臺灣博碩士論文加值系統

本論文主要是建構一可平衡之二輪平衡機器人，在軟體上模擬二輪平衡機器人之數學模型在使用 PID 及 Fuzzy控制器是否可以穩定，並實際建構二輪平衡機器人進行控制實驗。在二輪平衡機器人平台上整合了鋰電池、穩壓器、微控制器、編碼器、Inertial Measurement Unit (IMU)、馬達驅動板、直流減速馬達、無線模組等部件，藉由讀取 IMU 的加速度計與陀螺儀的感測值進行感測器融合，以取得傾斜角度資訊，並把proportional–integral–derivative (PID) 控制器實作在微控制器上對二輪平衡機器人進行控制，使二輪平衡機器人可以無需藉由外部的控制訊號及可自主平衡。在感測器融合的部分，使用低成本 MPU-6500 內的3 軸加速度計與3軸陀螺儀進行多種感測器融合方法來量測傾斜角度，並使用 PID控制器進行二輪平衡機器人之平衡控制，觀測在不同感測器融合方法下對 PID 控制器控制的影響。

This study is mainly to build a two-wheeled balancing robot. The first task is to simulate the results of using two-wheeled robot mathematical model under PID control or Fuzzy control, then build to a real two-wheeled balancing robot. Two-wheeled balancing robot integrates Lithium polymer battery, converter, microcontroller unit, encoder, Inertial Measurement Unit (IMU), dc motor driver, DC geared motor, wireless module, etc. On the two-wheeled balancing robot platform, the tilt angle is obtained by reading the accelerometer and the gyroscope measurement and fuse the sensor measurement and then implement the proportional-integral-derivative (PID) controller to control the two-wheeled balancing robot on a microcontroller unit. The two-wheeled balancing robot can independently balance without external control signal or support. In the sensor fusion part, a three-axis accelerometer and a three-axis gyroscope are integrated in a low-cost MPU-6500, it is used to perform a variety of sensor fusion methods to measure the tilt angle, and it will be the feedback of PID controller, and according tilt angle controller will drive the motor to balance the two-wheeled balancing robot. Finally observe the influence of different sensor fusion methods on controller behavior.

摘要 i
Abstract ii
致謝 iii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Literature Review 2
1.3 Organization of the Thesis 3
Chapter 2 Two-wheeled Balancing Robot 4
2.1 Introduction of Two-wheeled Balancing Robot 4
2.2 Mathematical Model of Two-Wheeled Balancing Robot 5
Chapter 3 Measurements and Control method 14
3.1 Sensor Fusion 14
3.1.1 Introduction of sensor fusion 14
3.1.2 Complementary Filter 14
3.1.3 Kalman Filter 16
3.1.4 Mahony Filter 19
3.2 Control Method 21
3.2.1 PID Control 21
3.2.2 Fuzzy Control 24
Chapter 4 Hardware of Two-Wheeled Balancing Robot 26
4.1 Microcontroller Unit 28
4.2 Inertial Measurement Unit 30
4.3 DC Geared Motor 31
4.4 DC Motor Driver 32
4.5 Li-Po battery 33
4.6 DC-DC Converter 35
4.7 Experiment environment 36
Chapter 5 Experiment Results 37
5.1 Results of Simulation 37
5.2 Results of Sensor Fusion 42
Chapter 6 Conclusions and Future Work 45
6.1 Conclusions 45
6.2 Future Work 46
Reference 47

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