1. 控制原理與設計期中報告 指導教授：曾慶耀 學 號： 10167030 學 生：楊長諺

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

3.  PI control schemes  The artificial neural network technique

4. A-1. Electrical Governing Equation:

5. A-2. Mechanical Governing Equation:

6. B. Neural-Network-Based Friction Model

7. C. Complete Model of the PMAC Motor

9. D. PMAC Motor PI Control

10. A. Controller Structure

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

12. C. System Integration

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

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

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

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

20.  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).