This master’s thesis examines whether it is possible to improve an existing road grade estimator by measuring an additional signal from a GPS receiver. The existing estimator uses the differentiated altitude signal from the GPS to estimate the current road grade. The additional signal is the vertical component of the 3D-velocity that is reported by the GPS. By using spectral analysis together with system identification the additional signal, which is based on velocity information, is compared to the previously used signal which is based on position information (altitude). By sampling data from an experiment at Swedish highway E4 it was possible to conclude whether the two signals should be used together (sensor fusion) or if it was better to use just one of them. The signals’ measurement errors were not white noise but they were modeled as autoregressive (AR) processes. An augmented Kalman filter, with the two AR models included in the system model, was used to evaluate the performance of the sensor fusion algorithm. The result of the thesis is that the previously used signal, the one based on differentiated altitude, should be used alone in this specific road grade estimator. The major reason for this is that the additional signal proved to have a bias that would have been integrated by the road grade estimator and driven the estimation of the altitude away from the true altitude. A solution for this problem would be to neglect this bias and construct a road grade estimator that does not include the altitude as a state.

The methods used today at Scania to test and calibrate diagnostic tests are often carried out given data from only a few trucks. This is due to the expenses and vast amount of time associated with collecting data. The dispersion between different sensor individuals is thus not included in the evaluation of a diagnostic test. Often data from a single truck is collected, the data is then used for diagnostic test calibration. This leads to that when the diagnostic test is implemented in a different truck with a different set of sensors, the diagnostic test might not act as desired. The main focus of this thesis is the development of a method that evaluates the performance of a diagnostic test, both when data is taken from only one individual, and when data is taken from a whole population. The method makes it possible to use data from only one individual to predict the diagnostic test performance for the whole population of individuals. A performance evaluating method that can take sensor individuals into account is compared with a method that can not. These methods are tested on parts of the Scania Selective Catalytic Reduction diagnosis system. Results show that the authenticity of the evaluated diagnostic test performance is improved significantly when the requirements for using the developed method are fulfilled. / Statistisk utvärdering av diagnostester med fokus på individspridning Designen och utvärderingen av ett diagnostest på Scania sker idag ofta givet data från endast ett fåtal lastbilar. Detta för att insamlandet av ny kördata är en dyr och tidskrävande process. Den spridning mellan olika lastbilar som finns, individspridningen, inkluderas då inte i utvärderingen. Individspridningen kan till exempel bero på att olika lastbilar har olika uppsättningar av givare. Det ger till följd att prestandan för ett diagnostest är olika för olika lastbilar. I detta examensarbete behandlas utvärdering av ett diagnostest med hänsyn taget till individspridning samt kravsättning av ett diagnostest. En metod presenteras som givet data från endast en lastbil uppskattar ett diagnostests prestanda. Metoden gör det möjligt att använda data från endast en individ för att uppskatta ett diagnostests prestanda för en hel population av individer. Den framtagna metoden har applicerats och utvärderats på ett diagnostest designat för att detektera reducerad verkningsgrad i Scanias SCR-efterbehandlingssystem. Resultat visar att autenticiteten av ett diagnostests prestanda utvärdering ökar markant då kraven för att använda den utvecklade metoden är uppfyllda.

There are different automation systems to control processes in industry. One of them is DeltaV, which is a product from Emerson Process Management. Recently, Sandvik Coromant, bought the DeltaV automation system for using it in production. To increase the knowledge about DeltaV at Sandvik Coromant a project was initiated with the aim to create a test platform and a course compendium for self learning. This thesis describes how the test platform was developed and its functionality. / För att styra processer i industrin finns det olika styrsystem. Ett av dessa är DeltaV, som är en produkt från Emerson Process Management. Nyligen köpte Sandvik Coromant styrsystemet DeltaV för att använda det i sin produktion. För att öka kunskapen om DeltaV på Sandvik Coromant initierades ett projekt: Att utforma en test plattform och ett kurskompendium för självlärning. Denna uppsats beskriver hur tesplattformen har utvecklats och dess funktionalitet.

In order to facilitate testing of anesthesia machines a patient simulator has been required by Maquet Critical Care AB. A patient simulator has been developed to reduce expensive and time consuming animal experiments and temporary lab arrangements. The patient simulator has been developed and designed to simulate human lungs with regards to anesthetic uptake, production of carbon dioxide and heating and moisturizing exhaled air. The system comprises a container and a test lung to simulate the volume and the dynamic of the human lung. The simulation of uptake of anesthetics has been implemented by pumping a fraction of the gas from the container through a bypass circuit with an active charcoal filter that adsorbs volatile anesthetics. The flow through the bypass circuit is generated by a pump and is controlled by a linear valve. The resulting performance of the patient simulator has fulfilled the requirements. A verification of the performance has been done by weighing the anesthetic uptake filter and comparing it with calculated values. The exhaled simulator imitates human lungs to a large extent. The simulation of uptake of anesthetics follows a model that is a modification of a well known model. A modification of the model was made in order to handle changes in agent concentration. The carbon dioxide production was implemented by dosing carbon dioxide from a gas tank through a mass flow controller. The carbon dioxide in the system can be set to a constant flow to achieve a certain production rate or set to achieve a desired concentration in the exhaled gas. To achieve moisture and right temperature in the exhaled air, a humidification chamber with water with heating elements was used. A connection to the humidification chamber with non return valves was used to guide the exhalation gas through the humidification chamber while bypassing the inhalation gas. This minimizes the humidity uptake in the charcoal filter. To monitor and control the system, a program was made in LabVIEW. The graphical user interface of this program consists of a control panel where measurement data of the system is displayed. Settings of patient data and other parameters are also made here. The program handles the communication with the process using a data acquisition and instrument control device. A PI-controller with gain scheduling and anti-windup was implemented to control the flow through the anesthetic uptake filter. The parameters of the controller were tuned experimentally. / För att kunna testa anestesimaskiner har en patientsimulator efterfrågats av Maquet Critical Care AB. Patientsimulatorn konstrueras för att kunna minska antalet kostsamma djurförsök och tillfälliga labuppställningar. Patientsimulatorn har utvecklats och konstruerats för att efterlikna mänskliga lungor med egenskaper såsom upptag av anestesimedel, avgivning av koldioxid samt uppvärmning och befuktning av utandningsluft. Systemet har en behållare och en testlunga för att simulera lungdynamiken hos människan. Anestesimedelsupptaget har implementerats genom att pumpa en del av gasen i behållaren via en bypasskrets med ett kolfilter som adsorberar anestesimedel. Flödet genom bypasskretsen skapas av en pump och styrs med en linjärventil. Anestesimedelsupptaget följer en modell som är en modifiering av en känd modell. Den befintliga modellen hanterade endast konstanta halter anestesimedel, vilket inte återspeglar verkligheten. Modifieringen gjordes för att hantera förändringar i anestesimedelskoncentrationen. Koldioxidavgivningen har implementeras genom att dosera in koldioxid från ett tryckkärl via en massflödesregulator, för att erhålla önskad koldioxidkoncentration alternativt koldioxidproduktion. Fukt och temperatur i utandningen har åstadkommits genom att använda en befuktarkammare med vatten som värms upp med hjälp av två PTC-element. En anslutningsmodul med backventiler konstruerades för att kunna leda exspirationsgasen genom befuktarkammaren och inspirationsgasen förbi kammaren, då vissa komponenter i patientsimulatorn är känsliga för fukt. På detta sätt undviker man även att fukten upptar en del av kolfiltrets adsoptionskapacitet. För att övervaka systemet och dess sensorer samt styra det, har ett program skapats i LabVIEW. I LabVIEW-programmets användargränssnitt har en kontrollpanel byggts där systemets mätvärden presenteras samt patientdata och parametervärden kan ställas in. Programmet hanterar kommunikationen med systemet via en styr- och mätmodul. Styrningen av flödet genom kolfiltret sker med hjälp av en PI-regulator försedd med parameterstyrning och anti-windup och parametervärden har tagits fram experimentiellt. Anti-windup användes för att begränsa styrsignalen till ventilen. Resultaten har varit goda och noggrannheten har bekräftats genom kontrollvägningar av kolfiltret att rätt mängder anestesimedel tagits upp. Patientsimulatorn efterliknar till stor del de egenskaper en mänsklig lunga har.

Understanding and interpreting dynamic human actions is an important area of research in the field of computer vision and robotics. In robotics, it is closely related to task programming. Traditionally, robot task programming has required an experienced programmer and tedious work. By contrast, Programming by Demonstration is an intuitive method that allows to program a robot in a very flexible way. The programmer demonstrates or shows how a particular task is performed and the robot learns in an efficient and natural manner how to imitate or reproduce the human actions. Here, we develop a general policy for learning the relevant features of a demonstrated activity and we restrict our study to imitation of object manipulation activities. A Nest of Birds magnetic tracker is used for activity recognition and two different dimensionality reduction techniques are applied. The first one uses linear dimensionality reduction in order to find the underlying structure of the data. Particularly, Principal Component Analysis (PCA) is used to learn a set of principal components (PCs) to characterize the data. The main problem using PCA is that linear PCs cannot represent the non-linear nature of human motion. The second method uses a non-linear dimensionality reduction technique. Specifically, spatio-temporal Isomap is applied to uncover the intrinsic non-linear geometry of the data, and it is captured through computing the geodesic manifold distances between all pairs of data points. For classification purposes, both PCA and ST-Isomap can be viewed as a preprocessing step. When the dimensionality of the input data is so high that becomes intractable, most classification methods will suffer and even fail in their goals due to their sensitivity to the input data dimensionality. Fortunately, high dimensional data often represent phenomena that are intrinsically low dimensional. Thus, the problem of high dimensional data classification can be solved by first mapping the original data into a lower dimensional space using a dimensionality reduction method such as PCA or ST-Isomap and then applying K-nearest neighbors (K-NN), radial basis functions (RBF) or any other classification method to classify of the query sequence. In the first stage of our work, PCA combined with k-means clustering is applied. In the second stage of our work, spatio temporal Isomap (ST-Isomap) combined with Shepard’s interpolation is applied. For classification purposes, simple Euclidean distances are used. The experimental evaluation shows that a linear dimensionality reduction technique can not find the intrinsic structure of human motions due to their non-linear nature. In contrary, a non-linear one, such as spatio-temporal Isomap is able to uncover a low dimensional space in which the data lies facilitating the classification step in a much better way than PCA.

In the closed chemical recovery cycle in the sulphate pulp mill it is important to remove non-process elements. This is done by clarification of the green liquor, either in clarifiers or in filters. This project focuses on a cassette filter developed by Kvaerner Pulping AB. The cassette filter is semi-continuous and the aim of the project was to model the filter in order to be able to control cycle time and feed towards optimization of the capacity. The green liquor sludge forms a compressible filter cake when filtered. The model was built on the filter equation for compressible cake filtration and parameters such as filter cloth resistance, compressibility index and specific resistance in the cake were to be determined. The parameters were calculated by minimizing the difference between the calculated model and the measured data. Some simulation experiments were done to examine if optimization was possible. It turned out that the two parameters describing the green liquor (specific resistance in the cake and compressibility) were not identifiable from each other and the third parameter (resistance in the filter cloth) was also sometimes unidentifiable. The simulation experiments showed that the capacity of the cassette filter is hard to optimize during unfavourable conditions controlling only cycle time and feed. Proper actions might be to add lime mud or aluminium to increase the filterability of the green liquor sludge or to wash the filter socks to decrease the resistance in the filter cloths.

A patient simulator for testing anaesthesia machines has been developed at Maquet Critical Care AB, to reduce costly and time-consuming experiments on animals. The device simulates human lungs regarding lung dynamics and volume, uptake of anaesthetic agents and the production of carbon dioxide, heat and moisture. Further demands on the simulator are durability and size; the device shall be compact enough to be moveable. The resulting simulator fulfils the requirements and enables better repeatability and ability to test extreme cases than experiments on animals do. Uptake of the anaesthetic agent is achieved in a active carbon filter and controlled by regulating the flow. The flow is created by a regenerative blower and controlled using a proportional valve. The uptake can be set by the user or by a simple uptake model modified to recursively handle changes in the concentration of anaesthetic agent. Carbon dioxide is fed into the system from a tank by a mass flow regulator. The supply of carbon dioxide can be set according to a constant flow to simulate a certain production rate, or to achieve a desired concentration of carbon dioxide in the exhaled gas. Heat and moisture is added to the exhaled gas in a chamber partially filled with water. The chamber is heated by self-regulating heating elements and supervised by a combined temperature and humidity sensor. Control and monitoring of the system is implemented in LabView and includes data processing, user interface and communication with sensors, actuators and data acquisition devices. When verifying the functionality of the simulator, tests show the exhaled gas is saturated with moisture and has a temperature sufficiently close to normal body temperature. The deviation of the calculated uptake compared to the measured value is within the requirements for larger flows through the filter. When performing tests with extremely small flows, the performance deteriorates due to limited precision of the flow measurement. Thus, if better performance is desired when simulating small uptakes, improved flow sensing accuracy is recommended.

In this thesis two methods to solve the Simultaneous Localization And Mapping problem are presented. The classical extended Kalman filter is used as a reference from where an efficient particle filter is examined, which uses deterministic samples called sigma points. Most of the effort is put on implementing these algorithms together with the Symmetries and Permutations Model, but a preliminary comparison of the methods has been done as well. Experiments show that linearization errors make the map inaccurate over long periods of time, and methods are discussed which decrease these effects.

In this thesis we discuss the development of three homeworks for the course Hybrid and Embedded Control Systems given at the institution S3 (Signals, Sensors and Systems) at the Royal Institute of Technology (KTH) in Stockholm. The main objective of the course is to present tools for modeling, analyze and design embedded control system. The course, first given in spring 2003, is interdisciplinary since its program cover arguments strictly related to system theory and computer science. As part of the course three homeworks has to be solved by the students. The main idea of the homeworks is that the student should gain further practical understanding of the material presented during the lectures. The main topic of the thesis is to describe the design of three homeworks. For each homework a problem a problem definition, containing different tasks, is formulated. Also a computer based tool, used to solve the tasks, is presented. All tasks are explained in detail and the expected results are presented.

This thesis considers the problem of designing a robust controller that achieves highperformance positioning and reference tracking of a machine tool. Specifically, the machine tool is a XY-table used in high-accuracy/high speed milling applications. The XY-table consists of a DC motor drive connected to the load using a ball screw, and can be modelled as a two-mass system. However, the presence of friction and backlash requires nonlinear models and associated control designs. Moreover, the machine tool needs to operate under a wide variety of load conditions, which necessitates a robust design. The starting point of this thesis is a PID controller comprised of position and velocity feedback loops, velocity and acceleration feed-forward controls and a nonlinear friction compensator. With this controller as a baseline, we develop two advanced controllers of sliding mode type: one is based on disturbance observer theory, while the other uses adaptive methods. It turns out that the controller based on disturbance observer theory fails to improve the performance of the baseline PID solution. However, the controller based on adaptive methods achieves superior performance towards the PID controller.