| 1. |
- Hidayat, Egi, 1981-
(författare)
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On Identification of Biological Systems
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- System identification finds nowadays application in various areas of biological research as a tool of empiric mathematical modeling and model individualization. A fundamental challenge of system identification in biology awaits in the form of response variability. Furthermore, biological systems tend to exhibit high degree of nonlinearity as well as significant time delays. This thesis covers system identification approaches developed for the applications within two particular biomedical fields: neuroscience and endocrinology.The first topic of the thesis is parameter estimation of the classical Elementary Motion Detector (EMD) model in insect vision. There are two important aspects to be taken care of in the identification approach, namely the nonlinear dynamics of the individual EMD and the spatially distributed structure of multiple detectors producing a measurable neural response. Hence, the suggested identification method is comprised of two consecutive stages addressing each of the above aspects. Furthermore, visual stimulus design for high spatial excitation order has been investigated.The second topic is parameter estimation of mathematical model for testosterone regulation in the human male. The main challenges of this application are in the unavailability of input signal measurements and the presence of an unknown pulsatile feedback in the system resulting in a highly nonlinear closed-loop dynamics. Semi-blind identification method has been developed based on a recently proposed pulse-modulated model of pulsatile endocrine regulation.The two system identification problems treated in the thesis bear some resemblance in the sense that both involve measured signals that can be seen as square-integrable functions of time. This property is handled by transforming the signals into the Laguerre domain, i.e. by equivalently representing the functions with their infinite Laguerre series.
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| 2. |
- Jansson, Daniel, 1986-
(författare)
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Identification Techniques for Mathematical Modeling of the Human Smooth Pursuit System
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis proposes nonlinear system identification techniques for the mathematical modeling of the human smooth pursuit system (SPS) with application to motor symptom quantification in Parkinson's disease (PD). The SPS refers to the complex neuromuscular system in humans that governs the smooth pursuit eye movements (SPEM). Insight into the SPS and its operation is of importance in a wide and steadily expanding array of application areas and research fields. The ultimate purpose of the work in this thesis is to attain a deeper understanding and quantification of the SPS dynamics and thus facilitate the continued development of novel commercial products and medical devices. The main contribution of this thesis is in the derivation and evaluation of several techniques for SPS characterization. While attempts to mathematically model the SPS have been made in the literature before, several key aspects of the problem have been previously overlooked.This work is the first one to devise dynamical models intended for extended-time experiments and also to consider systematic visual stimuli design in the context of SPS modeling. The result is a handful of parametric mathematical models outperforming current State-of-the-Art models in terms of prediction accuracy for rich input signals. As a complement to the parametric dynamical models, a non-parametric technique involving the construction of individual statistical models pertaining to specific gaze trajectories is suggested. Both the parametric and non-parametric models are demonstrated to successfully distinguish between individuals or groups of individuals based on eye movements.Furthermore, a novel approach to Wiener system identification using Volterra series is proposed and analyzed. It is exploited to confirm that the SPS in healthy individuals is indeed nonlinear, but that the nonlinearity of the system is significantly stronger in PD subjects. The nonlinearity in healthy individuals appears to be well-modeled by a static output function, whereas the nonlinear behavior introduced to the SPS by PD is dynamical.
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| 3. |
- Silva, Margarida M., 1984-
(författare)
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Nonlinear Modeling and Feedback Control of Drug Delivery in Anesthesia
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- General anesthesia is a drug-induced reversible state where neuromuscular blockade (NMB), hypnosis, and analgesia (jointly denoted by depth of anesthesia - DoA) are guaranteed. This thesis concerns mathematical modeling and feedback control of the effect of the muscle relaxants atracurium and rocuronium, the hypnotic propofol, and the analgesic remifentanil. It is motivated by the need to reduce incidences of awareness and overdose-related post-operative complications that occur in standard clinical practice. A major challenge for identification in closed-loop is the poor excitation provided by the feedback signal. This applies to the case of drugs administered in closed-loop. As a result, the standard models for the effect of anesthetics appear to be over-parameterized. This deteriorates the result of system identification and prevents individualized control.In the first part of the thesis, minimally parameterized models for the single-input single-output NMB and the multiple-input single-output DoA are developed, using real data. The models have a nonlinear Wiener structure: linear time-invariant dynamics cascaded with a static nonlinearity. The proposed models are shown to improve identifiability as compared to the standard ones.The second part of the thesis presents system identification methods for Wiener systems: a batch prediction error method, and two recursive techniques, one based on the extended Kalman filter, and another based on the particle filter. Algorithms are given for both the NMB and the DoA using the minimally parameterized models.Nonlinear adaptive controllers are proposed in the third part of the thesis. Using the model parameter estimates from the extended Kalman filter, the controller performs an online inversion of the Wiener nonlinearity. A pole-placement controller or a linear quadratic Gaussian controller is used for the linearized system. Results show good reference tracking performance both in simulation and in real trials.Relating to patient safety, the existence of undesirable sustained oscillations as consequence of Andronov-Hopf bifurcations for a NMB PID-controlled system is analyzed. Essentially the same bifurcations are observed in the standard and the minimally parameterized models, confirming the ability of the latter to predict the nonlinear behavior of the closed-loop system. Methods to design oscillation-free controllers are outlined.
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| 4. |
- Evestedt, Magnus, 1978-
(författare)
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Parameter and State Estimation with Information-rich Signals
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The complexity of industrial systems and the mathematical models to describe them increases. In many cases, point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. These are examples of information-rich signals for which suitable applications range in a broad spectrum from micro-electromechanical systems and bio-medical engineering to paper making and steel production. Recursive parameter estimation algorithms are employed to identify parameters in a mathematical model from measurements of input and output signals. For accurate parameter estimation, the input signal must be persistently exciting, i.e. such that important features of the modelled system are reflected in the output over a sufficient period of time. The Stenlund-Gustafsson (SG) algorithm, a Kalman filter based method for recursive parameter estimation in linear regression models, that does not diverge under lack of excitation, is studied. The stationary properties of the algorithm and the corresponding Riccati equation are formulated in terms of the excitation space spanned by the regressor vectors. Furthermore, it is shown that the Riccati equation of the studied algorithm can be solved element-wise. Convergence estimates for the elements of the solution to the Riccati equation are provided, directly relating convergence rate to the signal-to-noise ratio in the regression model. An element-wise form of the parameter update equation is also given, where the connection to specific elements of the solution to the Riccati equation is apparent. The SG-algorithm is employed for two applications with audio signals. One is in an acoustic echo cancellation setting where its performance is shown to match that of other well-known estimation techniques, such as the normalized least mean squares and the Kalman filter. When the input is not sufficiently exciting, the studied method performs best of all considered schemes. The other application is the Linz-Donawitz (LD) steel converter. The converter consists of a vessel with molten metal and foam is produced to facilitate chemical reactions. A common problem, usually referred to as slopping, arises when the foam rises above the limits of the vessel and overflows. A warning system is designed, based on the SG-algorithm and change detection methods, to give alarms before slopping occurs. A black-box model relates different sensor values of which one is the microphone signal picked up in the area above the converter. The system detected slopping correctly in 80% of the blows in field studies at SSAB Oxelösund. A practical example of a vision-based system is provided by cavity form estimation in a water model of the steel bath. The water model is captured from the side by a video camera. The images together with a non-linear model are used to estimate important process parameters, related to the heat and mass transport in the LD-converter.
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| 5. |
- Jakobsson, Erik, 1987-
(författare)
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Data-driven Condition Monitoring in Mining Vehicles
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Situation awareness is a crucial capability of any autonomous system, including mining vehicles such as drill rigs and mine trucks. Typically situation awareness is interpreted as the capability of an autonomous system to interpret its surroundings and the intentions of other agents. The internal system awareness however, is often not receiving the same focus, even though the success of any given mission is completely dependent of the condition of the agents themselves. The internal system awareness in the form of vehicle health is the focus of this thesis.As the mining industry becomes increasingly automated, and vehicles become increasingly advanced, the need for condition monitoring and prognostics will continue to rise. This thesis explores data-driven methods that estimate the health of mining vehicles to accommodate those needs. We do so by utilizing available sensor signals, common on a large amount of mining vehicles, to make assessments of the current vehicle condition and tasks. The mining industry is characterized by small series of highly specialized vehicles, which affects the possibility to use more traditional prognostic solutions.The resulting health information can be used both to aid in tasks such as maintenance planning, but also as an important input to decision making for the planning system, i.e. how to run the vehicle for minimum wear and damage, while maintaining other mission objectives.The contributions include: a) A method to use operational data to estimate damage on the frame of a mine truck. This is done using system identification to find a model describing stresses in the structure with input from other sensors such as accelerometers, load sensors and pressure sensors. The estimated stress time signal is in turn used to calculate accumulated damage, and is shown to reveal interesting conclusions on driver behavior. b) A method to characterize the different driving tasks by using an accelerometer and a convolutional neural network. We show that the model is capable of classifying the vehicle task correctly in 96 % of the cases. And finally c), a system for underground road monitoring, where a quarter car model and a Kalman filter are used to generate an estimate of the road profile, while positioning the vehicle using inertial measurements and access point signal strength.
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| 6. |
- Martins da Silva, Margarida
(författare)
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System identification and control for general anesthesia based on parsimonious Wiener models
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The effect of anesthetics in the human body is usually described by Wiener models. The high number of patient-dependent parameters in the standard models, the poor excitatory pattern of the input signals (administered anesthetics) and the small amount of available input-output data make application of system identification strategies difficult.The idea behind this thesis is that, by reducing the number of parameters to describe the system, improved results may be achieved when system identification algorithms and control strategies based on those models are designed. The choice of the appropriate number of parameters matches the parsimony principle of system identification.The three first papers in this thesis present Wiener models with a reduced number of parameters for the neuromuscular blockade and the depth of anesthesia. Batch and recursive system identification algorithms are presented. Taking advantage of the small number of continuous time model parameters, adaptive controllers are proposed in the two last papers. The controller structure combines an inversion of the static nonlinearity of the Wiener model with a linear controller for the exactly linearized system, using the parameter estimates obtained recursively by an extended Kalman filter. The performance of the adaptive nonlinear controllers is tested in a database of realistic patients with good results.
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| 7. |
- Mattsson, Per, 1984-
(författare)
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Modeling and identification of nonlinear and impulsive systems
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Mathematical modeling of dynamical systems plays a central roll in science and engineering. This thesis is concerned with the process of finding a mathematical model, and it is divided into two parts - one that concentrates on nonlinear system identification and another one where an impulsive model of testosterone regulation is constructed and analyzed.In the first part of the thesis, a new latent variable framework for identification of a large class of nonlinear models is developed. In this framework, we begin by modeling the errors of a nominal predictor using a flexible stochastic model. The error statistics and the nominal predictor are then identified using the maximum likelihood principle. The resulting optimization problem is tackled using a majorization-minimization approach, resulting in a tuning parameter-free recursive identification method. The proposed method learns parsimonious predictive models. Many popular model structures can be expressed within the framework, and in the thesis it is applied to piecewise ARX models.In the first part, we also derive a recursive prediction error method based on the Hammerstein model structure. The convergence properties of the method are analyzed by application of the associated differential equation method, and conditions ensuring convergence are given.In the second part of the thesis, a previously proposed pulse-modulated feedback model of testosterone regulation is extended with infinite-dimensional dynamics, in order to better explain testosterone profiles observed in clinical data. It is then shown how the analysis of oscillating solutions for the finite-dimensional case can be extended to the infinte-dimensional case. A method for blind state estimation in impulsive systems is introduced, with the purpose estimating hormone concentrations that cannot be measured in a non-invasive way. The unknown parameters in the model are identified from clinical data and, finally, a method of incorporating exogenous signals portraying e.g. medical interventions is studied.
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| 8. |
- Olsson, Claes, 1973-
(författare)
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Active Vibration Control of Multibody Systems : Application to Automotive Design
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Active vibration control to reduce vibrations and structure borne noise is considered using a powerful multi-disciplinary virtual design environment which enables control system design to be considered as an integral part of the overall vehicle design. The main application studied is active automotive engine vibration isolation where, first, the potential of large frequency band multi-input multi-output H2 feedback control is considered. Facilitated by the virtual environment, it is found necessary to take non-linear characteristics into account to achieve closed-loop stability. A physical explanation to why receiver structure flexibility insignificantly affect the open and closed-loop characteristics in case of total force feedback in contrast to acceleration feedback is then given. In this context, the inherent differences between model order reduction by modal and by balanced truncation are being stressed. Next, applying state-of-the-art algorithms for recursive parameter estimation, time-domain adaptive filtering is shown to lack sufficient tracking performance to deal with multiple spectral components of transient engine excitations corresponding to rapid car accelerations. Finally, plant non-linearity as well as transient excitation are successfully handled using narrow band control based on feedback of disturbance states estimates. To deal with the non-linear characteristics, an approach to generate linear parameter varying descriptions of non-linear systems is proposed. Parameter dependent quadratic stability is assessed using a derived affine closed-loop system representation. This thesis also considers actuator saturation induced limit cycles for observer-based state feedback control systems encountered when dealing with the active isolation application. It is stressed that the fundamental observer-based anti-windup technique could imply severely deteriorated closed-loop characteristics and even sustained oscillations. That is in the case when the observer is fed by the saturated control signal in contrast to the computed one. Based on piecewise affine system descriptions, analytical tools to conclude about limit cycles and exponential closed-loop stability are provided for the two observer implementations.
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| 9. |
- Rosén, Olov, 1985-
(författare)
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Parallel Stochastic Estimation on Multicore Platforms
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The main part of this thesis concerns parallelization of recursive Bayesian estimation methods, both linear and nonlinear such. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in signal processing, system identification, and automatic control. Solving the recursive Bayesian estimation problem is known to be computationally expensive, which often makes the methods infeasible in real-time applications and problems of large dimension. As the computational power of the hardware is today increased by adding more processors on a single chip rather than increasing the clock frequency and shrinking the logic circuits, parallelization is one of the most powerful ways of improving the execution time of an algorithm. It has been found in the work of this thesis that several of the optimal filtering methods are suitable for parallel implementation, in certain ranges of problem sizes. For many of the suggested parallelizations, a linear speedup in the number of cores has been achieved providing up to 8 times speedup on a double quad-core computer. As the evolution of the parallel computer architectures is unfolding rapidly, many more processors on the same chip will soon become available. The developed methods do not, of course, scale infinitely, but definitely can exploit and harness some of the computational power of the next generation of parallel platforms, allowing for optimal state estimation in real-time applications.
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| 10. |
- Yamalova, Diana, 1991-
(författare)
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Hybrid observers for systems with intrinsic pulse-modulated feedback
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Dynamical processes resulting from the interaction of continuous and discrete dynamics are often encountered in living organisms. Time evolutions of such processes constitute continuous variables that are subject to instant changes at discrete points of time. Usually, these discrete events cannot be observed directly and have to be reconstructed from the accessible for measurement continuous variables.Thus, the problem of hybrid state estimation from measurements of continuous outputs is important to and naturally arises in life sciences but, so far, scarcely covered in the existing literature.This thesis deals with a special class of hybrid systems, where the continuous linear part is controlled by an intrinsic impulsive feedback that contributes discrete dynamics. The impacting pulsatile feedback signal is not available for measurement and, therefore, has to be reconstructed. To estimate all the elements of the hybrid state vector, an observation problem is considered.The focus of the work is on a state observation problem for an analytically tractable example of a hybrid oscillator with rich nonlinear dynamics including, e.g., monostable and bistable high-periodic and quasiperiodic solutions as well as deterministic chaos. At the same time, the three-dimensional case of the considered hybrid oscillator constitutes a mathematical model of testosterone regulation in the male validated through system identification on human endocrine data. In a pulsatile endocrine regulation loop, one of the hormones (releasing hormone) is secreted in pulses from neurons in the hypothalamus of the brain. Thus a direct measurement of the concentration of this hormone in the human is not possible for ethical reasons and it has to be estimated in some manner from the available data, for instance by applying an observer.It is desirable for an observer to guarantee asymptotic convergence of the state estimate to that of the observable plant from all feasible initial conditions at a highest possible rate. When the state estimation error is zero, the hybrid observer is in a synchronous mode characterized by the firings of the impulses in the observer feedback and those of the plant occurring simultaneously.Therefore, the observer design problem can be formulated as synchronization of the plant states with those of the observer. This approach does not formally demand observability of the hybrid plant solution. Further, since the dynamics of the oscillator are highly nonlinear, the state estimation problem is considered with respect to particular solutions of the observed system, whose characteristics are assumed to be known, but not the initial conditions. The observer design problem for the impulsive Goodwin's oscillator consists of the selection of the observer structure and of assigning desired properties to a discrete map that captures the observer state transitions from one impulse firing to another through manipulating the degrees of freedom of the observer.
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