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Neural-Network-Based Self-Tuning PI Controller forPrecise Motion Control of PMAC Motors

Neural-Network-Based Self-Tuning PI Controller forPrecise Motion Control of PMAC Motors. 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. Outline. Introduction System Modeling of the PMAC Motor Neural - Network - Based Self - Tuning PI Control System for PMAC Motors

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Neural-Network-Based Self-Tuning PI Controller forPrecise Motion Control of PMAC Motors

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  1. Neural-Network-Based Self-Tuning PI Controller forPrecise Motion Control of PMAC Motors 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. Outline • Introduction • System Modeling of the PMAC Motor • Neural - Network - Based Self - Tuning PI Control System for PMAC Motors • Experiments and Discussions • Conclusion

  3. Introduction • PI control schemes • The artificial neural network technique

  4. System Modeling of the PMAC Motor(1/6) A-1. Electrical Governing Equation:

  5. System Modeling of the PMAC Motor(2/6) A-2. Mechanical Governing Equation:

  6. System Modeling of the PMAC Motor(3/6) B. Neural-Network-Based Friction Model

  7. System Modeling of the PMAC Motor(4/6) C. Complete Model of the PMAC Motor

  8. System Modeling of the PMAC Motor(5/6)

  9. System Modeling of the PMAC Motor(6/6) D. PMAC Motor PI Control

  10. Neural - Network - Based Self - Tuning PI Control System for PMAC Motors (1/5) A. Controller Structure

  11. Neural - Network - Based Self - Tuning PI Control System for PMAC Motors (2/5) B. NNPT Training k1=100 k2=5 k3=100

  12. Neural - Network - Based Self - Tuning PI Control System for PMAC Motors (3/5) C. System Integration

  13. Neural - Network - Based Self - Tuning PI Control System for PMAC Motors (4/5) D. Computer Simulations 1) Self-Tuning PI Control versus Fixed-Gain PI Control:

  14. Neural - Network - Based Self - Tuning PI Control System for PMAC Motors (5/5) 2) Self-Tuning PI Control versus Gain-Scheduling PI Control:

  15. Experiments and Discussions(1/5)

  16. Experiments and Discussions (2/5) • Neural-Network-Based Self-Tuning PI Control:

  17. Experiments and Discussions (3/5)

  18. Experiments and Discussions (4/5)

  19. Experiments and Discussions (5/5) • Neural-Network-Based Self-Tuning PI versus Fixed-Gain

  20. Conclusion • 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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