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Neural Fuzzy Sliding Mode Control Based on 3D Crane System

Author: ZhaoWei
Tutor: LiHongXing
School: Dalian University of Technology
Course: Control Engineering
Keywords: 3D Crane Fuzzy Sliding Mode Control Neural Fuzzy Control Universe Divide
CLC: TP273.4
Type: Master's thesis
Year: 2011
Downloads: 38
Quote: 0
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Abstract


This paper is based on the fuzzy sliding model control and neural fuzzy control, integrating the both methods, getting their advantages and avoiding the shortages. At last, a controller with strong robustness and adaptation is constructed. This method accomplishes the controlling of the positioning and anti-swing on the 3D crane system.The 3D crane system is similar with a space single inverted pendulum. It’s a typical nonlinear. under-actuated, high-order, multivariable and strong coupling complicated system. It directly expresses many abstract controlling concepts such as stability, controllability, rapidity and robustness. The research on the 3D system has an important theoretical impact and practical significance. It not only solutes the difficult question in practice, but also represents a kind of nonlinear and under-actuated system. So the methods and techniques stemming from the study of the 3D crane system can be extensively used in many fields, such as space technology, transportation, industrial production and people’s daily life. This research has a broad development and utilization foreground.At first, we explain the research significance and objective. The research status of the 3D crane system is analyzed from PID controller to intelligent controller.Secondly, we introduce the basic structure of the 3D crane system briefly and build its mathematic model with Lagrange method. Then we simplify the model by fixing the length of lifting rope, and prove its simple model is the same as single inverted pendulum. Through the qualitative analysis theorems, the paper proves that the 3D crane system is completely controllable and observable.In addition, we introduce the fuzzy control, the sliding model control and neural network theory, analyze their advantages and shortages. Then we combine them to design a new controller which is neural fuzzy sliding model controller. We declare this control method is effective through analyzing the simulation. And comparing to the simulation of PID controller, we prove the new one is more effective.At last, we developed the real-time control program in VC++6.0 environment, and realize the control of the 3D crane system. It proves this method is effective again and gets good control effects.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Automatic control,automatic control system > Fuzzy control, fuzzy control system
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