This report aims to describe the latest research and method developmentof image-based multi sensor fusion navigation and summarizes open aerialdatasets which can support the latest research related to this project. Itsupports the initial setting of the direction of the algorithm development inthe early stage of the project.The Multi Sensor Image-based Navigation project aims to study and developthe methods focusing on image-based multisensor navigation in orderto acquire a precise localization of the aircraft. GNSS-based localizationand navigation systems are sensitive to disturbances and jamming, hencethe capability to provide reliable position accuracy without GNSS is a keyelement to develop the navigation systems.The output of this project can be utilized in a wide range of applications,such as aircraft operation in GNSS denied environments or urban air mobilitycontext.

Modeling and failure prediction is an important task in manyengineering systems. For this task, the machine learning literaturepresents a large variety of models such as classification trees,random forest, artificial neural networks, fuzzy systems, amongothers. In addition, standard statistical models can be applied suchas the logistic regression, linear discriminant analysis, $k$-nearestneighbors, among others. This work evaluates advantages andlimitations of statistical and machine learning methods to predictfailures in industrial robots. The work is based on data from morethan five thousand robots in industrial use. Furthermore, a newapproach combining standard statistical and machine learning models,named \emph{hybrid gradient boosting}, is proposed. Results show thatthe a priori treatment of the database, i.e., outlier analysis,consistent database analysis and anomaly analysis have shown to becrucial to improve classification performance for statistical, machinelearning and hybrid models. Furthermore, local joint information hasbeen identified as the main driver for failure detection whereasfailure classification can be improved using additional informationfrom different joints and hybrid models.

A toolbox for parameterizing the ellipse model, that is a control-oriented compressor model, to any given measured compressor map is described in detail in this document. The compressor model has been developed in previous publications and shown to be capable of accurately reproducing the measured data obtained from gas stand measurements, for a wide range of compressors, starting from small automotive applications to large compressors used in marine propulsion. In addition, it has been shown that it is possible to extrapolate both mass flow and efficiency to the unmeasured low speed region of the compressor in a physical way. The parameterization algorithm is based on Total Least Squares (TLS), which is shown here and in previous publications to be a fast and reliable approach to fit the compressor model to the map. The toolbox is implemented in a Matlab Graphical User Interface (GUI) in order to make it easy for the user to parameterize the compressor model. To demonstrate the workflow and ease of use, a complete step-by-step example of how to work with the toolbox is provided. To further facilitate the user in applying the model, the package also provides implementations of the ellipse compressor model both as a Matlab function and as a Simulink block. This way, the user can quickly and reliably use the results of the parameterization process in a desired application, e.g. including the compressor model of a given compressor map in a combustion engine simulation model.

This report contains supplementary material for the paper [1], and gives detailed proofs of all lemmas and theorems that could not be included into the paper due to space limitations. The notation is adapted from the paper.

[1] C. Fritsche, U. Orguner, and F. Gustafsson, “Bobrovsky-Zakai bound for filtering, prediction and smoothing ofnonlinear dynamic systems,” in International Conference on Information Fusion (FUSION), Cambridge, UK, Jul.2018, pp. 1–8.

In this report, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed by Reece and Nicholson [1] is revisited. It turns out that the general results presented in [1] are incorrect, as some expectations appearing in the information matrix recursions are missing. This report presents the corrected results and it is argued that the missing expectations are only zero in a number of special cases. A nonlinear toy example is used to illustrate when this is not the case.

In this report, the parametric Cramér-Rao lower bound for the smoothing problem is derived.

This reportontains supplementary material for the paper [1].

We are interested in Bayesian modelling of panel data using a mixed effects model with heterogeneity in the individual random effects. We compare two different approaches for modelling the heterogeneity using a mixture of Gaussians. In the first model, we assume an infinite mixture model with a Dirichlet process prior, which is a non-parametric Bayesian model. In the second model, we assume an over-parametrised finite mixture model with a sparseness prior. Recent work indicates that the second model can be seen as an approximation of the former. In this paper, we investigate this claim and compare the estimates of the posteriors and the mixing obtained by Gibbs sampling in these two models. The results from using both synthetic and real-world data supports the claim that the estimates of the posterior from both models agree even when the data record is finite.

Interaction measures quantify the input-output relations in MIMO processes and can support the selection of control structures (CSS). Interaction measures are typically computed based on an existing process models. The study of input-output interactions based on data can complement missing information on a model, e.g., revealing unknown relations in a complex system or adjusting for a time dependent behavior. This paper presents a unified approach for data-driven estimation of Gramian based interaction measures from input-output data. Given open or closed-loop data, a high-order Vector ARX (VARX) model is identified and its parameters are used to calculate predictor Markov parameters, together with a covariance estimate. Three interaction measures based on the Hankel, Hilbert-Schmidt-Hankel and H2 norms are calculated from the estimated predictor Markov parameters and uncertainty estimates are provided for the last two, allowing for robust CSS. A solution which is recursive in the data points is presented, making it practical for applications to large datasets. The approach is verified through simulations and several possible extensions are discussed. As the method is suitable for open and closed-loop data and for large datasets, it opens up for data-driven control structure selection based on operational data from entire plants.

The interest for system identification in dynamic networks has increased recently with a wide variety of applications. In many cases, it is intractable or undesirable to observe all nodes in a network and thus, to estimate the complete dynamics. If the complete dynamics is not desired, it might even be challenging to estimate a subset of the network if key nodes are unobservable due to correlation between the nodes. In this contribution, we will discuss an approach to treat this problem. The approach relies on additional measurements that are dependent on the unobservable nodes and thus indirectly contain information about them. These measurements are used to form an alternative indirect model that is only dependent on observed nodes. The purpose of estimating this indirect model can be either to recover information about modules in the original network or to make accurate predictions of variables in the network. Examples are provided for both recovery of the original modules and prediction of nodes.

This report describes the motivation, the current state and the future actions of an improvement process in engineering education at the Brazilian higher education institution called the Military Institute of Engineering. Based on the reasons for why and how to change, the CDIO framework was chosen, at the end of 2014, as the kernel of this improvement process. The activities realized, the plan for the future actions and the open questions are presented in this report.

This technical report gives analytical formulas for the mean and covariancematrix of a multivariate normal distribution with one componenttruncated from both below and above.

This report contains supplementary material for the paper, and gives detailed proofs of all theorems and lemmas that could not be included into the paper due to space limitations.

This report contains supplementary material for the paper, and gives detailed proofs of all lemmas and propositions that could not be included into the paper due to space limitations. The notation is adaptedfrom the paper.

In recent years, micro-machined electromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors, discussing different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter implementations.

The first challenge in robustness analysis of large-scale interconnected uncertain systems is to provide a model of such systems in a standard-form that is required within different analysis frameworks. This becomes particularly important for large-scale systems, as analysis tools that can handle such systems heavily rely on the special structure within such model descriptions. We here propose an automated framework for providing such models of large-scale interconnected uncertain systems that are used in Integral Quadratic Constraint (IQC) analysis. Specifically, in this paper we put forth a methodological way to provide such models from a block-diagram and nested description of interconnected uncertain systems. We describe the details of this automated framework using an example.

Conditions for the existence of controllers for lineartime-varying (LTV) systems is given. The closed loop performance isspecified in terms of energy gain, which also includes terminalconstraints. The conditions can be formulated either as linear matrixinequalities (LMIs) or as solutions to Riccati differental equationswith algebraic constraints.

A ship's roll dynamics is very sensitive to changes in the loading conditions and a worst-case scenario is the loss of stability.  This paper proposes an approach for online estimation of a ship's mass and center of mass. Instead of focusing on a sensor-rich environment where all possible signals on a ship can be measured and a complete model of the ship can be estimated, a minimal approach is adopted. A model of the roll dynamics is derived from a well-established model in literature and it is assumed that only motion measurements from an inertial measurement unit together with measurements of the rudder angle are available. Furthermore, identifiability properties and disturbance characteristics of the model are presented. Due to the properties of the model, the parameters are estimated with an iterative instrumental variable approach to mitigate the influence of the disturbances and it uses multiple datasets simultaneously to overcome identifiability issues. Finally, a simulation study is presented to investigate the sensitivity to the initial conditions and it is shown that there is a low sensitivity for the desired parameters.

A common issue with many system identification problems is that the true input to the system is unknown. In this paper, a framework, based on indirect input measurements, is proposed to solve the problem when the input is partially or fully unknown, and cannot be measured directly. The approach relies on measurements that indirectly contain information about the unknown input. The resulting indirect model formulation, with both direct- and indirect input measurements as inputs, can be used to estimate the desired model of the original system. Due to the similarities with closed-loop system identification, an iterative instrumental variable method is proposed to estimate the indirect model. To show the applicability of the proposed method, it is applied to data from an inverted pendulum experiment with good results.

A ship's roll dynamics is sensitive to the mass and mass distribution. Changes in these physical properties might introduce unpredictable behavior of the ship and a {worst-case} scenario is that the ship will capsize. In this paper, a recently proposed approach for online estimation of mass and center of mass is validated using experimental data. The experiments were performed using a scale model of a ship in a wave basin. The data was collected in free run experiments where the rudder angle was recorded and the ship's motion was measured using an inertial measurement unit. The motion measurements are used in conjunction with a model of the roll dynamics to estimate the desired properties. The estimator uses the rudder angle measurements together with an instrumental variable method to mitigate the influence of disturbances. The experimental study shows that the properties can be estimated with quite good accuracy but that variance and robustness properties can be improved further.

This report describes the modeling of a sounding rocket using a linear time varying model. Different control strategies are investigated to control the time varying model.

In this technical report, some derivations for the filter and smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes filter and smoother for state space models with skew t likelihood proposed in [1] are presented.

In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.

[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.

Model Predictive Control (MPC) is increasing in popularity in industry as more efficient algorithms for solving the related optimization problem are developed. The main computational bottle-neck in on-line MPC is often the computation of the search step direction, \ie the Newton step, which is often done using generic sparsity exploiting algorithms or Riccati recursions. However, as parallel hardware is becoming increasingly popular the demand for efficient parallel algorithms for solving the Newton step is increasing. In this paper a tailored, non-iterative parallel algorithm for computing the Riccati factorization is presented. The algorithm exploits the special structure in the MPC problem, and when sufficiently many processing units are available, the complexity of the algorithm scales logarithmically in the prediction horizon. Computing the Newton step is the main computational bottle-neck in many MPC algorithms and the algorithm can significantly reduce the computation cost for popular state-of-the-art MPC algorithms.

The use of Model Predictive Control in industry is steadily increasing as more complicated problems can be addressed. Due to that online optimization is usually performed, the main bottleneck with Model Predictive Control is the relatively high computational complexity. Hence, a lot of research has been performed to find efficient algorithms that solve the optimization problem. As parallelism is becoming more commonly used in hardware, the demand for efficient parallel solvers for Model Predictive Control has increased. In this paper, a tailored parallel algorithm that can adopt different levels of parallelism for solving the Newton step is presented. With sufficiently many processing units, it is capable of reducing the computational growth to logarithmic growth in the prediction horizon. Since the Newton step computation is where most computational effort is spent in both interior-point and active-set solvers, this new algorithm can significantly reduce the computational complexity of highly relevant solvers for Model Predictive Control.

We propose a novel method for MAP parameter inference in nonlinear state space models with intractable likelihoods. The method is based on a combination of Gaussian process optimisation (GPO), sequential Monte Carlo (SMC) and approximate Bayesian computations (ABC). SMC and ABC are used to approximate the intractable likelihood by using the similarity between simulated realisations from the model and the data obtained from the system. The GPO algorithm is used for the MAP parameter estimation given noisy estimates of the log-likelihood. The proposed parameter inference method is evaluated in three problems using both synthetic and real-world data. The results are promising, indicating that the proposed algorithm converges fast and with reasonable accuracy compared with existing methods.

Recent development has renewed the interest in drivetrain concepts which gives a higher degree offreedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for activecontrol, which especially during transients is not trivial, but of which the quality is crucial for the successof the drivetrain concept. In this work the fuel optimal engine operating point trajectories for a naturallyaspirated and a turbocharged diesel engine, connected to a load which does not restrict the engine speed,is derived, analysed and utilized for finding a suboptimal operating point trajectory. The analysis andoptimization is made with dynamic programming, Pontryagin’s maximum principle and a suboptimalstrategy based on the static optimal operating points. Methods are derived for using Pontryagin’smaximum principle for finding the optimal operating point trajectories, for simple load cases. The timeneeded for computation is reduced a factor 1000−100, depending on engine layout, compared to dynamicprogramming. These methods are only applicable to very simple load cases though. Finally, a suboptimalcalculation method which reduce the time needed for computation a factor > 1000 compared to dynamicprogramming, while showing a < 5% increase in fuel consumption compared to the optimal, is presented.

This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback-Leibler distance. To decrease sensitivity to unknown disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The method was developed with interests in industrial robotics where a repetitive behavior is commonly found. The problem of wear monitoring in a robot joint is studied based on data collected from a test-cycle. Real data from accelerated wear tests and simulations are considered. Promising results are achieved where the method output shows a clear response to the wear increases.

Engine misfire detection is an important part of the On-Board Diagnostics (OBDII) legislations to reduce exhaust emissions and avoid damage to the catalytic converters. The flywheel angular velocity signal is analyzed, investigating how to use the signal in order to best detect misfires. An algorithm for engine misfire detection is proposed based on the flywheel angular velocity signal. The flywheel signal is used to estimate the torque at the flywheel and a test quantity is designed by weighting and thresholding the samples of estimated torque related to one combustion. During the development process, the Kullback-Leibler divergence is used to analyze the ability to detect a misfire given a test quantity and how the misfire detectability performance varies depending on, e.g., load and speed. The Kullback-Leibler divergence is also used for parameter optimization to maximize the difference between misfire data and fault-free data. Evaluation shows that the proposed misfire detection algorithm is able to have a low probability of false alarms while having a low probability of missed detections.

The Swedish nuclear waste will be stored in copper canisters and kept isolated deep under ground for at least 100,000 years. To ensure reliable sealing of the canisters, friction stir welding is utilized. To repetitively produce high quality welds, it is vital to use automatic control of the process. A decentralized solution is designed based on an already existing temperature controller and a proposed linear plunge depth controller. The plunge depth control is challenging mainly because of deection in the machine, thermal expansion and cross couplings in the process. The decentralized controller has been implemented and evaluated on the real system with good results, keeping the plunge depth within the necessary 0:1 mm of its setpoint at the same time as the temperature specications are met.

The iterative learning control (ILC) method improvesperformance of systems that repeat the same task several times. In this paper the standard norm-optimal ILC control law for linear systems is extended to an estimation-based ILC algorithm where the controlled variables are not directly available as measurements. The proposed ILC algorithm is proven to be stable and gives monotonic convergence of the error. The estimation-based part of the algorithm uses Bayesian estimation techniques such as the Kalman filter. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. It is further shown that for linear time-invariant systems the ILC design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ILC algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and verified in a simulation study with a simplified model of an industrial manipulator system.

An H_∞ synthesis method for control of a flexible joint, with non-linear spring characteristic, is proposed. The first step of the synthesis method is to extend the joint model with an uncertainty description of the stiffness parameter. In the second step, a non-linear optimisation problem, based on nominal performance and robust stability requirements, has to be solved. Using the Lyapunov shaping paradigm and a change of variables, the non-linear optimisation problem can be rewritten as a convex, yet conservative, LMI problem. The method is motivated by the assumption that the joint operates in a specific stiffness region of the non-linear spring most of the time, hence the performance requirements are only valid in that region. However, the controller must stabilise the system in all stiffness regions. The method is validated in simulations on a non-linear flexible joint model originating from an industrial robot.

Control of a flexible joint of an industrial manipulator using H_∞ design methods is presented. The considered design methods are i) mixed-H_∞ design, and ii) H_∞ loop shaping design. Two different controller configurations are examined: one uses only the actuator position, while the other uses the actuator position and the acceleration of end-effector. The four resulting controllers are compared to a standard PID controller where only the actuator position is measured. The choices of the weighting functions are discussed in details. For the loop shaping design method, the acceleration measurement is required to improve the performance compared to the PID controller. For the mixed-H_∞ method it is enough to have only the actuator position to get an improved performance. Model order reduction of the controllers is briefly discussed, which is important for implementation of the controllers in the robot control system.

Simultaneous Localisation and Mapping (SLAM) denotes the problem of jointly localizing a moving platform and mapping the environment. This work studies the SLAM problem using a combination of inertial sensors, measuring the platform's accelerations and angular velocities, and a monocular camera observing the environment. We formulate the SLAM problem on a nonlinear least squares (NLS) batch form, whose solution provides a smoothed estimate of the motion and map. The NLS problem is highly nonconvex in practice, so a good initial estimate is required. We propose a multi-stage iterative procedure, that utilises the fact that the SLAM problem is linear if the platform's rotations are known. The map is initialised with camera feature detections only, by utilising feature tracking and clustering of  feature tracks. In this way, loop closures are automatically detected. The initialization method and subsequent NLS refinement is demonstrated on both simulated and real data.

The effects of wear to friction are studied based on constant-speed friction data collected from dedicated experiments during accelerated wear tests. It is shown how the effects of temperature and load uncertainties produce larger changes to friction than those caused by wear, motivating the consideration of these effects. Based on empirical observations, an extended friction model is proposed to describe the effects of speed, load, temperature and wear. Assuming availability of such model and constant-speed friction data, a maximum likelihood wear estimator is proposed.  A criterion for experiment design is proposed which selects speed points to collect constant-speed friction data which improves the achievable performance bound for any unbiased wear estimator. Practical issues related to experiment length are also considered. The performance of the wear estimator under load and temperature uncertainties is found by means of simulations and verified under three case studies based on real data.

Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.

Indoor positioning in unknown environments is crucial for rescue personnel and future infotainment systems. Dead-reckoning inertial sensor data gives accurate estimate of distance, for instance using zero velocity updates, while the heading estimation problem is inherently more difficult due to the large degree of magnetic disturbances indoors. We propose a Kalman filter bank approach based on supporting a magnetic compass with gyroscope turn rate information, where a hidden Markov model is used to model the presence of magnetic disturbances. In parallel, we suggest to run a robust heading estimation system based on data from a sliding window. The robust estimate is used to detect filter divergence, and to restart the filter when needed. The underlying assumptions and the heading estimation performance are supported in field trials using more than 500 data sets from more than 50 venues in 5 continents.

The problem of estimating heading is central in the indoor positioning problem based on mea- surements from inertial measurement and magnetic units. Integrating rate of turn angular rate gives the heading with unknown initial condition and a linear drift over time, while the magnetometer gives absolute heading, but where long segments of data are useless in prac- tice because of magnetic disturbances. A basic Kalman filter approach with outlier rejection has turned out to be difficult to use with high integrity. Here, we propose an approach based on convex optimization, where segments of good magnetometer data are separated from disturbed data and jointly fused with the yaw rate measurements. The optimization framework is flexible with many degrees of freedom in the modeling phase, and we outline one design. A recursive solution to the optimization is derived, which has a computational complexity comparable to the simplest possible Kalman filter. The performance is evaluated using data from a handheld smartphone for a large amount of indoor trajectories, and the result demonstrates that the method effectively resolves the magnetic disturbances.

Synthetic Aperture Radar (SAR) equipment is a radar imaging system that can be used to create high resolution images of a scene by utilising the movement of a flying platform. Knowledge of the platform's trajectory is essential to get good and focused images. An emerging application field is real-time SAR imaging using small and cheap platforms with poorer navigation systems implying unfocused images. This contribution investigatesa joint estimation of the trajectory and SAR image.

For future advanced active safety systems, in road-vehicle applications, there will arise possibilities for enhanced vehicle control systems, due to refinements in, e.g., situation awareness systems. To fully utilize this, more extensive knowledge is required regarding the characteristics and dynamics of vehicle models employed in these systems. Motivated by this, an evaluative study for the lateral dynamics is performed, considering vehicle models of more simple structure. For this purpose, a platform for vehicle dynamics studies has been developed. Experimental data, gathered with this testbed, is then used for model parametrization, succeeded by evaluation for an evasive maneuver. The considered model configurations are based on the single-track model, with different additional attributes, such as tire-force saturation, tire-force lag, and roll dynamics. The results indicate that even a basic model, such as the single-track with tire-force saturation, can describe the lateral dynamics surprisingly well for this critical maneuver.

A Wiener model is a fairly simple, well known, and often used nonlinearblock-oriented black-box model. A possible generalization of the class ofWiener models lies in the parallel Wiener model class. This paper presents amethod to estimate the linear time-invariant blocks of such parallel Wienermodels from input/output data only. The proposed estimation methodcombines the knowledge obtained by estimating the best linear approxima-tion of a nonlinear system with a dimension reduction method to estimatethe linear time-invariant blocks present in the model. The estimation of thestatic nonlinearity is fairly easy once the linear blocks are known.

Prediction and filtering of continuous-time stochastic processes  require a solver of a continuous-time differential Lyapunov equation (CDLE).   Even though this can be recast into an ordinary differential equation (ODE),  where standard solvers can be applied, the dominating approach in  Kalman filter applications is to discretize the system and then  apply the discrete-time difference Lyapunov equation (DDLE). To avoid problems with  stability and poor accuracy, oversampling is often used. This  contribution analyzes over-sampling strategies, and proposes a  low-complexity analytical solution that does not involve  oversampling. The results are illustrated on Kalman filtering  problems in both linear and nonlinear systems.

Vid byggnation och underhåll av infrastruktur för järnväg används stora mängder av olika material, vilket medför stor miljöpåverkan från de tidiga produktionsstegen, till exempel råvaruutvinning. Hittills har Trafikverket inte haft något uttalad livscykeltänkande i sitt arbete med upphandlingar. Trafikverket behöver arbeta med miljöledning av nya produkter och välja de mest resurssnåla produkterna i ett livscykelperspektiv. En bättre planerad och förebyggande verksamhet för drift och underhåll skulle möjliggöra förlängd livslängd för järnvägsprodukter.

Integrerade produkt‐ och tjänsteerbjudanden (till exempel funktions‐ eller resultatorienterade kontrakt) är en affärsmodell som används av allt fler företag. Affärsmodelltypen benämns ofta även som funktionsförsäljning och kan beskrivas som ett livscykelkontrakt med funktionsåtagande. Tidigare forskning har visat att denna typ av affärsmodell, ofta ökar drivkrafterna för förändring och därmed ökad kosteffektivitet och kvalitet ur ett livscykelperspektiv.

Det övergripande syftet med det här projektet är att;

ta fram metoder, som stödjer Trafikverket, att utveckla sina sätt att utformaupphandlingsspecifikationer.

Mer specifikt så har det undersöks om integrerade produkt‐ och tjänsteerbjudande kan förbättra förvaltningen av järnvägsinfrastruktur, och vad skulle i sådant fall en implementering innebära för riskfaktorer samt hur kan kontrakten utvärderas ekonomiskt‐ och miljömässigt?

This report contains pseudo-code for, and a computational complexity analysis of, the Gaussian inverse Wishart Probability Hypothesis Density filter.

Exploration is in this work defined as the task of efficient information gathering of areas, building, roads, etc., by controlling a pan/tilt camera on a sensor platform. Good exploration is characterized by several images from different directions of the areas of interests and that the images can be used to create maps, video mosaics and multi-view imagery for anomaly and change detection. In this paper an aerial exploration framework based on the information filter is presented. The work is inspired by research on optimal trajectory for bearings-only tracking. A number of static grid points represent the area to be explored and the problem is to plan the trajectory of the sensor platform and the pointing direction of the camera to maximize the exploration performance of the grid points.

We propose a method for model reduction on a given frequency range, without having to specify input and output filter weights. The method uses a nonlinear optimization approach where we formulate a H2 like cost function which only takes the given frequency range into account. We derive a gradient of the proposed cost function which enables us to use off-the-shelf optimization software. 

Optimizing the configuration of a wheel loader to customer needs can lead to a significant increase in efficiency with respect to fuel economy, cost, component dimensioning etc. Experience show that even modest customer adaptation can save around 20% of fuel cost. A key motivator for this work is that wheel loader manufacturers in general does not have full information about customer usage of the machine and the main objective here is to develop an algorithm that automatically, using only production sensors, extracts information about the usage of a machine at a specific customer site. Two main challenges are that sensors are not located with respect to this task and the significant usage disturbances that typically occur during operation. The proposed solution is a robust method, based on a mix of techniques using basic signal processing, state automaton techniques, and parameter estimation algorithms. A key property of the method is the method of combining, individually very simple, basic techniques in a scheme where robustness are introduced. The approach is evaluated on measured data of a wheel loader loading gravel and shot rock.

This technical report summarizes a number of results for the multivariate t distribution which can exhibit heavier tails than the Gaussian distribution. It is shown how t random variables can be generated, the probability density function (pdf) is derived, and marginal and conditional densities of partitioned t random vectors are presented. Moreover, a brief comparison with the multivariate Gaussian distribution is provided. The derivations of several results are given in an extensive appendix.

A common assumption when proving stability of linear MPC algorithms fort racking applications is to assume that the desired setpoint is located farinto the interior of the feasible set. The reason for this is that the terminal state constraint set which is centered around the setpoint must be contained within the feasible set. In many applications this assumption can be severly limiting since the terminal set is relatively large and therefore limits how close the setpoint can be to the boundary of the feasible set. We present simple modifications that can be performed in order to guarantee stability and convergence to setpoints located arbitrarily close to the boundary of the feasible set. The main idea is to introduce a scaling variable which dynamically scales the terminal state constraint set and therefore allowsa setpoint to be located arbitrarily close to the boundary. In addition to this the concept of pseudo setpoints are used to gain the maximum possible region of attraction and to handle infeasible references. Recursive feasibility and convergence to the desired setpoint, or its closest feasible alternative, is proven and a motivating example of controlling an agile fighter aircraftis given.