The use of Remotely Operated Vehicles (ROVs) in exploring and building infrastructure in the ocean is expanding. ROVs are performing tasks underwater that would be difficult or impossible to do with human divers. These vehicles are being used in increasingly complicated and demanding environments that require improvements in the methods for controlling these vehicles. Currently, research into semi-autonomous control is being conducted to aide ROV pilots in compensating for environmental disturbances and unknown dynamics. To effectively implement semi-autonomous control, precise thrust forces must be elicited from the thrusters. This work discusses a low-level thruster controller that can be used as part of a semi- autonomous guidance, navigation and control system for a ROV. A thruster dynamics model describing the thrust force of a propeller-type underwater thruster was derived and implemented for the thruster on the Saab Seaeye Falcon ROV. The thruster dynamics model described is a quadratic equation that uses the propeller velocity to determine thrust force. This model includes a mechanism for compensation against the external motion of the thruster, such as occurs when the ROV moves through the water. Several experiments were performed to fully characterize the quadratic thruster dynamics model and test its ability to accurately predict thrust force based on a known ambient water velocity and propeller angular velocity. The drag force was calculated and removed from the force measurements to get the thrust force used in the model. The model coefficients were determined and then the resulting model was tested against experimental data to determine the efficacy of the model in the lab environment and compare it to a widely used linear thruster dynamics model. The results showed the quadratic model improved upon the linear model, and the quadratic model was valid over a larger range of ambient water velocities. The quadratic model was then inverted to provide a thruster control algorithm that determines the propeller angular velocity necessary to produce a desired thrust force. This algorithm was used to design a low-level thruster controller. This controller was designed to be used on an existing vehicle where thrust force feedback is not available and difficult or expensive to add. This allows it to be used in a wider range of applications than controllers that rely on such feedback to operate. The controller was implemented using a PID control loop to drive the angular velocity of the propeller to the desired rate. An iso-parametric mapping, which transforms the linear PID output to the non-linear thruster input, was added to provide a faster response time for the controller over the entire range of the propeller velocity. The performance of this low-level thruster controller was demonstrated in the test environment. The low-level thruster controller followed a desired thrust force under a range of ambient water velocities. The thruster characterization and low-level thruster controller was designed to be used on an existing ROV. The motivation behind this work is to build a controller that may be implemented for use by a high-level vehicle controller. The low-level thruster controller presented here does not depend on sensors or equipment that is largely unavailable on vehicles without costly retrofits, and the experimental characterization does not require intimate knowledge of the inner workings of the thruster. This makes it easy to implement and generalize to a variety of thrusters. The results of this work show a low-level thruster controller than can be used in a control schema for existing ROVs. / Graduate / 0547 / matt@amosbuchanan.net

An accurate satellite attitude and orbit control is a key factor for a successful mission. It guarantees for example sun acquisition on solar panels, fine pointing for optimal telescope usage or satellite lifting to reach higher orbits, when required. Furthermore attitude and orbit control is applied to compensate any occurring disturbances within the space environment. The problem tackled in the present thesis is the optimization of thruster commanding to perform spacecraft orbital manoeuvres. The main objective is to develop different algorithms that are suitable for on-board implementation and to compare their performance. For an optimal thruster command distribution the algorithms shall solve linear programming (or optimization) problems, more exact they shall compute thruster on-times to generate desired torques and/or forces, which are requested by the on-board software. In total three different algorithms are developed of which the first one is based on the pseudoinverse of a matrix, the second one is a variation of the Simplex method and the third one is based on Karmarkar’s algorithm, which belongs to the interior-point methods. The last two methods are well known procedures to solve linear programming problems and in theory they have been analyzed before. However this paper proves their practical application and industrial feasibility for orbital manoeuvres of the weather satellites of ESA’s MTG project and their scalability to any number of thrusters on a generic satellite for 6 degrees of freedom manoeuvres. There are 6 MTG satellites and each has 16 one-sided reaction control thrusters, placed at specific positions and pointed towards defined directions. Physical mechanisms limit the thrusters output to minimum on- and off-times. The focus of this thesis will be on the orbital transfer mode, due to the high disturbances that arise during four motor firing sessions at the apogee, executed to reach higher orbits and finally GEO. The firing sessions are performed by a liquid apogee engine and while this engine is in boost mode, the thrusters shall be used for attitude control only. The technique (nominal case) developed by OHB for this maneuver and currently operational uses 4 thrusters only, which are all pointing in the engine’s direction. They are also used to settle the fuel before the engine is turned on. For control the Pseudoinverse method is applied. If one of the 4 thrusters fails, the backup scenario takes place, which includes using 4 totally different thrusters and no fuel settling, due to their unfavorable position with respect to the engine. The initial idea of this work was to develop a controller for 6 thrusters, using only 2 of the 4 nominal case thrusters, to have a better control performance in the backup case. The Pseudoinverse method was developed by OHB before, thus only small changes needed to be applied to work with 6 thrusters. The two other algorithms, based on the Simplex and Karmarkar method, were completely developed from scratch. To analyze their performance several tests were executed. This includes unit tests on a simple computer hardware with different input, Monte Carlo simulations on a cluster to test if the algorithms are suitable for MTG orbital manoeuvres and the application to 12 thrusters, mounted on a generic satellite to generate torques and forces at the same time for 6 degrees of freedom manoeuvres. For each thruster configuration the worst case outputs are shown in so called minimum control authority plots. The performance analysis consists of the maximum and average deviation between requested and generated torque/force, the average computed thruster on-times, the algorithms computation(running) time and iteration steps. For MTG the test results clearly confirm that the usage of 6 thrusters leads to more accurate generated torques and better control authority, than using only 4 thrusters. The Simplex method stands out here in particular, showing excellence performance regarding torque precision. Nevertheless the accuracy goes at the expense of computation effort. While the Pseudoinverse method is very fast and needs only one iteration step, the Simplex is half a magnitude, the Karmarkar one magnitude slower. But the latter lead to lower thruster on-times in terms of firing duration and thus fuel consumption is reduced. Also it is shown that Simplex and Karmarkar can control 12 thrusters at the same time to generate torques and forces, which proves their scalability to any thruster distribution. In the end it comes to the question whether generating a more accurate torque/force or the computational effort, which is strongly hardware dependent, is more important. A decision which depends on the mission’s objective. This paper shows that all three implemented algorithms are able to handle attitude control in the MTG backup scenario and beyond.