The thesis treats model reduction for linear time-varying systems. Time-varying models appear in many fields, including power systems, chemical engineering, aeronautics, and computational science. They can also be used for approximation of time-invariant nonlinear models. Model reduction is a topic that deals with simplification of complex models. This is important since it facilitates analysis and synthesis of controllers. The thesis consists of two parts. The first part provides an introduction to the topics of time-varying systems and model reduction. Here, notation, standard results, examples, and some results from the second part of the thesis are presented. The second part of the thesis consists of four papers. In the first paper, we study the balanced truncation method for linear time-varying state-space models. We derive error bounds for the simplified models. These bounds are generalizations of well-known time-invariant results, derived with other methods. In the second paper, we apply balanced truncation to a high-order model of a diesel exhaust catalyst. Furthermore, we discuss practical issues of balanced truncation and approximative discretization. In the third paper, we look at frequency-domain analysis of linear time-periodic impulse-response models. By decomposing the models into Taylor and Fourier series, we can analyze convergence properties of different truncated representations. In the fourth paper, we use the frequency-domain representation developed in the third paper, the harmonic transfer function, to generalize Bode's sensitivity integral. This result quantifies limitations for feedback control of linear time-periodic systems. / QC 20120206

<p>This dissertation extends the theory of persistent homology to time varying systems. Most of the previous work has been dedicated to using this powerful tool in topological data analysis to study static point clouds. In particular, given a point cloud, we can construct its persistence diagram. Since the diagram varies continuously as the point cloud varies continuously, we study the space of time varying persistence diagrams, called vineyards when they were introduced by Cohen-Steiner, Edelsbrunner, and Morozov.</p><p>We will first show that with a good choice of metric, these vineyards are stable for small perturbations of their associated point clouds. We will also define a new mean for a set of persistence diagrams based on the work of Mileyko et al. which, unlike the previously defined mean, is continuous for geodesic vineyards. </p><p>Next, we study the sensor network problem posed by Ghrist and de Silva, and their application of persistent homology to understand when a set of sensors covers a given region. Giving each of these sensors a probability of failure over time, we show that an exact computation of the probability of failure of the whole system is NP-hard, but give an algorithm which can predict failure in the case of a monitored system.</p><p>Finally, we apply these methods to an automated system which can cluster agents moving in aerial images by their behaviors. We build a data structure for storing and querying the information in real-time, and define behavior vectors which quantify behaviors of interest. This clustering by behavior can be used to find groups of interest, for which we can also quantify behaviors in order to determine whether the group is working together to achieve a common goal, and we speculate that this work can be extended to improving tracking algorithms as well as behavioral predictors.</p> / Dissertation

Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. One realistic model assumption for such parameter variability is the Markovian model, and Kaiman filtering is then assumed to be a convenient estimator. In the thesis several aspects of using Kaiman filtering approaches to estimation in that framework are considered. The application of the Kaiman filter to estimation in econometric models is straightforward if a set of basic assumptions are satisfied, and if necessary initial specifications can be accurately made. Typically, however, these requirements can generally not be perfectly met. It is therefore of great importance to know the consequences of deviations from the basic assumptions and correct initial specifications for inference, in particular for the small sample situations typical in econometrics. If the consequences are severe it is essential to develop techniques to cope with such aspects.For estimation in interdependent systems a two stage Kaiman filter is proposed and evaluated, theoretically, as well as by a small sample Monte Carlo study, and empirically. The estimator is approximative, but with promising small sample properties. Only if the transition matrix of the parameter model and an initial parameter vector are misspecified, the performance deteriorates. Furthermore, the approach provides useful information about structural properties, and forms a basis for good short term forecasting.In a reduced form fraaework most of the basic assumptions of the traditional Kaiman filter are relaxed, and the implications are studied. The case of stochastic regressors is, under reasonable additional assumptions, shown to result in an estimator structurally similar to that due to the basic assumptions. The robustness properties are such that in particular the transition matrix and the initial parameter vector should be carefully estimated. An estimator for the joint estimation of the transition matrix, the parameter vector and the model residual variance is suggested and utilized to study the consequences of a misspecified parameter model. By estimating th transitions the parameter estimates are seen to be robust in this respect. / <p>Härtill 4 delar</p> / digitalisering@umu

In spite of increasingly sophisticated workforce management tools, a significant gap remains between the goal of effective staffing and the present difficulty predicting the stochastic demand of inbound calls. We have investigated the hypothesized nonhomogeneous Poisson process model of modem pool callers of the University community. In our case, we tested if the arrivals could be approximated by a piecewise constant rate over short intervals. For each of 1 and 10-minute intervals, based on the close relationship between the Poisson process and the exponential distribution, the test results did not show any sign of homogeneous Poisson process. We have examined the hypothesis of a nonhomogeneous Poisson process by a transformed statistic. Quantitative and graphical goodness-of-fit tests have confirmed nonhomogeneous Poisson process. <p>Further analysis on the intensity function revealed that linear rate intensity was woefully inadequate in predicting time varying arrivals. For sinusoidal rate model, difficulty arose in setting the period parameter. Spline models, as an alternative to parametric modelling, had more control of balance between data fitting and smoothness, which was appealing to our analysis on call arrival process.

Shape memory alloys (SMAs) are metallic materials, which have two main stable crystalline phases: austenite, a high temperature phase and martensite, a low temperature phase. Austenite and martensite each have unique physical and mechanical properties, and transformation between these phases enables two effects known as the shape memory effect (SME) and superelasticity. When a material that displays the SME is plastically deformed at low temperature, a heat input will cause the SMA to return to its original shape before the deformation. At higher temperatures, the material displays an effect called superelasticity, where strains of up to 10% are recoverable. These characteristics of SMA allow for significant amounts of strain recovery, and enable the design of SMA actuators. The temperature in an SMA actuator is generally controlled by resistive heating, also know as joule heating, and the strain recovery capabilities are used to do work on a load, thereby creating an electro-mechanical actuator. SMA actuators have attractive properties such as high energy density, smooth and silent actuation, reduced part counts compared to traditional alternatives, and scalability down to the micromechanical level. The phase transformation in SMA actuators, however, is highly non-linear. Therefore, the use of SMA as actuators, for example in positioning systems, benefits from the development of good models to predict and control the materials. The goals of this work are to develop a model suitable for real-time implementation, and that reproduces the observed behaviour of SMA actuators. The model is then inverted and used to develop a model-based controller, used in conjunction with traditional PID control to improve the precision and robustness of SMA actuators. The modelling portion of this work consists of the development of a phenomenological SMA model. The forward model is split into three blocks: a heating block, a phase kinetics block and a mechanical block. Since joule heating is commonly used in SMA actuators to bring about an increase in temperature, the heating block presents equations to convert a current input into the temperature of the wire. The phase kinetics block equations convert the calculated temperature and applied stress to the fraction of martensite present in the SMA. Finally, the mechanical model calculates the strain in the material from the martensite fraction and the applied stress. Once the model equations are presented, experimental verification tests are shown to compare physical SMA behaviour with that predicted by the model. Each of the blocks of the forward model are then inverted in order to be used as a feedforward linearizing controller. The control section of this thesis deals with the response of two common types of SMA actuators: a constant force SMA actuator and a spring-biased SMA actuator. The response of the system to step and sinusoidal signals with period of 5 seconds is investigated using two types of controllers: a traditional PI controller and the inverse-model controller in feedforward with a PI controller in feedback. Additionally, the robustness of the system is investigated through the response of the system to transient and sinusoidal stress disturbances. The disturbance rejection is investigated on a constant force actuator both with and without the presence of a force sensor.

The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.

Dynamiken för en starkt accelerande robot har modellerats. Modellen linjäriseras så att roboten beskrivs som ett linjärt tidsvariabelt system. Denna representation beskriver roboten väl då robotens anblåsningsvinkel, vilket är vinkeln mellan robotkroppen och robotens hastighet, är liten. Eftersom det ej är möjligt att mäta alla robotens tillstånd har en observatör tagits fram i form av ett Kalmanfilter. Problematik vid framtagandet av observatören diskuteras i rapporten. Den linjära tidsvariabla modellen har använts till att ta fram två regulatorer. En LQ-regulator och en H∞-regulator. Hur dessa tas fram och vilka problem som finns diskuteras i rapporten. För att kunna se fördelar och nackdelar beträffande prestanda och robusthet har en mängd tester gjort. Testerna visar på olika fördelar hos de olika reglersystemen. Till exempel är det lättare att få bra prestanda med LQ-regulatorn än H∞-regulatorn om systemet som styrs stämmer bra överens med systemet som använts vid reglerdesignen. H∞-regulatorn har bättre förmåga att anpassa sig till modellförändringar givet att observatören gör bra skattningar. Det är dock svårt att utnämna en generell vinnare.

Shape memory alloys (SMAs) are metallic materials, which have two main stable crystalline phases: austenite, a high temperature phase and martensite, a low temperature phase. Austenite and martensite each have unique physical and mechanical properties, and transformation between these phases enables two effects known as the shape memory effect (SME) and superelasticity. When a material that displays the SME is plastically deformed at low temperature, a heat input will cause the SMA to return to its original shape before the deformation. At higher temperatures, the material displays an effect called superelasticity, where strains of up to 10% are recoverable. These characteristics of SMA allow for significant amounts of strain recovery, and enable the design of SMA actuators. The temperature in an SMA actuator is generally controlled by resistive heating, also know as joule heating, and the strain recovery capabilities are used to do work on a load, thereby creating an electro-mechanical actuator. SMA actuators have attractive properties such as high energy density, smooth and silent actuation, reduced part counts compared to traditional alternatives, and scalability down to the micromechanical level. The phase transformation in SMA actuators, however, is highly non-linear. Therefore, the use of SMA as actuators, for example in positioning systems, benefits from the development of good models to predict and control the materials. The goals of this work are to develop a model suitable for real-time implementation, and that reproduces the observed behaviour of SMA actuators. The model is then inverted and used to develop a model-based controller, used in conjunction with traditional PID control to improve the precision and robustness of SMA actuators. The modelling portion of this work consists of the development of a phenomenological SMA model. The forward model is split into three blocks: a heating block, a phase kinetics block and a mechanical block. Since joule heating is commonly used in SMA actuators to bring about an increase in temperature, the heating block presents equations to convert a current input into the temperature of the wire. The phase kinetics block equations convert the calculated temperature and applied stress to the fraction of martensite present in the SMA. Finally, the mechanical model calculates the strain in the material from the martensite fraction and the applied stress. Once the model equations are presented, experimental verification tests are shown to compare physical SMA behaviour with that predicted by the model. Each of the blocks of the forward model are then inverted in order to be used as a feedforward linearizing controller. The control section of this thesis deals with the response of two common types of SMA actuators: a constant force SMA actuator and a spring-biased SMA actuator. The response of the system to step and sinusoidal signals with period of 5 seconds is investigated using two types of controllers: a traditional PI controller and the inverse-model controller in feedforward with a PI controller in feedback. Additionally, the robustness of the system is investigated through the response of the system to transient and sinusoidal stress disturbances. The disturbance rejection is investigated on a constant force actuator both with and without the presence of a force sensor.

The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.

Sampling theory studies the equivalence between continuous and discrete representations of information. This equivalence is ubiquitously used in communication engineering and signal processing. For example, it allows engineers to store continuous signals as discrete data on digital media. The classical sampling theorem, also known as the theorem of Whittaker-Shannon-Kotel'nikov, enables one to perfectly and stably reconstruct continuous signals with a constant bandwidth from their discrete samples at a constant Nyquist rate. The Nyquist rate depends on the bandwidth of the signals, namely, the frequency upper bound. Intuitively, a signal's `information density' and `effective bandwidth' should vary in time. Adjusting the sampling rate accordingly should improve the sampling efficiency and information storage. While this old idea has been pursued in numerous publications, fundamental problems have remained: How can a reliable concept of time-varying bandwidth been defined? How can samples taken at a time-varying Nyquist rate lead to perfect and stable reconstruction of the continuous signals? This thesis develops a new non-Fourier generalized sampling theory which takes samples only as often as necessary at a time-varying Nyquist rate and maintains the ability to perfectly reconstruct the signals. The resulting Nyquist rate is the critical sampling rate below which there is insufficient information to reconstruct the signal and above which there is redundancy in the stored samples. It is also optimal for the stability of reconstruction. To this end, following work by A. Kempf, the sampling points at a Nyquist rate are identified as the eigenvalues of self-adjoint extensions of a simple symmetric operator with deficiency indices (1,1). The thesis then develops and in a sense completes this theory. In particular, the thesis introduces and studies filtering, and yields key results on the stability and optimality of this new method. While these new results should greatly help in making time-variable sampling methods applicable in practice, the thesis also presents a range of new purely mathematical results. For example, the thesis presents new results that show how to explicitly calculate the eigenvalues of the complete set of self-adjoint extensions of such a symmetric operator in the Hilbert space. This result is of interest in the field of functional analysis where it advances von Neumann's theory of self-adjoint extensions.