호버형 AUV의 경로추적을 위한 제어기 설계 및 실해상 실험에 관한 연구. 호버형 무인잠수정의 경로추적을 위한 제어기 설계 및 실해상 실험에 관한 연구. 한국해양대학교 해양과학기술대학원.
복 하이을을 최동호의 공학석사 학위노문원 인준함. AUVs are deliberately different, but among them, Sovering-type AUV can perform jobs by keeping its position and posture underwater. Before field test, 6DOF equations of motion are developed, simulation program is compiled using Matlab/Simulink, and essential motion performance of designed vehicle is verified.
Furthermore, the PID controller and Fuzzy PID controller are designed to perform their missions, and the performance of the controllers is verified by simulation. Field tests are conducted to verify the AUV's motion performance, and waypoint tracking is performed by the PID and PID Fuzzy controller for the vehicle.
INTRODUCTION
The ROV is remotely operated by the user and has a command ship and cable for data, control and power. It also has a computer and various sensors inside because it is not controlled by a human. According to the cruising distance for the mission, AUVs are divided into cruising AUVs and hovering AUVs.
Unlike the Cruising-type AUV that navigates a wide range of water and explores the landform on the seabed, the Hovering-type AUV is used to inspect the bottom of a ship or underwater structure. Attitude control and position control function is important for Hovering type AUV for accurate work under water, so accurate thruster control is needed. The early type Hovering AUV is the University of Hawaii's 'ODIN' and consists of a pressure vessel that has a spherical shape of 0.64 m in diameter.
As in Figure 1, six degrees of freedom of movement are controlled by four vertical propellers and four horizontal propellers to maintain the underwater position. Tri-Dog1', developed in 1999 by the Ura Laboratory of the University of Tokyo in Japan, is an approximately two-meter-long hovering type AUV.
HOVERING-TYPE AUV’S HARDWARE & SENSORS
Sensors
The state value of the UUV cannot be measured by the sensor for the UUV to execute the given order. A pressure sensor is used to measure depth, GPS and DVL to measure the position of the UUV, and TCM-3 to measure the direction of the UUV. This sensor can measure a range of 0.5 bar (5m) and output 1V ~ 5V according to the water depth.
The sensor model used is TCM3 from PNI company and it provides 3-axis angle information by receiving a 5V input power source. The measured accuracy of the roll, pitch and yaw angles is 0.5 in stop mode and the sensor resolution is 0.1. The accuracy of the sensor is within 1m and it can be connected to PC directly because it is USB type.
DVL is a device to measure the relative speed of the body using Doppler effect of sound wave reflected from the surface of the sea and the seabed. In this paper, DVL is used instead of GPS sensor, which cannot be used in the water, to get some information about the UUV's speed and to find out the movement distance. DAQ board is a device that changes the input analog signal to digital signal and makes the computer decode this signal.
DAQ device for signal measurement consists of ADC (analog-digital converter) and computer bus. Analog signal from sensor needs to be changed to digital signal before manipulating digital device, ADC is a chip that plays this role. Computer bus functions as a communication interface between DAQ device and computer to deliver measured data.
8 is NI USB-6009 DAQ board from the National Instruments company, and it has eight analog input ports and provides bus with power supply by USB power supply. It is used to get measured value of pressure sensor, maximum voltage range of analog input is –10V ~ 10V, and accuracy is 7.73mV. When designing UUV, in order to improve usability, the size of pressure vessel was minimized, and the PC board used was mini-ITX board.
AUV’S MATHEMATICAL MODEL & DESIGN CONTROLLER
Design controller
- Design PID controller
- Design Fuzzy PID controller
Simulation results
Before tank experiments and field tests, the performance of the designed controller should be verified under different conditions. So, the control performance simulation is carried out using the designed controller and motion equation in chapter 3.1. Because the AUV flight type was needed to reach the target point through waypoints to explore certain areas, the target heading angle was set to use the LOS (Line Of Sight) method, and simulation was also performed to verify performance of the designed PID and fuzzy PID controller.
As the AUV passed each target point and changed course angle to the next target point, it was moved back by the current, but it was confirmable that all two controllers agreed with the given target points. AUV moved 1 meter underwater from the first position (0,0,0), found the heading angle of the next target point and then continued moving. On October 21, the AUV moved to the target depth 1 meter underwater, maintained the depth and floated on the water surface after reaching the final waypoint.
Because depth directional movement and y-axis rotation influence each other, the pitch angle must be controlled in AUV's movement. In this paper, considering the gliding type AUV's characteristics, pitch angle was set to 0° when AUV moved in the depth direction. 24 is AUV's heading angle during finding given target point, at this time AUV's target heading angle is determined using the LOS method.
The results of simulation showed that PID controller and Fuzzy PID controller had similar control performance. The results also showed that two controllers performed waypoint control well under current disturbance. However, because modeling error and value of hydrodynamic coefficient are different from that of Octagon, control performance should be verified by field tests.
FIELD TESTS
Depth control
In order to identify the control performance, regardless of whether the AUV follows the target depth or not, depth control was performed. Target depth set up 1m and 40 seconds later AUV floating to the water surface was confirmed. The AUV had an overshoot at 4 seconds due to inertia, later it converged to the target depth at about 10 seconds.
The reason for the value of the depth to fluctuate is disturbance applied to the AUV or change of input value of the thruster. The depth controller was designed to converge on error range within ±5 cm, and like below the graph, the AUV had errors within ±3 cm, so the performance of the controller was considered superior. The most important thing in waypoint control is to find the target waypoint as having little error.
The initial heading angle of the AUV was about – 140°, and 3 seconds later it converged to the target heading angle of 0°. As the graph below, the steering error range was within ±3°, so the performance of the controller was considered superior.
Way-point control
Fuzzy PID control
- Depth control
- Heading control
- way-point control
When the membership function was composed, one of several methods was used, the trapezoid shape, and the fuzzy rule was compiled using the and/or method. The AUV tracked the target depth well within about ±2 cm, and through this, the performance of the depth controller was verified by applying Fuzzy theory. The membership function of the input variable used in the Fuzzy head controller was divided into 5 sections as Fig.
38 is results graph of moving forward for about 50 seconds and performing heading control using designed Fuzzy PID controller. Experiments started with AUV pointing to about -120° heading angle, and 10 seconds later it tracked target heading angle 0° as an error within ±3°. Performance of Fuzzy PID controller was verified as AUV converges on target error range within ±5°.
Performance of Fuzzy PID controller was verified by results of depth control and rate control, and waypoint control was performed as Fig. Position of (0, 0, 1) was called WP1, and path that passed by each WP and returned to original position was plotted. 40 is the graph of AUV's 3D path, and it shows that at 1m underwater, AUV moves in the direction of the foam, and that it floats to the surface of the water after passing the waypoint to maintain the depth.
41 is the depth control result graph, and AUV tracks the target depth of 1 m with an error of about ± 3 cm. 42 is the heading control result graph, and AUV tracks the target heading angle from the initial heading angle of approximately 50°. It is better than the results of using the PID controller, but not better than the results of 4.2.2 heading control.
This was because depth control and course control experiments were carried out in the inner harbor where there was no current, and waypoint control experiments were carried out in the place heavily affected by current. When waypoint control experiments were carried out under current conditions, it had about ±10° error, which was larger than the inner harbor experiment error. 43 is the graph of the gain value used for depth control and steering angle control through the designed Fuzzy PID controller.
CONCLUSION
앞으로는 정확한 경로를 측정할 수 있는 필터를 설계하고 알고리즘도 개발해야 합니다. 홍승민, "호버링형 AUV의 자율항법 시스템에 관한 연구", 한국해양대학교 신소재공학부 석사학위 논문, 2015. 제5차 WSEAS 학회 응용 컴퓨팅 학회 및 제1차 학회 논문집 생물학적 영감을 받은 계산에 관한 국제 회의.
