<p>Control and diagnosis of complex systems demand accurate knowledge of certain quantities to be able to control the system efficiently and also to detect small errors. Physical sensors are expensive and some quantities are hard or even impossible to measure with physical sensors. This has made model-based estimation an attractive alternative.</p><p>Model-based estimators are sensitive to errors in the model and since the model complexity needs to be kept low, the accuracy of the models becomes limited. Further, modeling is hard and time consuming and it is desirable to design robust estimators based on existing models. An experimental investigation shows that the model deficiencies in engine applications often are stationary errors while the dynamics of the engine is well described by the model equations. This together with fairly frequent appearance of sensor offsets have led to a demand for systematic ways of handling stationary errors, also called bias, in both models and sensors.</p><p>In the thesis systematic design methods for reducing bias in estimators are developed. The methods utilize a default model and measurement data. In the first method, a low order description of the model deficiencies is estimated from the default model and measurement data, resulting in an automatic model augmentation. The idea is then to use the augmented model for estimator design, yielding reduced stationary estimation errors compared to an estimator based on the default model. Three main results are: a characterization of possible model augmentations from observability perspectives, an analysis of what augmentations that are possible to estimate from measurement data, and a robustness analysis with respect to noise and model uncertainty.</p><p>An important step is how the bias is modeled, and two ways of describing the bias are introduced. The first is a random walk and the second is a parameterization of the bias. The latter can be viewed as an extension of the first and utilizes a parameterized function that describes the bias as a function of the operating point of the system. The parameters, rather than the bias, are now modeled as random walks, which eliminates the trade-off between noise suppression in the parameter convergence and rapid change of the offset in transients. This is achieved by storing information about the bias in different operating points. A direct application for the parameterized bias is the adaptation algorithms that are commonly used in engine control systems.</p><p>The methods are applied to measurement data from a heavy duty diesel engine. A first order model augmentation is found for a third order model and by modeling the bias as a random walk, an estimation error reduction of 50\,\% is achieved for a European transient cycle. By instead letting a parameterized function describe the bias, simulation results indicate similar, or better, improvements and increased robustness.</p>

<p>Stronger environmental awareness as well as actual and future legislations increase</p><p>the demands on diagnosis and supervision of any vehicle with a combustion engine.</p><p>Particularly this concerns heavy duty trucks, where it is common with long driving</p><p>distances and large engines. Model based diagnosis is an often used method in</p><p>these applications, since it does not require any hardware redundancy.</p><p>Undesired changes in the intake manifold pressure can cause increased emissions.</p><p>In this thesis a diagnosis system for supervision of the intake manifold</p><p>pressure is constructed and evaluated. The diagnosis system is based on a Mean</p><p>Value Engine Model (MVEM) of the intake manifold pressure in a diesel engine</p><p>with Exhaust Gas Recirculation (EGR) and Variable Geometry Turbine (VGT).</p><p>The observer-based residual generator is a comparison between the measured intake</p><p>manifold pressure and the observer based estimation of this pressure. The</p><p>generated residual is then post treated in the CUSUM algorithm based diagnosis</p><p>test.</p><p>When constructing the diagnosis system, robustness is an important aspect. To</p><p>achieve a robust system design, four different observer approaches are evaluated.</p><p>The four approaches are extended Kalman filter, high-gain, sliding mode and an</p><p>adaption of the open model. The conclusion of this evaluation is that a sliding</p><p>mode approach is the best alternative to get a robust diagnosis system in this</p><p>application. The CUSUM algorithm in the diagnosis test improves the properties</p><p>of the diagnosis system further.</p>

<p>The Automated Manual Transmission system Opticruise is dependent on signals from sensors located in different parts of the Scania trucks. These signals are of different qualities and have different update frequencies. Some signals and quantities that are hard or impossible to measure are also of importance to this system.</p><p>In this thesis a driveline observer for the purpose of signal improvement is developed and estimations of unknown quantities such as road incline and mass of the vehicle are performed. The outputs of the observer are produced at a rate of 100 Hz, and include in addition to the mass and road incline also the speed of the engine, output shaft of the gearbox, wheel and the torsion in the driveline. Further the use of an accelerometer and the advantages gained from using it in the observer are investigated.</p><p>The outputs show an increased quality and much of the measurement noise is successfully removed without introducing any time delays. A simulation frequency of 100 Hz is possible, but some dependency toward the stiffness of the driveline is found. The observer manages to estimate the road slope accurately. With the use of an accelerometer the road slope estimation is further improved and a quickly converging mass estimation is obtained.</p>

Résumé : Le travail réalisé au cours de cette thèse traite de la mise au point de méthodes d’estimation d’état robuste, avec deux domaines d’application en ligne de mire. Le premier concerne le positionnement sécuritaire en transport. L’objectif est de fournir la position et la vitesse du véhicule sous la forme d’intervalles avec un grand degré de confiance. Le second concerne la synthèse de capteurs logiciels pour les bioprocédés, et en particulier la reconstruction des concentrations de composants réactionnels à partir d’un nombre limité de mesures et d’un modèle mathématique interprétant le comportement dynamique de ces composants. L’objectif principal est de concevoir des algorithmes qui puissent fournir des estimations acceptables en dépit des incertitudes provenant de la mauvaise connaissance du système comme les incertitudes sur les paramètres du modèle ou les incertitudes de mesures. Dans ce contexte, plusieurs algorithmes ont été étudiés et mis au point. Ainsi, dans le cadre du positionnement de véhicule, la recherche s’est dirigée vers les méthodes robustes Hinfini et les méthodes par intervalles. Les méthodes Hinfini sont des méthodes linéaires prenant en compte une incertitude dans la modélisation et réalisant une optimisation min-max, c’est-à-dire minimisant une fonction de coût qui représente la pire situation compte tenu des incertitudes paramétriques. La contribution de ce travail concerne l’extension à des modèles faiblement non linéaires et l’utilisation d’une fenêtre glissante pour faire face à des mesures asynchrones. Les méthodes par intervalles développées ont pour but de calculer les couloirs de confiance des variables position et vitesse en se basant sur la combinaison d’intervalles issus des capteurs d’une part et sur l’utilisation conjointe d’un modèle dynamique et cinématique du véhicule d’autre part. Dans le cadre des capteurs logiciels pour bioprocédés, trois familles de méthodes ont été étudiées: le filtrage particulaire, les méthodes par intervalles et le filtrage par horizon glissant. Le filtrage particulaire est basé sur des méthodes de Monte-Carlo pour estimer la densité de probabilité conditionnelle de l’état connaissant les mesures. Un de ses principaux inconvénients est sa sensibilité aux erreurs paramétriques. La méthode développée s’applique aux bioprocédés et profite de la structure particulière des modèles pour proposer une version du filtrage particulaire robuste aux incertitudes des paramètres cinétiques. Des méthodes d’estimation par intervalles sont adaptées à la situation où les mesures sont disponibles à des instants discrets, avec une faible fréquence d’échantillonnage, en développant des prédicteurs appropriés. L’utilisation d’un faisceau de prédicteurs grâce à des transformations d’état et le couplage entre les prédicteurs avec des réinitialisations fréquentes permettent d’améliorer les résultats d’estimation. Enfin, une méthode basée sur le filtre à horizon glissant est étudiée en effectuant une optimisation min-max : la meilleure condition initiale est reconstruite pour le plus mauvais modèle. Des solutions sont aussi proposées pour minimiser la quantité de calculs. Pour conclure, les méthodes et résultats obtenus constituent un ensemble d’améliorations dans le cadre de la mise au point d’algorithmes robustes vis-à-vis des incertitudes. Selon les applications et les objectifs fixés, telle ou telle famille de méthodes sera privilégiée. Cependant, dans un souci de robustesse, il est souvent utile de fournir les estimations sous forme d’intervalles auxquels est associé un niveau de confiance lié aux conditions de l’estimation. C’est pourquoi, une des méthodes les plus adaptées aux objectifs de robustesse est représentée par les méthodes par intervalles de confiance et leur développement constituera un point de recherche futur. __________________________________________ Abstract : This thesis work is about the synthesis of robust state estimation methods applied to two different domains. The first area is dedicated to the safe positioning in transport. The objective is to compute the vehicle position and velocity by intervals with a great confidence level. The second area is devoted to the software sensor design in bioprocess applications. The component concentrations are estimated from a limited number of measurements and a mathematical model describing the dynamical behavior of the system. The main interest is to design algorithms which achieve estimation performance and take uncertainties into account coming from the model parameters and the measurement errors. In this context, several algorithms have been studied and designed. Concerning the vehicle positioning, the research activities have led to robust Hinfinity methods and interval estimation methods. The robust Hinfinity methods use a linear model taking model uncertainty into account and perform a min-max optimization, minimizing a cost function which describes the worst-case configuration. The contribution in this domain is an extension to some systems with a nonlinear model and the use of a receding time window facing with asynchronous data. The developed interval algorithms compute confidence intervals of the vehicle velocity and position. They use interval combinations by union and intersection operations obtained from sensors along with kinematic and dynamic models. In the context of bioprocesses, three families of state estimation methods have been investigated: particle filtering, interval methods and moving-horizon filtering. The particle filtering is based on Monte-Carlo drawings to estimate the posterior probability density function of the state variables knowing the measurements. A major drawback is its sensitivity to model uncertainties. The proposed algorithm is dedicated to bioprocess applications and takes advantage of the characteristic structure of the models to design an alternative version of the particle filter which is robust to uncertainties in the kinetic terms. Moreover, interval observers are designed in the context of bioprocesses. The objective is to extend the existing methods to discrete-time measurements by developing interval predictors. The use of a bundle of interval predictors thanks to state transformations and the use of the predictor coupling with reinitializations improve significantly the estimation performance. Finally, a moving-horizon filter is designed, based on a min-max optimization problem. The best initial conditions are generated from the model using the worst parameter configuration. Furthermore, additional solutions have been provided to reduce the computational cost. To conclude, the developed algorithms and related results can be seen as improvements in the design of estimation methods which are robust to uncertainties. According to the application and the objectives, a family may be favored. However, in order to satisfy some robustness criteria, an interval is preferred along with a measure of the confidence level describing the conditions of the estimation. That is why, the development of confidence interval observers represents an important topic in the future fields of investigation.

The current study attempted to discern the extent to which a gender bias influences the adult ratings of observed childhood pain. While gender differences in pain sensation are well documented in physiologically mature individuals, there seems to be no such difference in children. The effect of manipulating gender on the procedural pain ratings of 201 university undergraduate and nursing students was examined via a deceptive pain observation task. Results demonstrated no significant difference between gender conditions; however a strong link was established between prior exposure to painful pediatric medical procedures and lower pain ratings. The results suggest that, while a gender bias failed to alter pain ratings, desensitization to viewing painful procedures could alter how much pain healthcare professionals believe a patient is experiencing.

Stronger environmental awareness as well as actual and future legislations increase the demands on diagnosis and supervision of any vehicle with a combustion engine. Particularly this concerns heavy duty trucks, where it is common with long driving distances and large engines. Model based diagnosis is an often used method in these applications, since it does not require any hardware redundancy. Undesired changes in the intake manifold pressure can cause increased emissions. In this thesis a diagnosis system for supervision of the intake manifold pressure is constructed and evaluated. The diagnosis system is based on a Mean Value Engine Model (MVEM) of the intake manifold pressure in a diesel engine with Exhaust Gas Recirculation (EGR) and Variable Geometry Turbine (VGT). The observer-based residual generator is a comparison between the measured intake manifold pressure and the observer based estimation of this pressure. The generated residual is then post treated in the CUSUM algorithm based diagnosis test. When constructing the diagnosis system, robustness is an important aspect. To achieve a robust system design, four different observer approaches are evaluated. The four approaches are extended Kalman filter, high-gain, sliding mode and an adaption of the open model. The conclusion of this evaluation is that a sliding mode approach is the best alternative to get a robust diagnosis system in this application. The CUSUM algorithm in the diagnosis test improves the properties of the diagnosis system further.

The Automated Manual Transmission system Opticruise is dependent on signals from sensors located in different parts of the Scania trucks. These signals are of different qualities and have different update frequencies. Some signals and quantities that are hard or impossible to measure are also of importance to this system. In this thesis a driveline observer for the purpose of signal improvement is developed and estimations of unknown quantities such as road incline and mass of the vehicle are performed. The outputs of the observer are produced at a rate of 100 Hz, and include in addition to the mass and road incline also the speed of the engine, output shaft of the gearbox, wheel and the torsion in the driveline. Further the use of an accelerometer and the advantages gained from using it in the observer are investigated. The outputs show an increased quality and much of the measurement noise is successfully removed without introducing any time delays. A simulation frequency of 100 Hz is possible, but some dependency toward the stiffness of the driveline is found. The observer manages to estimate the road slope accurately. With the use of an accelerometer the road slope estimation is further improved and a quickly converging mass estimation is obtained.

Based on the Lyapunov stability theorem, a modified stability analysis as well as a modified observer is proposed in this thesis for a class of uncertain nonlinear systems with an existent high gain observer. By assuming that the first two state variables are indirectly measurable, reanalyzing the stability of the error dynamics is presented first. The advantage of this modified analytic method is that the upper bound of the disturbance distribution functions is not required to be known in advance, and the asymptotic stability is still guaranteed. Next, based on this existent observer, a slightly modified observer is presented for systems with disturbances whose upper bound is unknown. An adaptive mechanism is embedded in the proposed observer, so that the upper bound of perturbations is not required to be known beforehand. The resultant dynamics of estimation errors can be driven into the sliding surface in a finite time, and guarantee asymptotic stability. A numerical example and a practical example are given to demonstrate the feasibility of the proposed observer.

In this dissertation three robust control strategies are proposed for a class of multi-input multi-output dynamic systems with matched or mismatched perturbations. Firstly, an adaptive variable structure observer and controller are introduced for solving the regulation problems, where some state variables are not measurable. By utilizing adaptive mechanisms in the design of sliding mode controller, one can enable the controlled systems not only to generate a reaching mode in finite time, but also to suppress the mismatched perturbations during the sliding mode. Secondly, the design of adaptive sliding mode controllers with application to robot manipulators is presented to solve the tracking problems. The dynamic equations of the controlled systems contain a perturbed leading coefficient matrix and can be either positive definite or negative definite. The asymptotical stability of the controlled systems will be attained if the proposed control scheme is employed. Thirdly, a design methodology of adaptive sliding mode controller based on T-S fuzzy model is proposed to solve tracking problems. It is shown that the trajectories of the controlled systems can be driven into a designated sliding surface in finite time, and the property of asymptotical stability is also guaranteed. All these three control schemes are designed by means of Lyapunov stability theorem. Each control scheme contains three parts. The first part is designed for eliminating measurable feedback signals. The second part is used for adjusting the convergent rate of state variables (or tracking errors) of the controlled system. The third part is the adaptive control mechanism, which is used to adapt some unknown constants of the least upper bounds of perturbations, so that the knowledge of the least upper bounds of matched or mismatched perturbations are not required. Several numerical examples and an application of controlling robot manipulator are demonstrated for showing the feasibility of the proposed control methodologies.

This dissertation addresses three diverse research problems pertaining to rigid body attitude stabilization and control. The problems addressed result in theoretical development for the topics of cooperative control, delayed feedback, and state estimation, through the formulation of a novel class of switching observers. In the area of consensus management for cooperative control, the problem of designing torque control laws that synchronize the attitude of a team of rigid bodies under constant, unknown communication time delays is addressed. Directed communication graphs are considered, which encompass both leader-follower and leaderless architectures. A feedback linearization result involving the Modified Rodrigues parameter (MRP) representation of attitude kinematics reduces the attitude dynamics equations to double integrator agents and the remainder of the control effort achieves position consensus. New necessary and sufficient delay dependent stability conditions for the system of double integrator agents are presented. This dissertation also considers the problem of stabilizing attitude dynamics with unknown piecewise-constant delayed feedback. The problem is addressed through stability analysis of switched linear time-invariant and nonlinear timedelay systems. In the case of linear systems with switched delay feedback, a new sufficiency condition for average dwell time result is presented using a complete type Lyapunov-Krasovskii (L-K) functional approach. Further, the corresponding switched system with nonlinear perturbations is proven to be exponentially stable inside a well characterized region of attraction for an appropriately chosen average dwell time. Finally, this dissertation provides a new switching angular velocity observer formulation to the classical problem of rigid body attitude tracking in the absence of angular rate measurements. Exponential convergence of the angular velocity state estimation errors is proven independent of control design by using a novel error signal definition through this switching-type observer. The switching ensures C0 continuity for all the estimated states. Further, the maximum number of switches required by the observer is shown to be finite and that zeno-type behavior cannot occur. A “separation property” type result in the absence of actual angular rate measurements is established, wherein a linear and nonlinear controller utilizes angular velocity estimates from the proposed observer to achieve attitude tracking. / text