<p>This thesis is a part of the SSETI (Student Space Exploration Technology Initiative) project, where students from several universities around Europe work together with the European Space Agency (ESA) with designing, building, testing and launching an Earth-Moon satellite orbiter (European Student Moon Orbiter (ESMO). A satellite model with reaction wheels placed in tetrahedron was deduced in a preliminary study together with an extended Kalman filter to estimate the attitude from star measurements. The stability and convergence properties of this system are studied in this thesis. Previous studies on the convergence of extended Kalman filter are presented and a proof of exponentially convergence of a system with extended Kalman filter is given and used to prove that ESMO with the extended Kalman filter converges exponentially. The most recent work and different methods to apply a nonlinear separation principle is presented. Three feedback controllers with proof of global asymptotic stability (GAS) is then introduced and implemented on ESMO. Based upon the global asymptotic stability of the feedback controllers, and the proof that the extended Kalman filter works as an exponentially observer, a nonlinear separation principle is deduced. The closed loop system can then be stated globally asymptotically stable based upon the deduced separation principle. The closed loop with the three different controllers is then simulated in Simulink for varying gains and different reference steps. The three controllers show stable characteristic as the theory implies. The robust controller shows best tracking and estimation properties, it is very accurate, simple, robust and adaptable to environmentally changes, and is therefore proposed as the most suitable controller for ESMO.</p>

<p>This report describes the modelling and performance of an attitude determination and control system (ADCS) for a small satellite in lunar orbit. The focus is on stability analyses of each of the components in the system, and of the system as a whole. In connection to this, the separation principle for nonlinear systems is investigated. Central background information is presented, covering necessary rigid body dynamics and stability properties. Three different controller types are analysed and compared herein, namely a model-dependent linearizing controller, a robust controller and a standard PD-controller. An observer is chosen based on earlier work, but some detail modifications are made to its structure. A state-space model of the satellite and environment is derived and implemented in Matlab, along with the observer and controllers. The observer and all three controllers are shown to be stable with Lyapunov analysis. The total ADCS including the observer is shown to have a cascaded structure, on which theory of nonlinear separation principles is used to establish stability properties of the total system. Finally, the ADCS is put to simulation tests imitating real-life scenarios and the performance of the different controllers are compared. The PD-controller shows the best performance, both in speed of convergence and robustness to model errors. While not completely satisfactory, the results give a basis on which to perform further work.</p>

<p>In this project a robust adaptive controller has been developed for Minerva, NTNU's research ROV. The controller was tested in simulation using Matlab/Simulink with a mathematical model of the vessel. It was also tested in a practical experiment at sea, with the ROV Minerva. The simulations, the control system performs very well. The results from the practical experiment are promising, but several improvements are necessary before the system works satisfactory. The single factor which is believed to degrade the perfomance the most is an error in the mapping between thrust force and rotational speed.</p>

<p>An adaptive Luenberger-type observer with the purpose of locating and quantifying leakages is presented. The observer only needs measurements of velocity and temperature at the inlet and pressure at the outlet to function. The beneficial effect of output injection in form of boundary conditions is utilized to ensure fast convergence of the observer error. This approach is different from the usual practice where output injection might appear as a part of the PDE’s. This makes it possible to employ OLGA, which is a state of the art computational fluid dynamics simulator, to govern the one-phase fluid flow of the observer. Using OLGA as a base for the simulations introduces the possibility to incorporate temperature dynamics in the simulations which in previous work was impossible. The observer is tested with both a straight, horizontal pipeline and an actual, long pipeline with difference in altitude. Both simulations with oil and gas are carried out and verification of the robustness of the observer is emphasized. In order to cope with modelling errors and biased measurements, estimation of roughness in the monitored pipeline is introduced.</p>

<p>In reservoir management it is important with reservoir models that have good predictive abilities. Since the models initially are based on measurements with high uncertainties it is important to utilize new available data. Ensemble Kalman Filter (EnKF) is a new method for history matching that has received a lot of attention the last couple of years. This method is sequential and continuously update the reservoir model states (saturations, pressures etc.) and parameters (permeabilities, porosities etc) as data become available. The EnKF algorithm is derived and presented with a different notation, similar to that of the Kalman Filter (KF) used in control engineering. This algorithm is also verified on a simple linear example to illustrate that the covariance of the EnKF approaches that of the linear KF in case of an infinite ensemble size. In control theory this method falls under the category of parameter and state estimation of nonlinear large scale systems. Interesting aspects as observability and constraint handling arises, and these are linked to the EnKF and the reservoir case. To determine if the total problem is observable is a nearly impossible task, but one can learn a lot from introducing this concept. The EnKF algorithm was implemented on a simple “shoe box” reservoir model and four different problem initializations were tested. Although decent results were achieved from some of the simulations other failed completely. Some strange development in the ensemble when little information is available in the measurements was experienced and discussed. An outline was presented for a reservoir management scheme where EnKF is combined with Model Predictive Control (MPC). Some challenges was pointed out and these involve computation time, predictive ability, closed-loop behavior etc.</p>

<p>This thesis investigates different methods of backstepping controller design for an electro-pneumatic clutch actuator used in heavy duty trucks. The first part of the thesis is a literature study, where the subject is control of nonlinear-sampled data systems in general. Sampled-data systems contain a continuous-time plant and a digitally implemented controller, which in general make them harder to analyze and control than systems that operate purely in the continuous-time or discrete-time domain. The available theory of nonlinear sampled-data control systems is scarce, but three different methods are described in this thesis; emulation design, direct discrete-time design, and sampled-data design. The electro-pneumatic clutch actuator is controlled using a continuous-time backstepping controller implemented digitally. This is essentially the procedure of emulation design and is the common, if not only, method used in practical engineering tasks so far. However, redesign of the continuous-time controller using the direct discrete-time method shows great potential of improving performance and robustness of sampled-data systems. Direct discrete-time design is based on an approximate discrete-time model of the plant, giving the controller a structure that accounts for the sampling of the hybrid system. Potentially, one can utilize slower sampling in the system by implementing a discrete-time controller into the digial computer instead of a continuous-time one. Examples and case studies that prove the improvement one can achieve by chosing the direct discrete-time design is included in the first part of the thesis. Both a third- and fifth-order model of the electro-pneumatic clutch actuator are presented, and used as a basis for continuous- and discrete-time state-feedback backstepping controllers. These controllers are simulated with different sampling intervals to show their performance under different circumstances. The continuous-time controllers prove good reference trajectory tracking of the pure continuous-time system, while the performance of the sampled-data systems descends as higher sampling intervals are used. And, as opposed to the mentioned examples and case studies, the controller designed when taking the sampling into account shows no sign to outperform the controller that was designed without considering the sampling, at least not for the relative fast sampling the clutch actuator operates with.</p>

<p>This report covers the development of a new joint mechanism and a robotic simulator. These are intended to be used in the further development of a climbing robot that has been previously worked on. The climbing robot is briefly described in this report. It is a robot with four arms that each has 6 joints with 1 degree of freedom each. At the end of each arm is a gripper that will serve as the robots main tool of interaction with its immediate environment, for instance a ladder the robot is climbing in. The weight of this robot is estimated and used to find torque requirements on the new joint mechanism. A lightweight joint mechanism is of interest not only for a climbing robot, but for many if not all other kinds of robots as well. Such a joint will not need to be as strong as its heavier counterparts, as less power is required to support the joint itself. The new joint mechanism is an attempt of creating a joint that is lighter than traditional joints. The main idea is to have a single powerful actuator driving several joints. The joints are mechanically powered by a rotating shaft that runs through all of them. Each joint uses a clutching mechanism to connect to the shaft and thus transfer torque. the clutching mechanism has been explored in detail, and a suitable clutch has been selected. This clutch is then used in four design proposals. The last of these proposals is assembled into a prototype, and the prototype is qualitatively tested. The tests demonstrate the concept, but it is not yet shown that this joint mechanism will be lighter that a traditional joint mechanism. The simulator is developed to be a design tool for further development of the climbing robot. A list of specifications for the simulator is presented, however the simulator is not able to meet all these requirements at the current stage of development. The remaining work on the simulator is discussed, as well as an evaluation of the software as it is at the time of writing. The report describes the kinematic modeling that is used to represent the translational and rotational relationship between the different elements of the robot. The kinematic relationships is then put into a mathematical representation of the robot which represents the robots as a chain of elements. Each element has several properties, among which are its relationship to other elements and a graphical representation of the element. The elements, and thus the robot, is represented graphically in a virtual reality environment. The simulator allows the user to specify several parameters of the robot, such as number and dimensions of the joints and the dimensions of the body. The user is also able to control the velocity of each joint. Both the simulator and the joint has been developed to be general enough for use on other kinds of robots, with small modifications in the case of the simulator.</p>

<p>Summary: In this thesis work, mathematical models required to simulate an offshore ploughing operation has been derived. This includes a surface vessel model, a model of the plough and its friction force due to seabed sediment and a towline model. A Dynamic Positioning control system has been derived in order to regulate the vessel to a desired location based on the plough’s desired position. A supervisor module has been derived in order to generate the vessel’s reference position in a smooth manner. And finally the total system has been implemented and simulated in the Simulink_TM environment. The surface vessel model derived in this assignment is based on an offshore supply vessel from the ”MatLab Marine GNC Toolbox” in Simulink_TM. The vertical motion of the vessel has been kept constant during simulations, based on the assumption that the buoyancy force of the vessel is large compared to the vertical towline force. The plough’s friction force due to penetration of the seabed sediment has been modeled, based on the content in reference [5], to get a realistic picture of the sediment forces involved in ploughing operations. It was found that the plough’s friction force profile changed with different operational boundaries. The boundaries are the ocean depth and the ploughing speed. For the boundaries in this assignment the resulting ploughing force equation were found to be nonlinear and shaped as a sigmoid function. In this assignment the lumped mass model has been derived for the towline’s motion and proven to give reasonably good numerical results when implemented in the Simulink TM environment. To get a realistic towline motion in seawater, a hydrodynamic quadratic damping force has been added to the equations. This hydrodynamic damping had effect on the towline’s tangential and normal motion components. The DP controller derived in this assignment consists of a PD-controller with feed forward signal from the horizontal towline tension. Feed forward signals are often influenced by noise and must be filtered to obtain low-frequency signals. In this assignment a ordinary 1st-order low-pass filter has been used in order to damp out oscillations from the towline. This filter has been proven to give a good damping effect when the towline was exposed to underwater currents. The DP controller provides good position tracking quality. The supervisor module designed in this assignment consists of a reference generator an a reference model. The supervisor module is responsible for converting input signals for the plough’s desired path into a smooth tracking signal for the vessel’s control system. The reference generator produces smaller intermediate reference signals, as input to the reference model, from a final desired vessel position. A circle of acceptance has been introduced in order to change reference values at a convenient vessel location. This has been proven to give a nice effect on the vessel’s and the plough’s behavior. The reference model has been designed with a speed saturation element, in order to bound the speed of the ploughing operation. During the case simulations it was found that by defining the operation over a longer distance, a more efficient operation is gained. When crossing longer distances the plough will reach the vessel’s speed and underwater current disturbances are small compared to the ploughing force that has gotten time to be built up. Underwater currents has great influence on the towline when the towline’s pulling force is small. In appendix A a CD can be found. On this CD this report can be found, the original work schedule, pictures and the Simulink program for the ploughing operation.</p>

<p>In the autumn of 2006 a satellite project was started at NTNU. The goal of the project is two-folded, first it seeks to create more interest and expertise around the field of space technology, secondly to create a satellite platform which can be modified and equipped with different payloads to perform selected tasks in a Low Earth Orbit. For a satellite to be able to complete missions involving sensory and imaging, an attitude determination and control system is needed to give the satellite a stable attitude. In order to create a good attitude control system, a Gauss-Newton improved extended Kalman filter is used together with reference models to supply the controller with estimates of both satellite angular velocity and orientation. This report focuses on the Attitude Determination System, ADS, realized by implementing the improved extended Kalman filter on a microcontroller. The challenge is to create an estimator that will provide the control system with adequate estimates without requiring to much computational power, as this is a limiting factor on board a micro satellite. The need for good computational power comes from the multidimensional matrix mathematical operations performed on float numbers. Based on previous work, an improved Extended Kalman filter has been developed and implemented on a microcontroller for further testing. A new filter, the Unscented Kalman Filter has also been explored but not implemented.</p>

<p>Electric motors with metal graphite brushes experience a change in contact resistance depending on current load. This varying resistance leads to varying gain in the motor, since the proportion of power dissipated due to the resistance changes. This investigation is concerned with a servomechanism, which uses such motors for position and force control. Force control with electric motors is typically accomplished by controlling the current. The servomechanism utilizes a discrete controller, and discretization renders the system unstable for any practical sampling times and controller gains when using current feedback. Feedforward is therefore used to control the current, and is thus sensitive to the variation in the motor's resistance. To counter the sensitivity, a parameter estimator using least mean squares is presently used to learn the value of the resistance. An alternative to the parameter estimator might be a gain-scheduler based on a model of the resistance attenuation phenomenon. It should be possible to find such a model because of the deterministic quality of the resistance attenuation. In the course of this investigation, an extended Kalman filter has been developed to estimate current and resistance with accuracy. Estimated current and resistance data from a series of experiments has been fitted to a rational model using nonlinear regression. The obtained model was used as a gain-scheduler. The estimated resistance from the extended Kalman filter, the least mean squares estimator, and the gain-scheduler were used in conjunction with the feedforward controller and compared for their ability to control the current. The extended Kalman filter provided the most accurate results, but at the expense of being more complex than the least mean squares estimator. The gain scheduler was the worst performer, most likely due to unmodeled effects. With some modifications, it was made to perform on par with the least mean squares estimator, but more work is required before it can be recommended for use.</p>