<p>The original promise behind the Internet Protocol was to deliver data from a sender to the receiver using a best-effort approach. This means that the protocol makes no guarantees except that it will try to deliver the data to the destination. If some problem occurs the packet may be discarded by the network without any notice. No guarantees are made regarding the time it takes to deliver the data, the rate at which data will be delivered or if data is delivered in the same order it was sent. The best-effort approach is arguably the reason behind the success of the Internet Protocol and is what makes IP scalable to networks the size of the Internet. However, this approach is also a problem for network operators who want to offer better quality of service to some of their customers. This master thesis will discuss some of the theories behind the implementation of quality of service schemes in an IP network and also provide an example of how to implement it in an existing network.</p>

<p>An automatic control system for calibration of pressure indicators has been constructed. This control system can be used as a complement to the calibration system located at AerotechTelub, division MainPartner, in Linköping. </p><p>The calibration system used today contains some not wanted pressure changes, which is compensated by hand today. The new system presented in this report automates the calibrating process and simplifies the calibration. </p><p>Suitable hardware as motor, controls card and so on have been found and software in the form of Visual Basic program has been developed. </p><p>The solution presented in this thesis satisfies the goals that have been presented in the goal description. To achieve a more complete automatic calibration system there are some further improvements yet to be done.</p>

<p>This master thesis studies distributed navigation which isa function implemented in a future network based combat information system to improve the accuracy in navigation for combat vehicles in a mechanized battalion, above all in the event of loss of GPS. In the event of loss of the GPS the vehicles obtain dead reckoning performance through the backup system that consists of an odometer and a magnetic compass. Dead reckoning means a drift in the position that makes the accuracy in the navigation worse. </p><p>The distributed navigation function uses position and navigation data with measurements between the vehicles to estimate the errors and uncertainties in positions, which are used to improve the accuracy in position for the vehicles. </p><p>To investigate and demonstrate distributed navigation, a simulation environment has been produced in Matlab. The environment is general so different navigation systems can be used and studied and also dynamical so a further development is possible. </p><p>The simulation environment has been used to investigate and evaluate an implementation of distributed navigation. The implementation has been made using a central filter where fusion takes place of all navigation data and measurements. This filter has been realized with help of Kalman filter theory, in which all vehicles are put together in a state space model. Simulations have been performed for different scenarios and the result of these show that the drift in position is avoided.</p>

<p>The use of spread spectrum signals has increased dramatically in military applications. Finding methods for detecting and positioning of these signals have become interesting research areas for signal intelligence purposes. One method is to measure the time difference of arrival (TDOA) that occurs when two receivers are synchronous and spatially separated. Based on the TDOA-technique an audio-demonstrator has been developed and implemented. This report describes the theory for how sound received in microphones can be used to extract information about the transmitter’s position from the measured time difference. The technique has been implemented and tested in a non-silencing room where sound from a loudspeaker has been recorded into MATLAB through the use of microphones. By correlating the received signals, an estimation of the time difference can be made. A hyperbolic function represents all possible transmitter positions for the given time difference. With three of more receivers a single position can be estimated. With an accuracy within a couple of hundreds of a degree, a direction can be estimated with simulated signals for a given SNR (usually 25dB) or a position within a few centimetres when the transmitter is a couple of meters away. Tests with real audio signals show less satisfying results. The position can at best be estimated with an accuracy of 5% of the distance to the receiver when the microphones are spread out 0.7m apart. The performance of the audio-demonstrator can be improved by using better methods for finding the points of intersection between hyperbolas and by weighting the estimated time differences.</p>

<p>In this final thesis two simulation models have been integrated. The simulation models are models of JAS 39 Gripen’s fuel system, software and hardware. The time consuming and costly work to develop software has made the department of fuel system to build two models in Xmath/Systembuild. The software model also simplifies the comprehension of how the software in the control computer of the fuel system works. Before the final thesis was done the software and hardware models were used separately and for different purposes. The software model wasused for verification and validation, and the hardware model for the system simulator. Because of the complex software it was a requirement to be able to simulate the software and hardware when they were connected, which would make it possible to study the different control signals generated by the software. To do that an interface between the user and the simulation model was created because it takes too much time to set all the inputs to the simulations model. The final thesis was assumed to deal only with an upgrading of the software model. The models have been connected and a user’s interface has been created with help of the program language Mathscript, which is a part of Xmath. A user’s guide is written so the user can simulate without experience of Xmath/Systembuild. With the connected models the user can now efficiently simulate and the result can be saved for later usage. The user can also simulate predefined missions from Saabs system simulator.</p>

<p>This Masters Thesis considers tracking of ground targets. Since ground targets often are difficult to detect with sensors a track is easily lost in ground target tracking. The main question in this thesis is whether information on close targets and observations of them can be used to make the tracking more reliable. An algorithm using Kalman filters and JPDA-association to create a group track for vehicles travelling together has been implemented in Matlab. Using the state of the centre of the group the state of a vehicle in the group can be updated. The result of updating vehicles states with the state of the centre of the group is that the relative decrease in lost tracks is improved with up to 53 % in the studied case.</p>

No description available.

This thesis considers robust control of an induction motor drive, consisting of an input filter, a voltage source inverter and one or several induction motors in parallel. The motor torque is here controlled by using the method Indirect Self Control (ISC), and power oscillations between the inverter and the input filter are damped by means of a stabilization controller in an outer feedback loop. Closed-loop performance with ISC is analyzed under the assumption of a stiff inverter input voltage. It is shown how parameter errors influence the torque loop and the conclusion is that the motor leakage inductance should not be overestimated, especially not with a large desired control bandwidth. It is also shown that model errors enhance cross coupling, but that the performance is quite insensitive even to large parametric errors. Based on the closed-loop model, expressions for the controller parameters are derived to obtain required stability margins. For design of the stabilization controller to suppress oscillations between the inverter and the input filter, it is shown that the effects of time delays and limited torque (or current) control bandwidth are important and cannot be neglected. From models including these non-ideal properties of the control system, explicit expressions for stabilization controllers to use with ISC as well as field-oriented control (FOC) are derived. This is valuable as stabilization often is designed through costly and time-consuming manual tuning. In the controller design, the trade-off between tight torque control and stability of the DC-link is also explicitly considered. In this way reasonable stability margins are obtained, while minimizing the negative effects on torque control. Stability of the closed-loop drive with the proposed stabilization is validated through realistic hardware-in-the-loop simulations using real control HW and SW. Using models obtained from frequency domain system identification, stability of the non-linear closed-loop drive is verified by combining stability results for linear systems with the small gain theorem for the non-linear model errors. This thesis considers the input filter dynamics in connection with torque control of an induction motor. The key result is a model-based framework for simultaneous treatment of DC-link stability and efficient torque control. / QC 20100913

The purpose of system identification is to construct mathematical models of dynamical system from experimental data. With the current trend of dynamical systems encountered in engineering growing ever more complex, an important task is to efficiently build models of these systems. Modelling the complete dynamics of these systems is in general not possible or even desired. However, often, these systems can be modelled as simpler linear systems interconnected in a dynamic network. Then, the task of estimating the whole network or a subset of the network can be broken down into subproblems of estimating one simple system, called module, embedded within the dynamic network. The prediction error method (PEM) is a benchmark in parametric system identification. The main advantage with PEM is that for Gaussian noise, it corresponds to the so called maximum likelihood (ML) estimator and is asymptotically efficient. One drawback is that the cost function is in general nonconvex and a gradient based search over the parameters has to be carried out, rendering a good starting point crucial. Therefore, other methods such as subspace or instrumental variable methods are required to initialize the search. In this thesis, an alternative method, called model order reduction Steiglitz-McBride (MORSM) is proposed. As MORSM is also motivated by ML arguments, it may also be used on its own and will in some cases provide asymptotically efficient estimates. The method is computationally attractive since it is composed of a sequence of least squares steps. It also treats the part of the network of no direct interest nonparametrically, simplifying model order selection for the user. A different approach is taken in the second proposed method to identify a module embedded in a dynamic network. Here, the impulse response of the part of the network of no direct interest is modelled as a realization of a Gaussian process. The mean and covariance of the Gaussian process is parameterized by a set of parameters called hyperparameters that needs to be estimated together with the parameters of the module of interest. Using an empirical Bayes approach, all parameters are estimated by maximizing the marginal likelihood of the data. The maximization is carried out by using an iterative expectation/conditional-maximization scheme, which alternates so called expectation steps with a series of conditional-maximization steps. When only the module input and output sensors are used, the expectation step admits an analytical expression. The conditional-maximization steps reduces to solving smaller optimization problems, which either admit a closed form solution, or can be efficiently solved by using gradient descent strategies. Therefore, the overall optimization turns out computationally efficient. Using markov chain monte carlo techniques, the method is extended to incorporate additional sensors. Apart from the choice of identification method, the set of chosen signals to use in the identification will determine the covariance of the estimated modules. To chose these signals, well known expressions for the covariance matrix could, together with signal constraints, be formulated as an optimization problem and solved. However, this approach does neither tell us why a certain choice of signals is optimal nor what will happen if some properties change. The expressions developed in this part of the thesis have a different flavor in that they aim to reformulate the covariance expressions into a form amenable for interpretation. These expressions illustrate how different properties of the identification problem affects the achievable accuracy. In particular, how the power of the input and noise signals, as well as model structure, affect the covariance. / Systemidentifiering används för att skatta en modell av ett dynamiskt system genom att anpassa modellens parametrar utifrån experimentell mätdata inhämtad från systemet som ska modelleras. Systemen som modelleras tenderar att växa sig så omfattande i skala och så komplexa att direkt modellering varken är genomförbar eller önskad. I många fall går det komplexa systemet att beskriva som en komposition av enklare linära system (moduler) sammakopplade i något vi kallar dynamiska nätverk. Uppgiften att modellera hela eller delar av nätverket kan därmed brytas ner till deluppgiften att modellera en modul i det dynamiska nätverket. Det vanligaste sättet att skatta parametrarna hos en model är genom att minimera det så kallade prediktionsfelet. Den här typen av metod har nyligen anpassats för att identifiera moduler i dynamiska nätverk. Metoden åtnjuter goda egenskaper vad det gäller det modelfel som härrör från stokastisk störningar under experimentet och i de fall där störningarna är normalfördelade sammanfaller metoden med maximum likelihood-metoden. En nackdel med metoden är att functionen som minimeras vanligen är inte är konvex och därmed riskerar metoden att fastna i ett lokalt minimum. Det är därför essentiellt med en bra startpunkt. Andra metoder krävs därmed för att hitta en startpunkt, till exempel kan instrumentvariabelmetoder användas. I den här avhandlingen föreslås en alternativ metod kallad MORSM. MORSM är motiverad med argument hämtade från maximum likelihood och är också asymptotiskt effektiv i vissa fall. MORSM består av steg som kan lösas med minstakvadratmetoden och är därmed beräkningsmässigt attraktiv. Den del av nätverket som är utan intresse skattas enbart ickeparametriskt vilket underlättar valet av modellordning för användaren. En annan utgångspunkt tas i den andra metoden som föreslås för att skatta en modul inbäddad i ett dynamiskt nätverk. Impulssvaret från den del av nätverket som är utan intresse modelleras som realisation av en Gaussisk process. Medelvärdet och kovariansen hos den Gaussiska processen parametriseras av en mängd parametrar kallade hyperparametrar vilka skattas tillsammans med parametrarna för modulen. Parametrarna skattas genom att maximera den marginella likelihood funktionen. Optimeringen utförs iterativt med ECM, en variant av förväntan och maximering algoritmen (EM). Algoritmen har två steg. E-steget har en analytisk lösning medan CM-steget reduceras till delproblem som antingen har analytisk lösning eller har låg dimensionalitet och därmed kan lösas med gradientbaserade metoder. Den övergripande optimeringen är därmed beräkningsmässigt attraktiv. Med hjälp av MCMC tekniker generaliseras metoden till att inkludera ytterligare sensorer vars impulssvar också modelleras som Gaussiska processer. Förutom valet av metod så påverkar valet av signaler vilken nogrannhet eller kovarians den skattade modulen har. Klassiska uttryck för kovariansmatrisen kan användas för att optimera valet av signaler. Dock så ger dessa uttryck ingen insikt i varför valet av vissa signaler är optimalt eller vad som skulle hända om förutsättningarna vore annorlunda. Uttrycken som framställs i den här delen av avhandlingen har ett annat syfte. De försöker i stället uttrycka kovariansen i termer som kan ge insikt i vad som påverkar den nogrannhet som kan uppnås. Mer specifikt uttrycks kovariansen med bland annat avseende på insignalernas spektra, brussignalernas spektra samt modellstruktur. / <p>QC 20170614</p>

This thesis treats the identification of Volterra models of the human smooth pursuit system from eye-tracking data. Smooth pursuit movements are gaze movements used in tracking of moving targets and controlled by a complex biological network involving the eyes and brain. Because of the neural control of smooth pursuit, these movements are affected by a number of neurological and mental conditions, such as Parkinson's disease. Therefore, by constructing mathematical models of the smooth pursuit system from eye-tracking data of the patient, it may be possible to identify symptoms of the disease and quantify them. While the smooth pursuit dynamics are typically linear in healthy subjects, this is not necessarily true in disease or under influence of drugs. The Volterra model is a classical black-box model for dynamical systems with smooth nonlinearities that does not require much a priori information about the plant and thus suitable for modeling the smooth pursuit system. The contribution of this thesis is mainly covered by the four appended papers. Papers I–III treat the problem of reducing the number of parameters in Volterra models with the kernels parametrized in Laguerre functional basis (Volterra–Laguerre models), when utilizing them to capture the signal form of smooth pursuit movements. Specifically, a Volterra–Laguerre model is obtained by means of sparse estimation and principal component analysis in Paper I, and a Wiener model approach is used in Paper II. In Paper III, the same model as in Paper I is considered to examine the feasibility of smooth pursuit eye tracking for biometric purposes. Paper IV is concerned with a Volterra–Laguerre model that includes an explicit time delay. An approach to the joint estimation of the time delay and the finite-dimensional part of the Volterra model is proposed and applied to time-delay compensation in eye-tracking data.