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控制原理與設計期中報告 指導教授：曾慶耀 學 號： 學 生：楊長諺

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Introduction System Modeling of the PMAC Motor Neural - Network - Based Self - Tuning PI Control System for PMAC Motors Experiments and Discussions Conclusion

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PI control schemes The artificial neural network technique

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A-1. Electrical Governing Equation:

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A-2. Mechanical Governing Equation:

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B. Neural-Network-Based Friction Model

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C. Complete Model of the PMAC Motor

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D. PMAC Motor PI Control

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A. Controller Structure

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B. NNPT Training k 1 =100 k 2 =5 k 3 =100

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C. System Integration

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D. Computer Simulations 1) Self-Tuning PI Control versus Fixed-Gain PI Control:

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2)Self-Tuning PI Control versus Gain-Scheduling PI Control:

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Neural-Network-Based Self-Tuning PI Control:

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Neural-Network-Based Self-Tuning PI versus Fixed- Gain

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In this paper, a new neural-network-based self-tuning PI controller design method was proposed to increase the robustness of the conventional fixed-gain PI control scheme. a well-trained neural network supplies the PI controller with suitable gain according to each feedback operating condition pair (torque, angular velocity, position error).

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