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Research on Control of the 3DOF Hover

Author: ZhangHongLiang
Tutor: YangGuangHong
School: Northeastern University
Course: Navigation,Guidance and Control
Keywords: Genetic Algorithm Fuzzy-PID controller three freedom of degree quantitative factor and scale factor degree of membership functions control rules
CLC: V249.1
Type: Master's thesis
Year: 2009
Downloads: 47
Quote: 0
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Abstract


The 3DOF hover model system is provided by the Canadian company Quanser. Three aspects of control are included in this model system, such as control of pitch angle, control of roll angle and control of yaw angle. The 3DOF hover model system is simple and intuitionist as a laboratory setup but is complicated as a controlled unit. To itself, the 3DOF hover model system is a multi-input multi-output complex system which possesses the characteristic of high order instability, multi-variable, non-linearity and cross-coupling. In order to better control the 3DOF hover model system, it is necessary to design a more effective controller.In this paper, for the non-linearity and cross-coupling of the 3DOF hover model system, the more effective controller need to be designed, making the performance of the system greatly improved. Major jobs are as follows:First of all, components and working principle of the 3DOF hover model system are introduced. With dynamic analysis of the 3DOF hover model system, Mathematical model is established and turned into a state equation.Secondly, the basic principles of LQR optimal control are given. LQR controller is designed which is able to track and control pitch angle, roll angle and yaw angle in real-time. The simulation results show that the control strategy is feasible and effective.Thirdly, the PID parameters are optimized by the fuzzy control theory. The fuzzy self-tuning PID controller is designed. Compared to the simulation results by LQR controller, The dynamic performance of system is improved with Fuzzy PID controller.Finally, Fuzzy PID controller is designed based on Genetic Algorithm (GA) to overcome incapability of online self-adjusting in fuzzy control rules. The quantitative factors, scale factors, membership function and fuzzy control rules are optimized with The Genetic Algorithm (GA).

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CLC: > Aviation, aerospace > Aviation > Aircraft instrumentation,avionics, flight control and navigation > Flight control system and navigation > Flight control
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