Linköpings universitet Institutionen för systemteknik
Overview
Works:  980 works in 1,138 publications in 1 language and 1,111 library holdings 

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Most widely held works by
Linköpings universitet
Kalman Filters for Nonlinear Systems and HeavyTailed Noise by
Michael Roth(
)
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for nonlinear systems, where the optimal Bayesian filtering recursions cannot be solved exactly. These algorithms rely on the computation of certain expected values. Second, the problem of filtering in linear systems that are subject to heavytailed process and measurement noise is addressed. Expected values of nonlinearly transformed random vectors are an essential ingredient in any Kalman filter for nonlinear systems, because of the required joint mean vector and joint covariance of the predicted state and measurement. The problem of computing expected values, however, goes beyond the filtering context. Insights into the underlying integrals and useful simplification schemes are given for elliptically contoured distributions, which include the Gaussian and Student's t distribution. Furthermore, a number of computation schemes are discussed. The focus is on methods that allow for simple implementation and that have an assessable computational cost. Covered are basic Monte Carlo integration, deterministic integration rules and the unscented transformation, and schemes that rely on approximation of involved nonlinearities via Taylor polynomials or interpolation. All methods come with realistic accuracy statements, and are compared on two instructive examples. Heavytailed process and measurement noise in state space models can be accounted for by utilizing Student's t distribution. Based on the expressions forconditioning and marginalization of t random variables, a compact filtering algorithm for linear systems is derived. The algorithm exhibits some similarities with the Kalman filter, but involves nonlinear processing of the measurements in form of a squared residual in one update equation. The derived filter is compared to stateoftheart filtering algorithms on a challenging target tracking example, and outperforms all but one optimal filter that knows the exact instances at which outliers occur. The presented material is embedded into a coherent thesis, with a concise introduction to the Bayesian filtering and state estimation problems; an extensive survey of available filtering algorithms that includes the Kalman filter, Kalman filters for nonlinear systems, and the particle filter; and an appendix that provides the required probability theory basis
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for nonlinear systems, where the optimal Bayesian filtering recursions cannot be solved exactly. These algorithms rely on the computation of certain expected values. Second, the problem of filtering in linear systems that are subject to heavytailed process and measurement noise is addressed. Expected values of nonlinearly transformed random vectors are an essential ingredient in any Kalman filter for nonlinear systems, because of the required joint mean vector and joint covariance of the predicted state and measurement. The problem of computing expected values, however, goes beyond the filtering context. Insights into the underlying integrals and useful simplification schemes are given for elliptically contoured distributions, which include the Gaussian and Student's t distribution. Furthermore, a number of computation schemes are discussed. The focus is on methods that allow for simple implementation and that have an assessable computational cost. Covered are basic Monte Carlo integration, deterministic integration rules and the unscented transformation, and schemes that rely on approximation of involved nonlinearities via Taylor polynomials or interpolation. All methods come with realistic accuracy statements, and are compared on two instructive examples. Heavytailed process and measurement noise in state space models can be accounted for by utilizing Student's t distribution. Based on the expressions forconditioning and marginalization of t random variables, a compact filtering algorithm for linear systems is derived. The algorithm exhibits some similarities with the Kalman filter, but involves nonlinear processing of the measurements in form of a squared residual in one update equation. The derived filter is compared to stateoftheart filtering algorithms on a challenging target tracking example, and outperforms all but one optimal filter that knows the exact instances at which outliers occur. The presented material is embedded into a coherent thesis, with a concise introduction to the Bayesian filtering and state estimation problems; an extensive survey of available filtering algorithms that includes the Kalman filter, Kalman filters for nonlinear systems, and the particle filter; and an appendix that provides the required probability theory basis
Estimation of Inverse Models Applied to Power Amplifier Predistortion by
Ylva Jung(
)
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
Mathematical models are commonly used in technical applications to describe the behavior of a system. These models can be estimated from data, which is known as system identification. Usually the models are used to calculate the output for a given input, but in this thesis, the estimation of inverse models is investigated. That is, we want to find a model that can be used to calculate the input for a given output. In this setup, the goal is to minimize the difference between the input and the output from the cascaded systems (system and inverse). A good model would be one that reconstructs the original input when used in series with the original system. Different methods for estimating a system inverse exist. The inverse model can be based on a forward model, or it can be estimated directly by reversing the use of input and output in the identification procedure. The models obtained using the different approaches capture different aspects of the system, and the choice of method can have a large impact. Here, it is shown in a small linear example that a direct estimation of the inverse can be advantageous, when the inverse is supposed to be used in cascade with the system to reconstruct the input. Inverse systems turn up in many different applications, such as sensor calibration and power amplifier (PA) predistortion. PAs used in communication devices can be nonlinear, and this causes interference in adjacent transmitting channels, which will be noise to anyone that transmits in these channels. Therefore, linearization of the amplifier is needed, and a prefilter is used, called a predistorter. In this thesis, the predistortion problem has been investigated for a type of PA, called outphasing power amplifier, where the input signal is decomposed into two branches that are amplified separately by highly efficient nonlinear amplifiers, and then recombined. If the decomposition and summation of the two parts are not perfect, nonlinear terms will be introduced in the output, and predistortion is needed. Here, a predistorter has been constructed based on a model of the PA. In a first method, the structure of the outphasing amplifier has been used to model the distortion, and from this model, a predistorter can be estimated. However, this involves solving two nonconvex optimization problems, and the risk of obtaining a suboptimal solution. Exploring the structure of the PA, the problem can be reformulated such that the PA modeling basically can be done by solving two leastsquares (LS) problems, which are convex. In a second step, an analytical description of an ideal predistorter can be used to obtain a predistorter estimate. Another approach is to compute the predistorter without a PA model by estimating the inverse directly. The methods have been evaluated in simulations and in measurements, and it is shown that the predistortion improves the linearity of the overall power amplifier system
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
Mathematical models are commonly used in technical applications to describe the behavior of a system. These models can be estimated from data, which is known as system identification. Usually the models are used to calculate the output for a given input, but in this thesis, the estimation of inverse models is investigated. That is, we want to find a model that can be used to calculate the input for a given output. In this setup, the goal is to minimize the difference between the input and the output from the cascaded systems (system and inverse). A good model would be one that reconstructs the original input when used in series with the original system. Different methods for estimating a system inverse exist. The inverse model can be based on a forward model, or it can be estimated directly by reversing the use of input and output in the identification procedure. The models obtained using the different approaches capture different aspects of the system, and the choice of method can have a large impact. Here, it is shown in a small linear example that a direct estimation of the inverse can be advantageous, when the inverse is supposed to be used in cascade with the system to reconstruct the input. Inverse systems turn up in many different applications, such as sensor calibration and power amplifier (PA) predistortion. PAs used in communication devices can be nonlinear, and this causes interference in adjacent transmitting channels, which will be noise to anyone that transmits in these channels. Therefore, linearization of the amplifier is needed, and a prefilter is used, called a predistorter. In this thesis, the predistortion problem has been investigated for a type of PA, called outphasing power amplifier, where the input signal is decomposed into two branches that are amplified separately by highly efficient nonlinear amplifiers, and then recombined. If the decomposition and summation of the two parts are not perfect, nonlinear terms will be introduced in the output, and predistortion is needed. Here, a predistorter has been constructed based on a model of the PA. In a first method, the structure of the outphasing amplifier has been used to model the distortion, and from this model, a predistorter can be estimated. However, this involves solving two nonconvex optimization problems, and the risk of obtaining a suboptimal solution. Exploring the structure of the PA, the problem can be reformulated such that the PA modeling basically can be done by solving two leastsquares (LS) problems, which are convex. In a second step, an analytical description of an ideal predistorter can be used to obtain a predistorter estimate. Another approach is to compute the predistorter without a PA model by estimating the inverse directly. The methods have been evaluated in simulations and in measurements, and it is shown that the predistortion improves the linearity of the overall power amplifier system
Particle filters and Markov chains for learning of dynamical systems by
Fredrik Lindsten(
Book
)
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and nonGaussian statespace models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods. Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated statetrajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forwardonly fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of nonMarkovian latent variable models. Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known RaoBlackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by RaoBlackwellization. Furthermore, a RaoBlackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear statespace models. The idea of RaoBlackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear statespace models with affine parameter dependencies
3 editions published in 2013 in English and held by 3 WorldCat member libraries worldwide
Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and nonGaussian statespace models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods. Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated statetrajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forwardonly fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of nonMarkovian latent variable models. Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known RaoBlackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by RaoBlackwellization. Furthermore, a RaoBlackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear statespace models. The idea of RaoBlackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear statespace models with affine parameter dependencies
Residual Generation Methods for Fault Diagnosis with Automotive Applications by
Carl Svärd(
Book
)
3 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance. One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is modelbased fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is faultfree, and nonzero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation. The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by DifferentialAlgebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observerbased residual generation for linear DAEmodels. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in statespace form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. nullspace calculations and equivalence transformations. In the second method, nonlinear DAEmodels are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system
3 editions published in 2009 in English and held by 3 WorldCat member libraries worldwide
The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance. One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is modelbased fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is faultfree, and nonzero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation. The main contributions in this thesis are two novel methods for residual generation. In both methods, systems described by DifferentialAlgebraic Equation (DAE) models are considered. Such models appear in a large class of technical systems, for example automotive systems. The first method consider observerbased residual generation for linear DAEmodels. This method places no restrictions on the model, such as e.g. observability or regularity, in comparison with other previous methods. If the faults of interest can be detected in the system, the output from the design method is a residual generator, in statespace form, that is sensitive to the faults of interest. The method is iterative and relies on constant matrix operations, such as e.g. nullspace calculations and equivalence transformations. In the second method, nonlinear DAEmodels are considered. The proposed method belongs to a class of methods, in this thesis referred to as sequential residual generation, which has shown to be successful for real applications. This method enables simultaneous use of integral and derivative causality, and is able to handle equation sets corresponding to algebraic and differential loops in a systematic manner. It relies on a formal framework for computing unknown variables in the model according to a computation sequence, in which the analytical properties of the equations in the model as well as the available tools for equation solving are taken into account. The method is successfully applied to complex models of an automotive diesel engine and a hydraulic braking system
Inertial Navigation and Mapping for Autonomous Vehicles by
Martin Skoglund(
Book
)
3 editions published in 2014 in English and held by 3 WorldCat member libraries worldwide
Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below. We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a socalled simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship. Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined. The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications. We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear leastsquares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKFSLAM. We show how NLSSLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKFSLAM. The results obtained using NLSSLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended RauchTungStriebel smoother while the second problem is solved with a quasiNewton method. The results using EMSLAM are better than NLSSLAM both in terms of accuracy and complexity
3 editions published in 2014 in English and held by 3 WorldCat member libraries worldwide
Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below. We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a socalled simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship. Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined. The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications. We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear leastsquares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKFSLAM. We show how NLSSLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKFSLAM. The results obtained using NLSSLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended RauchTungStriebel smoother while the second problem is solved with a quasiNewton method. The results using EMSLAM are better than NLSSLAM both in terms of accuracy and complexity
Control of EGR and VGT for emission control and pumping work minimization in diesel engines by
Johan Wahlström(
)
3 editions published between 2006 and 2009 in English and held by 3 WorldCat member libraries worldwide
Legislators steadily increase the demands on lowered emissions from heavy duty vehicles. To meet these demands it is necessary to integrate technologies like Exhaust Gas Recirculation (EGR) and Variable Geometry Turbochargers (VGT) together with advanced control systems. A control structure with PID controllers and selectors is proposed and investigated for coordinated control of EGR valve and VGT position in heavy duty diesel engines. Main control goals are to fulfill the legislated emission levels, to reduce the fuel consumption, and to fulfill safe operation of the turbocharger. These goals are achieved through regulation of normalized oxygen/fuel ratio and intake manifold EGRfraction. These are chosen as main performance variables since they are strongly coupled to the emissions, compared to manifold pressure or air mass flow, which makes it easy to adjust setpoints depending on e.g. measured emissions during an emission calibration process. In addition a mechanism for fuel efficient operation is incorporated in the structure, this is achieved by minimizing the pumping work. To design a successful control structure, a mean value model of a diesel engine is developed and validated. The intended applications of the model are system analysis, simulation, and development of modelbased control systems. Model equations and tuning methods for the model parameters are described for each subsystem in the model. Static and dynamic validations of the entire model show mean relative errors that are less than 12%. Based on a system analysis of the model, a key characteristic behind the control structure is that oxygen/fuel ratio is controlled by the EGRvalve and EGRfraction by the VGTposition, in order to handle a sign reversal in the system from VGT to oxygen/fuel ratio. For efficient calibration an automatic controller tuning method is developed. The controller objectives are captured in a cost function, that is evaluated utilizing a method choosing representative transients. The performance is evaluated on the European Transient Cycle. It is demonstrated how the weights in the cost function influence behavior, and that the tuning method is important in order to improve the control performance compared to if only a standard method is used. It is also demonstrated that the controller structure performs well regarding all control objectives. In combination with its efficient tuning, the controller structure thus fulfills all requirements for successful application
3 editions published between 2006 and 2009 in English and held by 3 WorldCat member libraries worldwide
Legislators steadily increase the demands on lowered emissions from heavy duty vehicles. To meet these demands it is necessary to integrate technologies like Exhaust Gas Recirculation (EGR) and Variable Geometry Turbochargers (VGT) together with advanced control systems. A control structure with PID controllers and selectors is proposed and investigated for coordinated control of EGR valve and VGT position in heavy duty diesel engines. Main control goals are to fulfill the legislated emission levels, to reduce the fuel consumption, and to fulfill safe operation of the turbocharger. These goals are achieved through regulation of normalized oxygen/fuel ratio and intake manifold EGRfraction. These are chosen as main performance variables since they are strongly coupled to the emissions, compared to manifold pressure or air mass flow, which makes it easy to adjust setpoints depending on e.g. measured emissions during an emission calibration process. In addition a mechanism for fuel efficient operation is incorporated in the structure, this is achieved by minimizing the pumping work. To design a successful control structure, a mean value model of a diesel engine is developed and validated. The intended applications of the model are system analysis, simulation, and development of modelbased control systems. Model equations and tuning methods for the model parameters are described for each subsystem in the model. Static and dynamic validations of the entire model show mean relative errors that are less than 12%. Based on a system analysis of the model, a key characteristic behind the control structure is that oxygen/fuel ratio is controlled by the EGRvalve and EGRfraction by the VGTposition, in order to handle a sign reversal in the system from VGT to oxygen/fuel ratio. For efficient calibration an automatic controller tuning method is developed. The controller objectives are captured in a cost function, that is evaluated utilizing a method choosing representative transients. The performance is evaluated on the European Transient Cycle. It is demonstrated how the weights in the cost function influence behavior, and that the tuning method is important in order to improve the control performance compared to if only a standard method is used. It is also demonstrated that the controller structure performs well regarding all control objectives. In combination with its efficient tuning, the controller structure thus fulfills all requirements for successful application
Fault isolation in distributed embedded systems by
Jonas Biteus(
Book
)
3 editions published in 2007 in English and held by 3 WorldCat member libraries worldwide
To improve safety, reliability, and efficiency of automotive vehicles and other technical applications, embedded systems commonly use fault diagnosis consisting of fault detection and isolation. Since many systems are constructed as distributed embedded systems including multiple control units, it is necessary to perform global fault isolation using for example a central unit. However, the drawbacks with such a centralized method are the need of a powerful diagnostic unit and the sensitivity against disconnections of this unit. Two alternative methods to centralized fault isolation are presented in this thesis. The first method performs global fault isolation by a istributed sequential computation. For a set of studied systems, themethod gives, compared to a centralizedmethod, amean reduction inmaximumprocessor load on any unitwith 40 and 70%for systems consisting of four and eight units respectively. The second method instead extends the result of the local fault isolation performed in each unit such that the results are globally correct. By only considering the components affecting each specific unit, the extended result in each agent is kept small. For a studied automotive vehicle, the second method gives, compared to a centralized method, a mean reduction in the sizes of the results and the maximum processor load on any unit with 85 and 90% respectively. To perform fault diagnosis, diagnostic tests are commonly used. If the additional evaluation of tests can not improve the fault isolation of a component then the component is ready. Since the evaluation of a test comes with a cost in for example computational resources, it is valuable to minimize the number of tests that have to be evaluated before readiness is achieved for all components. A strategy is presented that decides in which order to evaluate tests such that readiness is achieved with as few evaluations of tests as possible. Besides knowing how fault diagnosis is performed, it is also interesting to assess the effect that fault diagnosis has on for example safety. Since fault tree analysis often is used to evaluate safety, this thesis contributes with a systematic method that includes the effect of fault diagnosis in fault trees. The safety enhancement due to the use of fault diagnosis can thereby be analyzed and quantified
3 editions published in 2007 in English and held by 3 WorldCat member libraries worldwide
To improve safety, reliability, and efficiency of automotive vehicles and other technical applications, embedded systems commonly use fault diagnosis consisting of fault detection and isolation. Since many systems are constructed as distributed embedded systems including multiple control units, it is necessary to perform global fault isolation using for example a central unit. However, the drawbacks with such a centralized method are the need of a powerful diagnostic unit and the sensitivity against disconnections of this unit. Two alternative methods to centralized fault isolation are presented in this thesis. The first method performs global fault isolation by a istributed sequential computation. For a set of studied systems, themethod gives, compared to a centralizedmethod, amean reduction inmaximumprocessor load on any unitwith 40 and 70%for systems consisting of four and eight units respectively. The second method instead extends the result of the local fault isolation performed in each unit such that the results are globally correct. By only considering the components affecting each specific unit, the extended result in each agent is kept small. For a studied automotive vehicle, the second method gives, compared to a centralized method, a mean reduction in the sizes of the results and the maximum processor load on any unit with 85 and 90% respectively. To perform fault diagnosis, diagnostic tests are commonly used. If the additional evaluation of tests can not improve the fault isolation of a component then the component is ready. Since the evaluation of a test comes with a cost in for example computational resources, it is valuable to minimize the number of tests that have to be evaluated before readiness is achieved for all components. A strategy is presented that decides in which order to evaluate tests such that readiness is achieved with as few evaluations of tests as possible. Besides knowing how fault diagnosis is performed, it is also interesting to assess the effect that fault diagnosis has on for example safety. Since fault tree analysis often is used to evaluate safety, this thesis contributes with a systematic method that includes the effect of fault diagnosis in fault trees. The safety enhancement due to the use of fault diagnosis can thereby be analyzed and quantified
Robust control of a flexible manipulator arm : a benchmark problem by
Stig Moberg(
Book
)
3 editions published in 2006 in English and held by 3 WorldCat member libraries worldwide
A control engineering benchmark problem with industrial relevance is presented. The process is a simulation model of a nonlinear fourmass system, which should be controlled by a discretetime controller that optimizes performance for given robustness requirements. The control problem concerns only the socalled regulator problem
3 editions published in 2006 in English and held by 3 WorldCat member libraries worldwide
A control engineering benchmark problem with industrial relevance is presented. The process is a simulation model of a nonlinear fourmass system, which should be controlled by a discretetime controller that optimizes performance for given robustness requirements. The control problem concerns only the socalled regulator problem
Tracking and threat assessment for automotive collision avoidance by
Andreas Eidehall(
Book
)
2 editions published in 2007 in English and held by 3 WorldCat member libraries worldwide
This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments
2 editions published in 2007 in English and held by 3 WorldCat member libraries worldwide
This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give maximum safety based on information about real life traffic accidents. The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others. Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure. All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments
Modeling for control of centrifugal compressors by
Oskar Leufvén(
Book
)
2 editions published in 2013 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 2013 in English and held by 2 WorldCat member libraries worldwide
Techniques for Efficient Implementation of FIR and Particle Filtering by
Syed Asad Alam(
Book
)
2 editions published in 2016 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 2016 in English and held by 2 WorldCat member libraries worldwide
Navigation and SAR autofocusing in a sensor fusion framwork by
Zoran Sjanic(
Book
)
2 editions published in 2011 in English and held by 2 WorldCat member libraries worldwide
Since its discovery, in the 1940's, radar (Radio Detection and Ranging) has become an important ranging sensor in many areas of technology and science. Most of the military and many civilian applications are unimaginable today without radar. With technology development, radar application areas have become larger and more available. One of these applications is Synthetic Aperture Radar (SAR), where an airborne radar is used to create high resolution images of the imaged scene. Although known since the 1950's, the SAR methods have been continuously developed and improved and new algorithms enabling realtime applications have emerged lately. Together with making the hardware components smaller and lighter, SAR has become an interesting sensor to be mounted on smaller unmanned aerial vehicles (UAV's). One important thing needed in the SAR algorithms is the estimate of the platform's motion, like position and velocity. Since this estimate is always corrupted with errors, particularly if lower grade navigation system, common in UAV applications, is used, the SAR images will be distorted. One of the most frequently appearing distortions caused by the unknown platform's motion is the image defocus. The process of correcting the image focus is called autofocusing in SAR terminology. Traditionally, this problem was solved by methods that discard the platform's motion information, mostly due to the offline processing approach, i.e. the images were created after the flight. Since the image (de)focus and the motion of the platform are related to each other, it is possible to utilise the information from the SAR images as a sensor and improve the estimate of the platform's motion. The autofocusing problem can be cast as a sensor fusion problem. Sensor fusion is the process of fusing information from different sensors, in order to obtain best possible estimate of the states. Here, the information from sensors measuring platform's motion, mainly accelerometers, will be fused together with the information from the SAR images to estimate the motion of the flying platform. Two different methods based on this approach are tested on the simulated SAR data and the results are evaluated. One method is based on an optimisation based formulation of the sensor fusion problem, leading to batch processing, while the other method is based on the sequential processing of the radar data, leading to a filtering approach. The obtained results are promising for both methods and the obtained performance is comparable with the performance of a high precision navigation aid, such as Global Positioning System (GPS)
2 editions published in 2011 in English and held by 2 WorldCat member libraries worldwide
Since its discovery, in the 1940's, radar (Radio Detection and Ranging) has become an important ranging sensor in many areas of technology and science. Most of the military and many civilian applications are unimaginable today without radar. With technology development, radar application areas have become larger and more available. One of these applications is Synthetic Aperture Radar (SAR), where an airborne radar is used to create high resolution images of the imaged scene. Although known since the 1950's, the SAR methods have been continuously developed and improved and new algorithms enabling realtime applications have emerged lately. Together with making the hardware components smaller and lighter, SAR has become an interesting sensor to be mounted on smaller unmanned aerial vehicles (UAV's). One important thing needed in the SAR algorithms is the estimate of the platform's motion, like position and velocity. Since this estimate is always corrupted with errors, particularly if lower grade navigation system, common in UAV applications, is used, the SAR images will be distorted. One of the most frequently appearing distortions caused by the unknown platform's motion is the image defocus. The process of correcting the image focus is called autofocusing in SAR terminology. Traditionally, this problem was solved by methods that discard the platform's motion information, mostly due to the offline processing approach, i.e. the images were created after the flight. Since the image (de)focus and the motion of the platform are related to each other, it is possible to utilise the information from the SAR images as a sensor and improve the estimate of the platform's motion. The autofocusing problem can be cast as a sensor fusion problem. Sensor fusion is the process of fusing information from different sensors, in order to obtain best possible estimate of the states. Here, the information from sensors measuring platform's motion, mainly accelerometers, will be fused together with the information from the SAR images to estimate the motion of the flying platform. Two different methods based on this approach are tested on the simulated SAR data and the results are evaluated. One method is based on an optimisation based formulation of the sensor fusion problem, leading to batch processing, while the other method is based on the sequential processing of the radar data, leading to a filtering approach. The obtained results are promising for both methods and the obtained performance is comparable with the performance of a high precision navigation aid, such as Global Positioning System (GPS)
LowPower DeltaSigma Modulators for Medical Applications by
Ali Fazli Yeknami(
Book
)
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
This thesis investigates the design of highresolution and powerefficient [Delta][Sigma] modulators at very low frequencies. In total, eight discretetime (DT) modulators have been designed in a 65nm CMOS technology: two active modulators, two hybrid activepassive modulators, two ultralowvoltage modulators operated at 270mV and 0.5V supply voltages, one fully passive modulator, and a dualmode [Delta][Sigma] modulator using variablebandwidth amplifiers
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
This thesis investigates the design of highresolution and powerefficient [Delta][Sigma] modulators at very low frequencies. In total, eight discretetime (DT) modulators have been designed in a 65nm CMOS technology: two active modulators, two hybrid activepassive modulators, two ultralowvoltage modulators operated at 270mV and 0.5V supply voltages, one fully passive modulator, and a dualmode [Delta][Sigma] modulator using variablebandwidth amplifiers
Model Predictive Control in Flight Control Design : Stability and Reference Tracking by
Daniel Simon(
Book
)
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
Efficient estimation and detection methods for airborne applications by
PerJohan Nordlund(
Book
)
2 editions published between 2008 and 2009 in English and held by 2 WorldCat member libraries worldwide
The overall purpose with this thesis is to investigate and provide computationally efficient methods for estimation and detection. The focus is on airborne applications, and we seek estimation and detection methods which are accurate and reliable yet effective with respect to computational load. In particular, the methods shall be optimized for terrainaided navigation andcollision avoidance respectively. The estimation part focuses on particle filtering and the in general much more efficient marginalized particle filter. The detection part focuses on finding efficient methods for evaluating the probability of extreme values. This is achieved by considering the, in general, much easier task to compute the probability of levelcrossings. The concept of aircraft navigation using terrain height information is attractive because of the independence of external information sources. Typicallyterrainaided navigation consists of an inertial navigation unit supported by position estimates from a terrainaided positioning (TAP) system. TAP integrated with an inertial navigation system is challenging due to its highly nonlinear nature. Today, the particle filter is an accepted method for estimation of more or less nonlinear systems. At least when the requirements on computational load are not rigorous. In many online processing applications the requirements are such that they prevent the use of theparticle filter. We need more efficient estimation methods to overcome this issue, and the marginalized particle filter constitutes a possible solution. The basic principle for the marginalized particle filter is to utilize linear and discrete substructures within the overall nonlinear system. These substructures are used for efficient estimation by applying optimal filters such as the Kalman filter. The computationally demanding particle filter can then be concentrated on a smaller part of the estimation problem. The concept of an aircraft collision avoidance system is to assist or ultimately replace the pilot in order to to minimize the resulting collision risk. Detection is needed in aircraft collision avoidance because of the stochastic nature of thesensor readings, here we use information from video cameras. Conflict is declared if the minimum distance between two aircraft is less than a level. The level is given by the radius of a safety sphere surrounding the aircraft.We use the fact that the probability of conflict, for the process studied here, is identical to the probability for a downcrossing of the surface of the sphere. In general, it is easier to compute the probability of downcrossings compared to extremes. The Monte Carlo method provides a way forward to compute the probability of conflict. However, to provide a computationally tractable solution we approximate the crossing of the safety sphere with the crossing of a circular disc. The approximate method yields a result which is as accurate as the Monte Carlo method but the computational load is decreased significantly
2 editions published between 2008 and 2009 in English and held by 2 WorldCat member libraries worldwide
The overall purpose with this thesis is to investigate and provide computationally efficient methods for estimation and detection. The focus is on airborne applications, and we seek estimation and detection methods which are accurate and reliable yet effective with respect to computational load. In particular, the methods shall be optimized for terrainaided navigation andcollision avoidance respectively. The estimation part focuses on particle filtering and the in general much more efficient marginalized particle filter. The detection part focuses on finding efficient methods for evaluating the probability of extreme values. This is achieved by considering the, in general, much easier task to compute the probability of levelcrossings. The concept of aircraft navigation using terrain height information is attractive because of the independence of external information sources. Typicallyterrainaided navigation consists of an inertial navigation unit supported by position estimates from a terrainaided positioning (TAP) system. TAP integrated with an inertial navigation system is challenging due to its highly nonlinear nature. Today, the particle filter is an accepted method for estimation of more or less nonlinear systems. At least when the requirements on computational load are not rigorous. In many online processing applications the requirements are such that they prevent the use of theparticle filter. We need more efficient estimation methods to overcome this issue, and the marginalized particle filter constitutes a possible solution. The basic principle for the marginalized particle filter is to utilize linear and discrete substructures within the overall nonlinear system. These substructures are used for efficient estimation by applying optimal filters such as the Kalman filter. The computationally demanding particle filter can then be concentrated on a smaller part of the estimation problem. The concept of an aircraft collision avoidance system is to assist or ultimately replace the pilot in order to to minimize the resulting collision risk. Detection is needed in aircraft collision avoidance because of the stochastic nature of thesensor readings, here we use information from video cameras. Conflict is declared if the minimum distance between two aircraft is less than a level. The level is given by the radius of a safety sphere surrounding the aircraft.We use the fact that the probability of conflict, for the process studied here, is identical to the probability for a downcrossing of the surface of the sphere. In general, it is easier to compute the probability of downcrossings compared to extremes. The Monte Carlo method provides a way forward to compute the probability of conflict. However, to provide a computationally tractable solution we approximate the crossing of the safety sphere with the crossing of a circular disc. The approximate method yields a result which is as accurate as the Monte Carlo method but the computational load is decreased significantly
Sequential Monte Carlo for inference in nonlinear state space models by
Johan Dahlin(
Book
)
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 2014 in English and held by 2 WorldCat member libraries worldwide
Derivation of kinematic relations for a robot using Maple by Johanna Wallén(
Book
)
2 editions published in 2006 in English and held by 2 WorldCat member libraries worldwide
A first step towards making a toolbox in Maple for industrial robot modelling is taken. Position and orientation of the tool can be determined in terms of the DenavitHartenberg joint variables and also the Jacobian relating the linear and angular velocities to the joint velocities. Further on it will be possible to, eg, differentiate the Jacobian. Future work includes to evaluate different kinds of sensors and sensor locations and symbolically generate the kinematic models using Maple. It also means to incorporate the models in Matlab or Ccode for including the results, eg, in an Extended Kalman Filter algorithm for state estimation
2 editions published in 2006 in English and held by 2 WorldCat member libraries worldwide
A first step towards making a toolbox in Maple for industrial robot modelling is taken. Position and orientation of the tool can be determined in terms of the DenavitHartenberg joint variables and also the Jacobian relating the linear and angular velocities to the joint velocities. Further on it will be possible to, eg, differentiate the Jacobian. Future work includes to evaluate different kinds of sensors and sensor locations and symbolically generate the kinematic models using Maple. It also means to incorporate the models in Matlab or Ccode for including the results, eg, in an Extended Kalman Filter algorithm for state estimation
LowVoltage AnalogtoDigital Converters and MixedSignal Interfaces by
Prakash Harikuma(
Book
)
2 editions published in 2015 in English and held by 2 WorldCat member libraries worldwide
2 editions published in 2015 in English and held by 2 WorldCat member libraries worldwide
NoGAP: Novel Generator of Accelerators and Processors by
Per Axel Karlström(
Book
)
2 editions published in 2010 in English and held by 2 WorldCat member libraries worldwide
ASIPs are needed to handle the future demand of flexible yet highperformance embedded computing. The flexibility of ASIPs makes them preferable over fixed function ASICs. Also, a well designed ASIP, has a power consumption comparable to ASICs. However the cost associated with ASIP design is a limiting factor for a more wide spread adoption. A number of different tools have been proposed, promising to ease this design process. However all of the current state of the art tools limits the designer due to a template based design process. It blocks design freedoms and limits the I/O bandwidth of the template. We have therefore proposed the Novel Generator of Accelerator and Processors (NoGAP). NoGAP is a design automation tool for ASIP andaccelerator design that puts very few limits on what can be designed, yet NoGAP gives support by automating much of the tedious anderror prone tasks associated with ASIP design. This thesis will present NoGAP and much of its key concepts. Such as; the NoGAPCL) which is a language used to implement processors in NoGAP, This thesis exposes NoGAP's key technologies, which include automatic bus and wire sizing, instruction decoder and pipeline management, how PCFSMs can be generated, how an assembler can be generated, and how cycle accurate simulators can be generated. We have so far proven NoGAP's strengths in three extensive case studies, in one a floating point pipelined data path was designed, in another a simple RISC processor was designed, and finally one advanced RISC style DSP was designed using NoGAP. All these case studies points to the same conclusion, that NoGAP speeds up development time, clarify complex pipeline architectures, retains design flexibility, and most importantly does not incur much performance penalty, compared to hand optimized RTL code. We belive that the work presented in this thesis shows that NoGAP, using our proposed novel approach to micro architecture design, can have a significant impact on both academic and industrial hardware design. To our best knowledge NoGAP is the first system that has demonstrated that a template free processor construction framework can be developed and generate high performance hardware solutions
2 editions published in 2010 in English and held by 2 WorldCat member libraries worldwide
ASIPs are needed to handle the future demand of flexible yet highperformance embedded computing. The flexibility of ASIPs makes them preferable over fixed function ASICs. Also, a well designed ASIP, has a power consumption comparable to ASICs. However the cost associated with ASIP design is a limiting factor for a more wide spread adoption. A number of different tools have been proposed, promising to ease this design process. However all of the current state of the art tools limits the designer due to a template based design process. It blocks design freedoms and limits the I/O bandwidth of the template. We have therefore proposed the Novel Generator of Accelerator and Processors (NoGAP). NoGAP is a design automation tool for ASIP andaccelerator design that puts very few limits on what can be designed, yet NoGAP gives support by automating much of the tedious anderror prone tasks associated with ASIP design. This thesis will present NoGAP and much of its key concepts. Such as; the NoGAPCL) which is a language used to implement processors in NoGAP, This thesis exposes NoGAP's key technologies, which include automatic bus and wire sizing, instruction decoder and pipeline management, how PCFSMs can be generated, how an assembler can be generated, and how cycle accurate simulators can be generated. We have so far proven NoGAP's strengths in three extensive case studies, in one a floating point pipelined data path was designed, in another a simple RISC processor was designed, and finally one advanced RISC style DSP was designed using NoGAP. All these case studies points to the same conclusion, that NoGAP speeds up development time, clarify complex pipeline architectures, retains design flexibility, and most importantly does not incur much performance penalty, compared to hand optimized RTL code. We belive that the work presented in this thesis shows that NoGAP, using our proposed novel approach to micro architecture design, can have a significant impact on both academic and industrial hardware design. To our best knowledge NoGAP is the first system that has demonstrated that a template free processor construction framework can be developed and generate high performance hardware solutions
Using DAE Solvers to examine local identifiability for linear and nonlinear systems by
Markus Gerdin(
Book
)
2 editions published in 2005 in English and held by 2 WorldCat member libraries worldwide
If a model structure is not identifiable, then it is not possible to uniquely identify its parameters from measured data. This contribution describes how solvers for differentialalgebraic equations (DAE) can be used to examine if a model structure is locally identifiable. The procedure can be applied to both linear and nonlinear systems. If a model structure is not identifiable, it is also possible to examine which functions of the parameters that are locally identifiable
2 editions published in 2005 in English and held by 2 WorldCat member libraries worldwide
If a model structure is not identifiable, then it is not possible to uniquely identify its parameters from measured data. This contribution describes how solvers for differentialalgebraic equations (DAE) can be used to examine if a model structure is locally identifiable. The procedure can be applied to both linear and nonlinear systems. If a model structure is not identifiable, it is also possible to examine which functions of the parameters that are locally identifiable
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Related Identities
 Linköpings universitet Tekniska högskolan Publisher
 Larsson, Erik G. Thesis advisor Author
 Felsberg, Michael 1974 Thesis advisor Author
 Gustafsson, Fredrik Author
 Gustafsson, Fredrik 1964 Thesis advisor Author
 Ljung, Lennart 1946 Author
 Björnson, Emil Thesis advisor Author
 Hendeby, Gustaf 1978 Thesis advisor Author
 Schön, Thomas 1977 Author
 Norrlöf, Mikael 1971 Thesis advisor Author
Alternative Names
ISY
Linköping universitet Institutionen för systemteknik
Linköping University Department of Electrical Engineering
LiU ISY
Universitetet i Linköping Institutionen för systemteknik
University of Linköping. Department of Electrical Engineering.
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