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

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

 PI control schemes  The artificial neural network technique

A-1. Electrical Governing Equation:

A-2. Mechanical Governing Equation:

B. Neural-Network-Based Friction Model

C. Complete Model of the PMAC Motor

D. PMAC Motor PI Control

A. Controller Structure

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

C. System Integration

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

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

 Neural-Network-Based Self-Tuning PI Control:

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

 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).