<p>Valve stiction is one of the largest stand-alone reasons for oscillatory behavior in process industry. It is reported by Siemens Oil and Gas that valve stiction is a problem that is hard and time consuming to detect at offshore production plants for oil and gas. As a result, Siemens Oil and Gas wants to develop an algorithm that detects stiction. The algorithm is thought to be a feature of the logging system of Siemens in the future. To better understand the problem and scope of stiction at a offshore oil and gas production plant, the effects of stiction is studied on a model of such a plant. The study shows that oscillations from stiction in the control valves of a first stage separator easily spread to downstream components such as the connected gas compressor. An algorithm that detects stiction from routine operating data is developed in this thesis. The method in base for the algorithm is chosen from a variety of methods for stiction detection. To choose a suitable method in base for the algorithm a brief survey of methods for detecting stiction is given. The algorithm output is a stiction index that indicate the presence of stiction in the data analyzed. The algorithm detects stiction in data from non-integrating processes with constant inputs and can be applied on data with a varying sample rate. Proper testing on real data from an offshore production plant remains to be done. The algorithm should also be improved to handle data containing noise. Two new ideas of detecting stiction in integrating processes are presented in the end of the thesis. The new ideas try to regain the hidden ellipses in the PV-OP plots from data with stiction from integrating processes. The first idea is to plot the OP data against time shifted PV data, while the second idea is to plot the OP data against high-pass filtered PV data. Both the ideas show promising results but need to be further developed and tested before they can be applied in a future application for stiction detection.</p>

<p>In this thesis, a closed-loop control algorithm for regulating the blood glucose concentration in type 1 diabetic patients is developed. Two control criteria are imposed on the system, namely: -Avoidance of hypoglycemia. (blood glucose concentrations should always be above $3 frac{mmol}{L}$) -Reduction in the average blood glucose concentration compared to what is achieved with manual control. (average blood glucose concentrations should preferably be less than $7.0 frac{mmol}{L}$). The developed control algorithm manages to fulfill both these control criteria. Hypoglycemia is avoided, and average blood glucose concentrations is reduced by $20%$ and $22%$ to a level of $7.0 frac{mmol}{L}$ and $6.9 frac{mmol}{L}$ in the two test subjects. However, further experiments should be carried out to test the robustness of the control algorithm, and a thorough investigation of safety issues for the user needs to performed. As a basis for the implementation of closed-loop blood glucose control, data from three diabetic patients is used to identify the parameters of a proposed mathematical model of the human insulin-glucose regulatory system. The identification process reveals that there is large variations between individual patient's parameter values, and the difference in insulin sensitivity is found to be specially high, both between and within patients.</p>

<p>The purpose of this thesis has been to develop methods for identification of nonlinear models to be used in ship autopilots. An accurate model is essential when developing autopilot systems. Although a number of identification methods are available, only a few ship maneuvers are described in the literature. During this report a literary study on nonlinear identification methods has been carried out and an overview over several methods is presented. A new maneuvering model derived by Andrew Ross is simulated to generate measurement data. Based on the measurements during several predefined maneuvers, an iterative prediction error method is applied to identify the parameters of two different autopilot models. Secondly, a new ship maneuver is suggested for identification of ship steering dynamics. Compared to the classic turning circle and zig-zag maneuver the new maneuver shows better convergence properties and perform good adaptation of the dynamics. At last the identified autopilot models are verified by simulating the ship in closed-loop using a model-based autopilot controller.</p>

<p>This thesis contains a steady state simulation model of a multistage crude oil production plant. The unstable reservoir fluid is separated into crude oil and gas in a three stage gas oil separation plant. Peng Robinson Equation of State and successive substitution is used to model the separators. The liquid output for the third stage separators is stable crude oil and the crude oil is sold on the international market. The gas liberated in the separators is compressed by centrifugal compressors and injected into the reservoir. Several methods for modelling centrifugal compressors are discussed. The discussion concludes that Dimensionless Parameters is the best suited method for this case and this method is therefore used. The plant studied has three separation stages at decreasing pressure and the standard control method is to use fixed separator pressure. This is not optimal and brute force optimization is used to find the optimal pressure set-points. Adaptive pressure set-points will improve product separation and therefore increase the profit. The profit is calculated as the income from crude oil sale minus the energy cost. Using this new method, the optimum separator pressures for Snorre A are determined. As a result, the profit is increased with 0.17, 0.11 and 0.07 present for three different inlet conditions. For one year this small increase in profit gives an extra two million dollars in income. The adaptive pressure set-points give an extension of the plant’s maximum capacity for a reservoir fluid with a high gas oil ratio.</p>

<p>The control solution is a crucial part of Managed Pressure Drilling. Existing solutions are often based on Proportional plus Integral (PI) control and have several drawbacks, mainly poor performance for some common drilling operations and a large need for retuning. Better control solutions will reduce costs and improve safety. This thesis presents a comparison of two adaptive control solutions versus a PI benchmark. Both complexity, tuning, performance and robustness will be compared. The two adaptive solutions considered are the well-known Model-Reference Adaptive Control method, and the recently developed $Lone$ adaptive control method. The control solutions are tested on a wide range of common drilling operations, some common controller issues, changing parameters and for two different control setpoints. The adaptive control solutions are shown to give better performance and to have less need for retuning, but not without costs. The main issues of the $mathcal{L}_1$ adaptive controller is low time-delay margins and high computational demands, but the performance is much better than for the benchmark controller. The most important recommendation for future work is evaluation of higher order filter designs for the $mathcal{L}_1$ adaptive controller, because of the potential to both reduce computational needs and to optimize time-delay margins.</p>

<p>Today's Basel-type banking system is compared with a 100% reserve banking system. Furthermore, negative interest rate on deposits is introduced for these two systems thus two new models are introduced. Eventually all four models are compared with each other in terms of debt crisis mechanisms and if they are prone to enter a liquidity trap. The comparing is done for debt to GDP ratio around a certain ratio of 60% thought to give neutral confidence, thus the effect from confidence dynamics can be excluded for these simulations.</p>

<p>In many ways, the simple act of walking is one of the most complex modes of locomotion there is. For control-system scientists the periodic hybrid dynamical nature of walking systems presents a number of unique challenges, many of which still lack satisfying solutions. This thesis applies fairly recent concepts of motion generation and control to generate steps and gaits for such a walking robotic system. The robot, SemiQuad, developed and built at '{E}cole de Nantes in France, is a five degree of freedom, underactuated periodic hybrid dynamical system. This text presents a generic method of reparametrizing a given smooth motion by the use of virtual holonomic constraints, and comments on the conditions required for the method to succeed. It is then shown how virtual holonomic constraints can be generated from scratch, and certain properties of holonomically constrained systems are investigated. From the generated constraints and associated motion, a controller based on the principle of transverse linearization is created, and closed loop characteristics of the system are observed.</p>

<p>The increasing demand for oil and gas in the world, and the fact that most of the easily accessible reservoirs are in production or already abandoned, result in a need to develop new resources. These may be new reservoirs that have previously been considered uneconomical or impossible to develop, or extended operation of existing fields. Developing smaller reservoirs, means that more wells have to be drilled per barrel, which gives both more and eventually greater challenges as more and more wells are drilled because the wells has to be drilled further and into more difficult formations. Mature fields are drained, which leads to lowering of reservoir pressure and therefore tighter pressure margins for drilling. Because of the challenges with deep water drilling and depleted reservoirs, there is a need to precisely control the pressure profile in the well during drilling in such formations. Some of the parameters that are needed to control the well precisely are not easily obtained during drilling, and an estimation of these will therefore be crucial to be able to use a model to control the well. The transmission of measurements from a well is also often either delayed or absent during periods of drilling, which will cause problems for the control of the well. It is therefore required that an estimation scheme is able to estimate the pressure in the well for the time interval between the updates of the measurements from the well. The conventional method for transmitting measurements from the bottom of the well is by mud pulse telemetry which is pressure waves transmitted through the drilling mud. These measurements will be delayed, so accurate real-time measurements will never be available. To estimate the bottom hole pressure, a extended kalman filter was evaluated. This filter is based on a simple mathematical model derived for the drilling process. The states in the filter is height of mud in the riser, mud weight and different friction factors for the well. The filter is tested when the measurements are continuous available, with delayed update of the bottom hole measurement, and for cases where one of the measurements are absent. A simple controller to control the bottom hole pressure is implemented to control the pressure for reference tracking and during a simulated pipe connection. During simulations, it was not possible to achieve convergence for the friction factor for normal flows, and this led to errors in the other states. The friction factor would only converge to its true value during very high flows during the nominal testing, which led to the other states also achieving their correct values. The problem in estimating the friction factor applied to all different forms for friction parameters. The kalman filter was tested against an artificial well simulated in WeMod, and gave decent estimates of the bottom hole pressure except for at low flows.</p>

<p>Model predictive control (MPC) of a Kaibel distillation column is the main focus of this thesis. A model description together with a model extension is also considered. The motivation of using a Kaibel distillation column is primarily its energy saving potential. There is no reason for using such an energy saving column if the product purities are below some acceptable values. These purities can be kept above these acceptable values by sufficient control of the column. The simulation model of the Kaibel column was extended to include an efficiency parameter which describes an insufficient vapor mixing effect that occurs in distillation columns. This insufficient mixing leads to increased impurity flows in open-loop, but this is counteracted by increased reflux flow in closed-loop. The column can obtain sufficient purities for increasing insufficient mixing until the reflux flow reaches its maximum value. A single layer MPC and a supervisory MPC approach have been described, implemented and tested on the simulation model of the column. These MPCs show improved dynamic responses compared to the existing decentralized control approach. These MPCs have also been compared in a sensitivity analysis part. The sensitivity analysis shows a clear improvement in the robustness properties for a supervisory MPC compared to decentralized control in terms of input uncertainty. The supervisory MPC has also shown to be more robust than the single layer MPC. A brief qualitative discussion regarding alternative MPC approaches has been done, where inferential control is suggested as an alternative MPC approach for the Kaibel column. An MPC implementation has been done for use at the existing laboratory column at Department of Chemical Engineering. Also here, based on implementation issues, the supervisory MPC is preferable compared to a single layer MPC. A parameter adjusted version of the developed simulation model is recommended as the MPC's internal prediction model.</p>

<p>Adjoint based gradient calculation for Nonlinear Model Predictive Control.</p>