi-disciplinary physical system. Its development is the key factor in the progress of the development of an aircraft. CAE technology-based simulation ensures the predictive design technique, which is important in that it saves the cost and shortens the development period. The aviation industry in China is still in its beginning stage. It's by no way comparable to the conventional aviation powers such as USA, Russia, Britain and France. In the research and development of the aircraft engine power system, the disparity is especially huge. Given that the foundation of independent development in aviation industry is strongly weak in China, It's meaningful to develop virtual prototype-based aircraft power system CAE technology to improve China's aviation industry.Simulation of aircraft power system involves coupling of multi-physics. Up to now, all the research on simulation of aircraft engine and SRM was confined to their individual characteristics. The previous simulation of aircraft engine mainly focused on some specific aspects like the compressor map, the turbine map or the combustion characteristics etc; likewise, simulation of SRM was carried out alone and was focused on the electromechanical field. As for applications of SRM in aerospace, system-level simulation of aircraft power system together with SRM as a whole would be helpful for dynamic analysis. It should involve the coupling of multi-domains like mechanics, electrics, hydraulics, pneumatics and thermodynamics etc. However, very few research findings could be found in this aspect. On this account, this paper is devoted to analyze the coupling sub-systems of aircraft power system, and to realize multi-domain system-level simulation of aircraft power system. It's aimed to provide dynamic analysis of the coupling sub-systems and that of SRM on the global operation range.Therefore, our research group is devoted to developing a rather complete, generic aircraft power system model library. The model library is built based on Modelica, which is especially suitable for the modeling of complex, multi-disciplinary system, and is honored as the next generation unified modeling language (UML). The library is parameterized, expandable and it's also aimed to be standard and generic. Compared to the conventional block-oriented, signal-flow modeling software like Simulink, the object-oriented characteristic of Modelica makes the system decomposition more similar to the real physical system, and the non-causal characteristic of Modelica endows the model with bidirectional calculation ability, which is a good solution to problems of the calculation flow and the mutual coupling of sub-systems.SRM system is a potential important subsystem in aircraft power system, whereas SRM drive (SRD) itself is a time-varying, strongly nonlinear system. There exist many problems in the control of SRM. SRD characterizes flexible control but being hard to optimize. The conventional parameter-fixed PID control is not good enough to achieve satisfactory control effect. Neural network (NN) technique boasts strong nonlinear approximation, self-study, self-adaptability and parallel processing ability, and thus it's suitable to be used in the modeling and control of uncertain or nonlinear systems. NN technique has been applied in the modeling and control of SRM ever since 1990s. There has been quite a lot of school in the research of NN technology, and multi-layer feed-forward NN is one of the most fruitful and the most widely used techniques. However, there have still been some problems in NN research: the NN structure can not be derived analytically; there's no particular learning algorithm for dynamic identification and control; it's hard to apply the empirical knowledge in the initialization and training of the networks.Considering the potential application of SRM in aerospace, this dissertation carries out the research work on the Modelica-based nonlinear modeling of SRM, the application and control analysis of SRM in aerospace, and it's also aimed to improve or present solutions to some key technical problems in the NN control of SRM. The following are the achievements that are fulfilled in this dissertation:(1) Based on the analysis of the magnetic field characteristics, an improved nonlinear analytical model of SRM is presented. The original static magnetic characteristics data are derived from FEM and the model is verified through comparison with FEM,statically and dynamically. The model is proved to be satisfactory in accuracy and it also ensures rapid calculation due to its analyticity. Further on, this dissertation derives a BP neural network (NN) model of SRM. The structure of the network is based on gold section method in order for a balance between accuracy and complexity. Compared to the analytical model, the BP NN model of SRM is more accurate. However, the model is much more complex for calculation.(2) A comparatively complete SRM system model library is developed on Modelica/Dymola. For dynamic simulation of a complex physical system, the calculation is very intense. Therefore, models for sub-systems should be rapidly computable besides precise in accuracy. Thus, the nonlinear analytical model of SRM is chosen for the SRM Modelica model. The model library is parameterized, generic and conveniently expandable. It can be used as an independent modeling software package for the CAE analysis of SRM design alone, and it can also serve the whole aircraft power system model library to assemble a system-level model for system-level simulation analysis.(3) A BP NN PID controller is developed for the adaptive speed control of SRM. The interest of the dissertation is to explore on the utilization of the prior empirical knowledge as guidance in the initializing and the training of the neural networks. It's aimed to make the networks less sensitive on the initial weights and biases. Two proposed algorithms are compared. Simulation results show that the two proposed algorithms are much less sensitive on the initial weights. Learning algorithms of feed-forward NN for control applications are further discussed. This dissertation explores to involve the variation of the inputs into the adjusting of the weights. Subsequently, two algorithms, namely Dynamic Levenberg-Marquardt (DLM) and Dynamic Gradient Method (DGM), are presented. The purpose is to accelerate the training of the networks, and to improve the output accuracy after training as well, and also to solve problems caused by the transformation from static-space modeling to dynamic-time modeling.(5) In the end, this paper analyzes SRM's aerospace applications and the respective control. Aerospace applications characterize wide-speed operation. Therefore, selective operating points'analysis, which is mostly used in previous research papers, is not enough to evaluate the SRM's global performance. System-level models of Switched reluctance generator (SRG) coupled with an aircraft engine are constructed on Modelica/Dymola. Dynamic simulation results verify the control methods. The example also verifies the validation of the idea of system-level simulation with multi-field coupling, and thus makes a way to the predictive design of the aircraft power system.
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