Decoupled Modelling and Controller Design for the Hybrid Autonomous Underwater Vehicle: MACO Jeff Kennedy* , Emmett Gamroth†, Colin Bradley‡ , and Alison A. Proctor§ Department of Mechanical Engineering, University of Victoria Victoria, British Columbia Garry J. Heard¶ Defence R&D Canada Atlantic Dartmouth, Nova Scotia
Abstract Researchers at the University of Victoria have developed a hybrid autonomous underwater vehicle named MACO capable of 3-D station keeping and manoeuvring without forward velocity. This makes it suitable to perform many of the tasks traditionally accomplished by remotely-piloted underwater vehicles.
Once operational, MACO was used in a Defence Research and Development Canada (DRDC) feasibility study for using AUVs to support rapid deployment of acoustic element arrays. The AUV was required to stop and hover, while triggering a low frequency sound source. The performance of MACO during these sea trials is presented as the conclusion to this discussion.
*
J. Kennedy is a research engineer at the University of Victoria E. Gamroth is a graduate student at the University of Victoria ‡ C. Bradley is a professor at the University of Victoria and Director of the Laboratory for Automation Communications and Information Systems Research (LACIR) § A. Proctor is a graduate student at the University of Victoria,
[email protected] ¶ Dr. Garry Heard is the Leader of the Rapidly Deployable Systems Group at DRDC Atlantic, Dartmouth, Nova Scotia †
1
Introduction
Unmanned underwater vehicles (UUVs) are playing an ever- increasing role in oceanic exploration. The use of manned submersibles is limited, due to the high operational cost and concerns about personal safety. Modern UUVs can be classified into two groups: Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs). ROVs are hardtethered to a surface support vessel by means of an umbilical cable, which provides a link for transferring power, communication and video between the ROV and the surface. This constrains the ROV to operate in close proximity to its support vessel.
The AUV was developed to meet the demand for long-range survey vehicles. Initially, AUVs were very large vehicles, shaped like an unmanned submarines that did not require an umbilical. These vehicles are capable of travelling hundreds of kilometres and are equipped with elaborate navigation systems (Butler and Hertog, 1993). Over the past decade, AUVs were developed for many diverse applications. Currently, they fall under four main categories: survey AUVs, gliders, micro AUVs, and hybrid AUVs. Survey AUVs are designed around an efficient torpedo-style hull with a single tailmounted propeller and hydroplanes for control. Survey AUVs are further sub-classified by size: large, medium, and small. Large survey AUVs such as the Hugin 3000 (Marthiniussen et al., 2004), Autosub (Stevenson, 1996), and Theseus (Thorleifson et al., 1997) are typically around 1m in diameter and up to 10 m long, commonly used for detailed mapping involving side scan sonar, cable laying, and pipeline tracking operations. Medium survey AUVs such as the Dorado (Sibenac et al., 2002), BPAUV (Rish et al., 2001), and Odyssey III (Damus et al., 2002), are typically around 0.5 m in diameter and 2 m long. The medium-size survey class of AUVs is used in similar applications to its larger counterparts, but typically has less range. The size reduction allows launch and recovery from a vessel of opportunity.
As the academic and scientific interest in AUVs began to grow, a new class of smaller vehicle was developed, which is even easier to deploy. Examples of two small survey AUVs are the Remus (Allen et al., 1997), shown in Figure 1, and Gavia are typically around 15 to 20 cm in diameter and just over 1 m long. Even smaller are the micro AUVs such as the Ranger (Hobson et al., 2001) and the USNA-1 (Wick and Stilwell, 2001). These vehicles are survey style AUVs around 9 cm in diameter and less than 1 m in length. These small AUVs are proving useful in areas such as mapping chemical plumes (Fletcher, 2001), military reconnaissance and mine countermeasures (Stokey et al., 2001), search and rescue, and profiling the water column.
Gliders are designed to travel up and down through the water column over enormous distances. These vehicles are quite distinct from typical underwater vehicles in that they use buoyancy engines and ballast shifting to manoeuvre (Sherman et al., 2001; Eriksen et al., 2001; Webb et al., 2001). Typically, they are around 15 cm in diameter and up to 2 m long with hull- or tail- mounted wings of fixed attack angle.
Some emerging applications require a vehicle to act as both an AUV and ROV, combining computer control and hydrodynamic performance with 3-D station keeping, during the course of a mission. The standard AUV, like that shown in Figure 1, cannot meet these requirements as it requires forward velocity for control and steering. Hybrid AUVs were developed to facilitate these mission profiles. Some existing hybrid AUV are typified by vehicles such as Cetus (Trimble, 1998), Swimmer (Evans et al., 2001), Alive (Evans et al., 2003), and the Seabed (Singh et al., 1996).
This paper presents the design and testing of the autonomous underwater vehicle MACO, shown in Figure 2, at the University of Victoria (UVic). Once operational, MACO was utilised by Defence Research and Development Canada (DRDC) to investigate the feasibility of using an AUV to support rapid deployment of acoustic element arrays for array element localisation. The ability of MACO to stop and hover while triggering a low frequency sound source made it well suited to this task. The performance of the vehicle during the sea trials is presented in the final section of the paper.
2
Vehicle
The task of set out by DRDC required an AUV capable of transporting an acoustic sounding device along a specific trajectory and periodically coming to a full stop and hovering to trigger the acoustic projector. Since the trajectories involved were typically 1 km or longer, a hydrodynamically efficient vehicle with the ability to hover was necessary.
2.1
Design Specifications
The necessary payload and typical mission characteristics were provided by DRDC, which led to the design specifications presented in Table 1. Table 1 Design Specifications from the DRDC performance requirements. Performance Specification Requirement Maximum Speed 1.5 m/s Operating Depth 60 m Endurance Propulsion:2.5
[email protected] m/s Computer: 10 hrs Mission Length 1500 m Positional Accuracy (x y) ±5% mission length Positional Accuracy (z) ±0.5 m Payload Capacity Weight: 8.5 kg dry, 4 kg wet Size: 0.5 m long, 13 cm dia. Special Requirements Hovering without fwd velocity Turning without fwd velocity
2.2
Mechanical Design
The mechanical layout of MACO is shown in Figure 3. The overall ve hicle geometry is based on the key requirement for hovering and turning without forward velocity. MACO has a hybrid vehicle design that borrows attributes from both the traditional ROV and the AUV, as described in Section 1, to meet its functional requirements. MACO uses two vertical thrusters to control the pitch and depth, and two horizontal thrusters to control forward velocity and yaw. To minimise drag, MACO has a long slender hydrodynamic body with a 9:1 length-towidth ratio. Furthermore, the vertical thrusters are contained completely within the hull profile and the horizontal thrusters are located adjacent to the hull to minimise parasitic drag while still providing adequate turning capability.
MACO is constructed from PVC throughout with the exception of anodised aluminium pressure case end caps and thruster brackets. The instrument housing provides a one atmosphere environment for computer equipment, electronics, batteries, speed controllers, etc. The wet hull has removable side panels which improve the hydrodynamics while providing access to flotation and payload bays. The bulkheads structurally connect the pressure casings and provide mounting for the thrusters and other peripheral devices.
The control software runs on a PC-104 stack equipped with Ethernet, digital and analog I/O, and a hard drive for data storage. While on the surface the vehicle can receive GPS data and has an 11Mbs Ethernet data link for telemetry and communications with the ground control station (GCS). The vehicle was equipped with the following sensors: •
SeaMetrics TX-80 turbine flow meter for measuring forward velocity
•
BEI GyroChip Horizon Micro-Electro-Mechanical Systems (MEMS) rate gyro for measuring the angular rate in yaw
•
Honeywell HMS-3000 digital compass for measuring heading as well as pitch and roll angles
•
WIKA D-10 Pressure Transmitter for measuring depth
•
RoyalTek REB-12R GPS for measuring position on the surface
3
Ground Control Station (GCS)
The GCS runs on a laptop with a Windows operating system and connects to the vehicle using Ethernet. The graphical user interface (GUI) was created using virtual instruments in LabVIEW 6i. The GUI allows the operator to remotely operate the vehicle and to set lowlevel controller parameters. Additionally, it provides an interface for real- time sensor monitoring.
As a sample, Figure 4 shows the ROV control screen of the user interface. While in ROV mode, MACO can be controlled by a handheld joystick or by using the GUI pulse control. The GUI has the following two main modes: •
AUV Mode: This is used to upload and initiate mission and initialisation script files.
•
ROV Mode: Using this, MACO can be controlled at depth while tethered or on the surface via wireless modem.
3.1
Mission Management
A cross-platform scripting language called Lua 5.0.2 was used for uploading missions and initialising the control system. Lua is implemented as a small library of C functions, written in ANSI C. Lua can be imbedded into an application creating a simple communication interface, which allows a series of commands listed in a text file to be executed in a freeflowing sequence or with completion flag verification between commands.
4
System Modelling
A solid model of the vehicle was created using CAD software. This provided most of the static model characteristics like mass and inertia. The values derived from the solid model were then adjusted using water tests. In addition, it was necessary to derive a dynamic model of the thrusters, since they typically have non-trivial dynamics.
4.1
Thruster Characterisation
In order to adequately characterize the thrusters, both the steady-state and transient responses of the entire system were investigated. This was done using a custom apparatus which employed a strain-gage-based force scale connected to the AUV with low friction pulleys and cord (Kennedy, 2005). The apparatus was secured to the poolside and the AUV was controlled remotely using the tether and graphical user interface.
The objective of the steady-state output testing was to determine the thrust delivered by the thruster motor in response to a steady-state input to the driver. Inputs between 10% and 100% of the input range were investigated to generate a linear input-output relationship, and to determine the maximum thrust output in each configuration. A linear input-output relationship for the thruster simplifies the controller.
Figure 5-a contains the results from the steady-state testing. The thrusters have a dead zone, in the first 15% of the input range, and the thrust is negligible until the input is 20% of maximum. This dead zone is primarily caused by the friction of the shaft seals. In order to generate a linear input-output map, the percent input from 20% to 100% was plotted as a function of the output thrust and fit with 6th order polynomial. The steady state thruster output before and after the linearisation procedure is shown in Figure 5-a. The transient response is shown in Figure 5-b.
4.2
Vehicle Characterisation
Velocity step response data was collected for: surge, yaw, heave, and pitch. In additio n, a test was performed to determine the restoring moment on the pitch axis. Since the drag equals thrust in the steady state condition, it is possible to obtain velocity-drag profiles from the steady state velocities for known thrusts. In addition, the transient response can be used to validate the model.
4.2.1
Surge
The steady-state velocity in surge was recorded for open-loop linearised thrust levels ranging from 10% to 100%. The results are plotted in Figure 6 as two sets of five data curves for clarity. The steady-state velocity is obtained from the flow meter and by taking the slope of the steady-state portion of the displacement plots. Then using the corresponding thrust levels, the graph showing drag vs. velocity was generated, shown in Figure 7. Since the drag is expected to have a quadratic relationship to velocity, the data in Figure 7 was fit with a second order polynomial. The resulting parameterisation was:
D S = B1x Vx2 + B2 x Vx = 35.92 Vx2 + 9.19 Vx
(1)
where DS is the drag in Newtons and Vx is the horizontal velocity in m/s. Therefore, the equation of motion governing the surge dynamics can be derived from the balance of forces in the x direction.
M& x&= −B1 x x&x&− B 2 x x&− Fx
(2)
where M is the mass of the vehicle, Fx is the horizontal thruster force, and B1x and B2x are the drag coefficients from Equation 1.
4.2.2
Heave
The AUV is not equipped with a velocity sensor in the heave direction. As a result, the position (depth) response was recorded for open-loop thrust levels ranging from 10% to
100%. The slope of the steady-state segment of each data set was taken as the steady state velocity. Then using this velocity data and the corresponding open- loop thrust levels, the drag- velocity relationship was constructed as:
D H = B1z Vz2 + B 2 z Vz = 170.09Vz2 + 26.781Vz
(3)
where DH is the drag in Newtons and Vz is the vertical velocity in m/s. Therefore, the equation of motion governing the heave dynamics can be derived from the balance of forces in the z direction.
M& z&= −B1z z&z&− B 2 z z&− Fz + C B
(4)
where M is the vehicle mass, Fz is the thruster force in the z-axis, and B1z and B2z are the drag coefficients from Equation 3. In addition to the thrust and drag forces, the heave dynamics also include an additional buoyancy force, CB. This force accounts for the payload dependent buoyancy force.
4.2.3
Pitch
As with heave, the AUV is not equipped with a sensor to measure angular velocity in pitch. So again, the angular position response was recorded for open- loop thrust levels ranging from 10% to 80%, and the slope of the steady-state segment of each data set was taken as the steady-state velocity. Using this velocity data and the corresponding open-loop thrust levels, the drag-velocity relationship was constructed as: D ? = B1 ? ?&2 + B 2 ? ?& = 0.0072 ?&2 + 0.3486 ?& where D? is the angular drag in Nm and ?& is the angular velocity in deg/s.
(5)
Unlike the other DOFs, the pitch axis has a restoring moment that acts to oppose an increasing pitching angle. As a result, the velocity does not reach steady state. The drag relationship in Equation 5 provides an approximation to the actual AUV drag characteristics. The linear term dominates the function, which suggests a strong influence from the restoring moment. The restoring moment is caused by the opposing forces of the centre of buoyancy and centre of gravity acting distant to each other. To determine the restoring moment coefficient, the steady-state pitch angles were measured for six open-loop thrust levels ranging from 10% to 60%, shown in Figure 8. Using the pitch angle from Figure 8 and the applied Torque, a relationship between the torque and the restoring moment was obtained as:
TR = 0.63sin (? )
(6)
where TR is the restoring moment in Nm and ? is the pitch angle in degrees.
Therefore, the equatio n of motion governing the pitching dynamics can be derived from the balance of moments about the y-axis. J y& ?&= −B1? ?&?&− B 2? ?&+ T? + C F x&x&− C R sin (? )
(7)
where Jy , which was determined to be 0.49 kg-m2 , is the inertia about the y-axis. T? is the thruster torque about the y-axis, and B1? and B2? are the drag coefficients from Equation 5. CR is the restoring moment coefficients from Equation 6. The final contribution to the pitching dynamics comes from the asymmetric drag force which arises from the tower mounted atop the vehicle. This drag coefficient is denoted by CF.
4.2.4
Yaw
The steady-state angular velocities in yaw were recorded for open- loop torque levels ranging from 10% to 100%. There was some noise added to the measurements due to coupling between the yaw and the natural rolling motion due to water movement. Therefore, as was done with the surge data, the steady-state angular velocity was confirmed by taking the slope
of the steady-state portion of the corresponding heading output from the compass (which is roll compensated). The resulting drag equation as D ? = B1 ? ?&2 + B2 ? ?& = 0.0149 ?&2 + 0.0857 ?&
(8)
where D? is the angular drag in Nm and ?& is the angular velocity in deg/s.
Therefore, the equation of motion governing the yawing dynamics can be derived from the balance of moments about the z-axis. &= − B1 ? ?&?&− B 2 ? ?&+ T? J z ?&
(9)
where Jz, which was determined to be 0.40 kg-m2 , is the inertia of the vehicle about the zaxis, T? is the thruster torque about the z-axis, and B1? and B2? are the drag coefficients from Equation 8.
5
Control System
Simulink is a simulation platform for dynamic systems produced by MathWorks; it provides an interactive graphical environment as well as a customisable set of block libraries which facilitate design, simulation, implementation, and testing of controllers and many other timevarying systems. This software was used to test the control system and to validate the model, described in Section 4, against experimental data. In the Simulink simulation, sampling periods, quantisation, acquisition and communication delay, and noise associated with the discrete hardware components were also taken into account. The details of these models are not presented here but are available in (Kennedy, 2005).
The closed- loop controllers were designed using Simulink. The system models described in Section 4 were put into Simulink along with models of all the discrete components. The controller design for each DOF was developed by selecting a general controller type and then
individually modelled components were assembled to form a closed-loop system. This model was used to tune the controller gains prior to closing the loops with the vehicle in the water. The feed forward gains were obtained using the open loop velocity versus thrust data. The procedure for finding the PID controller gains followed these basic steps: •
The set point was set to the intended operating va lue.
•
With KD and KI set to zero, KP was increased until the system began to oscillate.
•
KP was then reduced by approximately 30%.
•
With KP fixed at 30% of maximum, the set point was given 5% positive and negative step changes, which again produced oscillations.
•
KD was increased until the system was, at a minimum, critically damped and then KD was fixed.
•
With KD and KP fixed, KI was increased until oscillations began again. This was considered the upper limit for KI.
•
KI was set to the minimum required value to satisfactorily reduce the steady state error, to a maximum of half of the upper limit determined in the previous step.
Since the characteristics of the systems were accurately modelled, including latency and descretisation, the control gains derived by this method resulted in reasonable performance by the vehicle when the loops were closed. The final step was to fine tune the gains in the water by tracking the responses using real-time graphs generated in the GCS.
5.1
Surge Controller
The AUV is expected to achieve a desired velocity quickly and then maintain that velocity for long periods and must be able to stop quickly with zero overshoot. Since the AUV cannot sense reverse velocity (due to the unidirectional flow meter), the controller would consider the zero crossing caused by a negative overshoot to be a full stop; as a result, the AUV would
continue to drift backwards uncontrollably. A PD controller, with a feed forward term, was chosen to control the forward velocity.
A block diagram of the surge controller is shown in Figure 9. An estimate of the position,
xˆ , is obtained by integrating the output of the flow meter. The desired velocity, vd, is proportional to the error between the desired position, xd, and the estimated position. Prior to being fed into the velocity control loop, the desired velocity is limited to ensure that it does not exceed the capabilities of the vehicle.
The upper and lower velocity limits were chosen to be 0.7 m/s and 0 m/s respectively. This is less than the maximum speed, but increases the battery life. The feed- forward component is the quadratic portion of drag at the desired velocity, calcula ted using the drag model from Equation 1. Based on the simulation results, the controller gains were chosen to be KP = 150, KD = 20, and KE = 0.2.
5.2
Heave Controller
Slight overshoot is acceptable on the heave axis, provided the vehicle achieves a steady state condition relatively quickly. To meet these requirements, a PID controller with a strong derivative term was chosen. The derivative term minimises the overshoot and oscillations and the integrator eliminates steady-state error. A block diagram of the heave controller is shown in Figure 10. An estimate of the position, zˆ , is obtained from the pressure transmitter, and the desired position, zd, is obtained from the trajectory. Based on the simulation results, the controller gains were chosen to be KP = 200, KD = 300, and KI = 0.1.
5.3
Pitch Controller
Feedback control for the pitch uses the output of the level sensor contained in the compass.
This sensor, while adequate, has significant latency, limiting the potential controller performance. Since oscillations in pitch can adversely affect the accuracy of the heading measurement from the compass, the bandwidth of the pitch control needs to be relatively large to ensure stability of the vehicle. A derivative controller was chosen, since the vehicle's inherent restoring moment provided sufficient proportional component. A block diagram of
) the pitch controller is shown in Figure 11. An estimate of the attitude, ? , is obtained from the level sensor in the compass, and the desired angle, ? d, is zero.
5.4
Yaw Controller
For heading manoeuvres, it is desirable to reach a fixed low angular rate and maintain it until the desired heading is reached. This allows the vehicle to do a steady pan motion. A PD controller with a feed forward term was chosen to control the angular velocity. A block diagram of the yaw controller is shown in Figure 12. The estimated attitude, ?ˆ , is obtained from the compass, and the estimated angular velocity, ?ˆ&, is obtained from the rate gyro. The desired angular velocity, ?&d , is proportional to the error between the desired heading, ? d, and the estimated heading. Prior to being fed into the velocity control loop the desired velocity is limited to the desired angular rate. The feed-forward component is the quadratic portion of the drag at the desired angular velocity, calculated using the drag model from Equation 8. The angular velocity limit was chosen to be ±10°/sec. Based on the simulation results, the controller gains were chosen to be KE = 0.225, KP = 5, and KD = 5.
5.5
Experimental Results
In order to validate the system models and subsequent controllers, experimental results were compared with simulated results for the same scenario. To verify the performance of the surge controller, the vehicle was commanded to travel 10 m at 0.7 m/s without overshoot. Similarly, for the heave controller, the AUV was required to begin at the surface, then dive to
a depth of 5 m and maintain a constant depth. The experimental and simulated test results are shown in Figure 13-a,b and Figure 13-c respectively.
The actual and predicted response curves fo r the surge and heave controllers, shown in Figure 13, are in good agreement. The surge displacement and velocity profiles are shown in Figure 13-a and b. The experimental velocity is slightly lower than the 0.7 m/s cruising velocity that was specified, which may be due to the feed forward function or the battery charge level. The heave controller provided a stable response with no oscillations and a constant diving velocity of approximately 0.5 m/s.
To test the pitch controller, the AUV was held at a pitch angle of 20° using open-loop thruster control, and then released. Immediately after the release, the differential controller was turned on to dampen the oscillations. In this scenario, the open- loop response of the plant was observed to take more than 40 seconds to decay to within 5% of the steady state solution. This settling time was improved to less than 7 seconds with the derivative controller. To test the yaw control, the AUV was required to achieve and maintain an angular velocity of 15°/sec, then stop and hold at 0°/sec. The actual and predicted response curves, shown in Figure 14, agree with well. In the pitch controller plot, Figure 14-a, the amplitudes match, but the model predicted a slightly lower frequency of oscillation. The yaw controller, Figure 14b, shows that the AUV undershot the set point angular velocity by approximately 1°/sec. These errors are likely due to either the feed forward function or a low battery charge level.
6
Results
The maiden field tests for the vehicle described in this paper are discussed below. Dead reckoning navigation was used to determine the commanded trajectories, but only the surge component of velocity with respect to the water was known. Despite this, the position
estimate obtained using this simple technique was amazingly accurate given the adversities presented by the underwater environment. Two sets of field tests are presented here. •
Elk Lake trial at a depth of 5 m.
•
Halifax sea trials for DRDC.
6.1
Lake Tests
The Elk Lake trials were performed to ensure that the AUV systems were operating properly prior to deployment in the ocean. The vehicle was commanded to perform an 850 m long raster search pattern at a 5 m depth. At each corner point, the vehicle was required to surface to obtain a GPS fix. Unfortunately, the GPS was unable to reacquire a signal during the 20 s it spent at each surfacing point, and the AUV only logged the start and finish positions. The overall position error during the mission was 8.8 m or approximately 1% of the total mission length.
6.2
Sea Trials in Halifax, Nova Scotia
The DRDC sea trial in Halifax, NS was MACOs first open ocean test. The purpose was to determine the feasibility of using an AUV to support rapid deployment of acoustic element arrays. The total mission length was 1.42 km, cons isting of a rectangle followed by a cornerto-corner diagonal. To minimise the effect of the current, the mission was performed during slack tide. Even so, the small current that was present caused a net drift in the trajectory. It should be noted that the data presented has been rotated by -6° to account for a hard- iron calibration problem with the compass. The maximum position error during the mission was 134 m or 9.5% of the mission length. The trajectory from the trials is shown in Figure 15.
7
Conclusions and Future Work
The overall development of the AUV MACO was successful. The experimental methods for acquiring parameters proved to be a very effective means to generate the quadratic drag curves used in the dynamic model. The decoupled approach to AUV modelling and controller
design is well suited to hybrid AUVs. It simplifies the problem greatly and the evaluations in Section 6 validate the effectiveness of this method.
MACOs small size made it an ideal candidate for integration with a rapid deployment array element localisation system. The use of AUV technology definitely enhances operations such as those conducted by DRDC and its low cost makes MACO an attractive asset.
7.1
Future Work
Based on the development process of MACO and the operations during the lake and sea trials, the following modifications would greatly improve the modelling process and navigational accuracy: •
Thrust prediction using velocity feedback on thrusters.
•
Amalgamation of the open- loop characterisation scripts into a single script file followed by first-pass automated data processing.
•
Incorporation of a second gyro to measure pitch rate.
•
In both pitch and yaw, the gyro rate output should be integrated to give pitch and heading and be reset periodically with the absolute sensors (tilt and compass).
•
Incorporation of linear accelerometers aligned with surge and heave to aid in modelling.
•
Inclusion of a quadratic and a linear term in all feed forward functions.
•
Development and implementation of navigation algorithms using GPS correction and global drift estimation.
Nomenclature Roman letters
B2
Linear Drag Coefficient
B1
D
Drag Force (N)
Quadratic Drag Coefficient
F
Thruster Force
UVic University of Victoria
J
vehicle Inertia (kg m3)
M
vehicle Mass (kg)
Greek letters
T
Thruster Torque
d
Control command signal
V
Velocity (m/s)
f
Roll angle (rad)
x
horizontal forward position (m)
?
Yaw angle (rad)
y
horizontal sideward position (m)
?
Pitch angle (rad)
z
vertical position (m)
AUV Autonomous Underwater Vehicle
Subscripts and Superscripts
DOF
Degree of Freedom
^
Estimated
GCS
Ground Control Station
?
Yaw
GNC Guidance, Navigation, and Control
?
Pitch
GPS
Global Positioning System
B
Buoyancy
GUI
Graphical User Interface
H
Heave
R
Restoring moment
S
Surge
ROV remotely
operated
underwater
vehicle UUV Unmanned Underwater Vehicle
Acknowledgments The authors would like to thank Rodney Katz, Kevin Jones, and Pan Agathoklis from UVic for the time they dedicated to developing the AUV.
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List of Figures Figure 1: The REMUS AUV being manually deployed from a small vessel Figure 2: Side profile of MACO before deployment at sea Figure 3: Mechanical component layout of MACO Figure 4: The graphical user interface shown in ROV mode, which allows manual control of MACO Figure 5: Thrusters a) Steady-state thruster output before and after linearisation b) Transient response of the thruster to a 100% positive to negative input swing. Figure 6: Velocity step responses at 10 constant thrust levels. Figure 7: The drag-velocity relationship in surge. Figure 8: Steady-state pitch angle at six constant input torque levels. Figure 9: Block Diagram of the Surge Controller Figure 10: Block Diagram of the Heave Controller Figure 11: Block Diagram of the Pitch Controller Figure 12: Block Diagram of the Yaw Controller Figure 13: Comparison between experimental and simulated results for the surge and heave controllers a) Surge Displacement b) Surge Velocity c) Heave Displacement Figure 14: Comparison between experimental and simulated results for the pitch and yaw controllers a) pitch controller b) yaw controller Figure 15: GPS data from the Halifax mission
