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Thermal control system design platform for the development and application of

Author: LiQiang
Tutor: LinYiQing
School: Shandong University
Course: Engineering Thermophysics
Keywords: Thermal Control System Design platform Heat Exchanger Neural Network Predictive Control
CLC: TK32
Type: Master's thesis
Year: 2005
Downloads: 141
Quote: 0
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Abstract


Control system simulation control system design has become a critical step . Thermal Control object is often more complex, security and economy requirements are also higher , it is almost impossible to carry on an object in the thermal control system design directly , but through simulation in order to deepen their understanding of the nature of knowledge and understanding of the system . At the same time , object-oriented software development method and the traditional process-oriented control system simulation software development methods have many advantages, can greatly facilitate the thermal control system design platform for the development and follow-up . According to the characteristics of thermal control system , this paper uses object-oriented thinking and development tools , application Java language designed thermal control system design platform . The software uses the typical aspects of the operation as the basic unit can be quick and easy to achieve structural mapping and processing. Through the signal flow graph analysis and Mason formula , we propose a new single- input single-output transfer function of the system structure diagram seeking computer algorithm to complete the derivation of the formula and related transfer function discrete processing and so on . Based on the analysis of modern object modeling and thermal control technology , based on a thermal control system design software platform to develop a dual- input single-output thermal object gray box-type neural network modeling and single-input single-output thermal object single neuron adaptive predictive control software modules , and the function is applied to large delay thermal objects - heat exchanger modeling and control simulation experiments . In the modeling process, derive a common feedforward networks and improved Elman neural network based on the operating point of the gray box modeling algorithm , these two algorithms in a matrix that is easy to understand and program applicability. In the network training , we propose a comprehensive adaptive step adjustment strategy to ensure network convergence and avoid local minima . Through simulation experiments to determine the neural network training in the heat exchanger and single neuron adaptive predictive control system in the optimum range of parameters . Compared with the conventional PID control , single neuron can obtain better control of quality control , indicating that the control system is feasible , this also indicates that the control system further improvements .

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CLC: > Industrial Technology > Energy and Power Engineering > Thermal measurement and thermal automatic control > Thermal Automatic Control
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