In this work a design for self-tuning non-linear Fuzzy Proportional Integral
Derivative (FPID) controller is presented to control position and speed of Multiple
Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles
(AUV) to follow desired trajectories. Non-linearity that results from the
hydrodynamics and the coupled AUV dynamics makes the design of a stable
controller a very difficult task. In this study, the control scheme in a simulation
environment is validated using dynamic and kinematic equations for the AUV
model and hydrodynamic damping equations. An AUV configuration with eight
thrusters and an inverse kinematic model from a previous work is utilized in the
simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the
parameters of the PID. Nonlinear fuzzy Gaussian membership functions are
selected to give better performance and response in the non-linear system. A
control architecture with two feedback loops is designed such that the inner loop is
for velocity control and outer loop is for position control. Several test scenarios
are executed to validate the controller performance including different complex
trajectories with and without injection of ocean current disturbances. A comparison
between the proposed FPID controller and the conventional PID controller is
studied and shows that the FPID controller has a faster response to the reference
signal and more stable behavior in a disturbed non-linear environment.
Á¦ 1Æí : SIMULINK ±âº»Æí
1.1 SIMULINKÀÇ ½ÃÀÛ 1
ºí·ÏÀÇ ¿¬°á 5
ºí·Ï ÆĶó¹ÌÅÍÀÇ ¼³Á¤ 7
½Ã¹Ä·¹ÀÌ¼Ç ÆĶó¹ÌÅÍ (Configuration Parameters)ÀÇ ¼³Á¤ 8
½Ã¹Ä·¹À̼ÇÀÇ ¼öÇà 9
ºí·Ï ÆĶó¹ÌÅÍÀÇ Ç¥½Ã 9
º¹¼ö µ¥ÀÌÅÍÀÇ Ç¥½Ã 11
2.2 µ¿Àû ½Ã¹Ä·¹ÀÌ¼Ç 13
ÀÌÂ÷ ¹ÌºÐ¹æÁ¤½Ä 17
¼±Çü »óź¯¼ö ¸ðµ¨ 23
DC ¸ðÅÍÀÇ ½Ã¹Ä·¹ÀÌ¼Ç 24
ÇÔ¼ö ºí·ÏÀÇ »ç¿ë 29
Â÷ºÐ¹æÁ¤½Ä(difference equation)ÀÇ ¸ðµ¨¸µ 34
Subsystem(ºÎ½Ã½ºÅÛ)ÀÇ ±¸¼º 37
Á¦ 2Æí : ¿¬±¸³í¹®
Trajectory following and stabilization control of fully actuated AUV
using inverse kinematics and self-tuning fuzzy PID
1. Introduction 41
2. Kinematics and coordinate systems 42
3. Configuration and inverse kinematics control model 46
4. Dynamic model 49
5. Control design 58
6 Results 66
7. Conclusion 72
8. References 74