The problem of parameter and state estimation of a class of nonlinear systems is addressed. An adaptive identifier and observer are used to estimate the parameters and the state variables simultaneously. The proposed method is derived using a new formulation. Uncertainty sets are defined for the parameters and a set of auxiliary variables for the state variables. An algorithm is developed to update these sets using the available information. The algorithm proposed guarantees the convergence of parameters and the state variables to their true value. In addition to its application in difficult estimation problems, the algorithm has also been adapted to handle fault detection problems. The technique of estimation is applied to two broad classes of systems. The first involves a class of continuous time nonlinear systems subject to bounded unknown exogenous disturbance with constant parameters. Using the proposed set-based adaptive estimation, the parameters are updated only when an improvement in the precision of the parameter estimates can be guaranteed. The formulation provides robustness to parameter estimation error and bounded disturbance. The parameter uncertainty set and the uncertainty associated with an auxiliary variable is updated such that the set is guaranteed to contain the unknown true values. The second class of system considered is a class of nonlinear systems with timevarying parameters. Using a generalization of the set-based adaptive estimation technique proposed, the estimates of the parameters and state are updated to guarantee convergence to a neighborhood of their true value. The algorithm proposed can also be extended to detect the fault in the system, injected by drastic change in the time-varying parameter values. To study the practical applicability of the developed method, the estimation of state variables and time-varying parameters of salt in a stirred tank process has been performed. The results of the experimental application demonstrate the ability of the proposed techniques to estimate the state variables and time-varying parameters of an uncertain practical system. / Thesis (Master, Chemical Engineering) -- Queen's University, 2011-10-31 22:04:58.762

7th IFAC Symposium on Advances in Automotive Control, The International Federation of Automatic Control, September 4-7, 2013. Tokyo, Japan

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.

Loss of Control (LOC) accidents are a major threat for aviation, and contribute the highest risk for fatalities in all aviation accidents. The major contributor to LOC accidents is pilot spatial disorientation (SD), which accounts for roughly 32% of all LOC accidents. A pilot experiences SD during flight when the pilot's expectation of the aircraft's state deviates from reality. This deviation results from a number of underlying mechanisms, such as distraction, failure to monitor flight instruments, and vestibular illusions. Previous researchers have developed computational models to understand those mechanisms. However, these models are limited in scope as they do not model the pilot's knowledge of the aircraft dynamics. This research proposes a novel model to predict the best-possible-pilot-expectation of the aircraft state given vestibular and visual cues. The proposed model uses a Model-Based Observer (MBO) as the infrastructure needed to establish an “expert pilot”. Expert pilots are known to form an internal model of the operated system through training and experience, which allows the expert to generate better internal expectations of the system states. Pilots' internal expectations are enhanced by the presence of information fed through the pilots̕ sensory systems. Thus, the proposed model integrates pilot's knowledge of the system dynamics (i.e. an aircraft model) with a continuous vestibular sensory model and a discrete visual-sampling sensory model. The computational model serves to investigate the underlying mechanisms of SD during flight and provide a quantitative analysis tool to support flight deck and countermeasure designs.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2006. / Härtill 4 uppsatser.

Le 21_eme siècle est le siècle de l'explosion informatique, des milliards de Données sont produites, collectées et stockées dans notre vie quotidienne. Les façons de collecter les ensembles de données sont multiples mais toujours en essayant d'optimiser le critère qui consiste _a avoir le maximum d'information dans le minimum de données numérique. Il est préférable de collecter directement l'information, car les informations étant contraintes sont dans un espace plus faible que celui où évolues les données (signaux ou états). Cette méthode est donc appelée \la collecte de l'information", et conceptuellement peut ^être résumée dans les trois étapes suivantes : (1) la modélisation, ceci consiste _a condenser l'information pertinente pour les signaux _a un sous-espace plus petit; (2) l'acquisition, ceci consiste _a collecter et préserver l'information dans un espace inferieur _a la dimension des données et (3) la restauration, ceci consiste _a reconstituer l'information dans son espace d'origine. En suivant cette pensée, les principales contributions de cette thèse, concernant les observateurs et le \Compressive Sensing" (CS) basé sur des modèles bay_esiens peuvent ^être unies dans le cadre de la collecte de l'information : les principaux problèmes concernés par ces deux applications peuvent ^être de façon analogue, scindés en les trois étapes sus- mentionnées. Dans la première partie de la th_ese, le problème réside dans le domaine des systèmes dynamiques où l'objectif est de retrouver l'état du système _a partir de la mesure de la sortie. Il nous faut donc déterminer si les états du système sont récupérables _a partir des mesures de la sortie et de la connaissance partielle ou totale du modèle dynamique, c'est le problème de l'observabilité. Ensuite de transposer notre problème dans une représentation plus appropriée, c'est l'écriture sous forme normale et en récupérer l'information, c'est la phase de synthèse d'observateur. Plus précisément dans cette partie, nous avons considéré une classe de systèmes à commutation haute fréquence allant jusqu'au phénomène de Zénon. Pour ces deux types de commutation les transitions de l'état discret sont considérées trop élevées pour ^être mesurées. Toutefois, la valeur moyenne obtenue par filtrage des transitions peut ^être acquise ce qui donne une connaissance partielle des états discrets. Ici, avec ces seuls informations partielles, nous avons discuté de l'observabilité et ceci par les approches géométrie différentielle et algébrique. Aussi, des observateurs ont été proposes par la suite. Dans la deuxième partie de cette thèse, nous avons abordé de la même manière le thème du CS qui est une alternative efficace à l'acquisition abondante de données faiblement informatives pour ensuite les compresser. Le CS se propose de collecter l'information directement de façon compressée, ici les points clés sont la modélisation du signal en fonction des connaissances a priori dont on dispose, ainsi que la construction d'une matrice de mesure satisfaisant la \restricted isometry property" et finalement la restauration des signaux originaux clairsemés en utilisant des algorithmes d'éparpillement régularisé et d'inversion linéaire. Plus précisément, dans cette seconde partie, en considérant les propriétés du CS liées _a la modélisation, la capture et la restauration, il est proposé : (1) d'exploiter les séquences chaotiques pour construire la matrice de mesure qui est appelée la matrice chaotique de mesure, (2) considérer des types de modèle de signal clairsemé et reconstruire le modèle du signal à partir de ces structures sous-jacentes des modèles clairsemés, et (3) proposer trois algorithmes non paramétriques pour la méthode bayesienne hiérarchique. Dans cette dernière partie, des résultats expérimentaux prouvent d'une part que la matrice chaotique de mesure a des propriétés semblables aux matrices aléatoires sous-gaussienne et d'autre part que des informations supplémentaires sur les structures sous-jacentes clairsemés améliorent grandement les performances de reconstruction du signal et sa robustesse vis-a-vis du bruit. / This is the era of information-explosion, billions of data are produced, collected and then stored in our daily life. The manners of collecting the data sets are various but always following the criteria { the less data while the more information. Thus the most favorite way is to directly measure the information, which, commonly, resides in a lower dimensional space than its carrier, namely, the data (signals or states). This method is thus called information measuring, and conceptually can be concluded in a framework with the following three steps: (1) modeling, to condense the information relevant to signals to a small subspace; (2) measuring, to preserve the information in lower dimensional measurement space; and (3) restoring, to reconstruct signals from the lower dimensional measurements. From this vein, the main contributions of this thesis, saying observer and model based Bayesian compressive sensing can be well uni_ed in the framework of information measuring: the main concerned problems of both applications can be decomposed into the above three aspects. In the _rst part, the problem is resided in the domain of control systems where the objective of observer design is located in the observability to determine whether the system states are recoverable and observation of the system states from the lower dimensional measurements (commonly but not restrictively). Speci_cally, we considered a class of switched systems with high switching frequency, or even with Zeno phenomenon, where the transitions of the discrete state are too high to be captured. However, the averaged value obtained through filtering the transitions can be easily sensed as the partial knowledge. Consequently, only with this partial knowledge, we discussed the observability respectively from differential geometric approach and algebraic approach and the corresponding observers are designed as well. At the second part, we switched to the topic of compressive sensing which is objected to sampling the sparse signals directly in a compressed manner, where the central fundamentals are resided in signal modeling according to available priors, constructing sensing matrix satisfying the so-called restricted isometry property and restoring the original sparse signals using sparse regularized linear inversion algorithms. Respectively, considering the properties of CS related to modeling, measuring and restoring, we propose to (1) exploit the chaotic sequences to construct the sensing matrix (or measuring operator) which is called chaotic sensing matrix, (2) further consider the sparsity model and then rebuild the signal model to consider structures underlying the sparsity patterns, and (3) propose three non-parametric algorithms through the hierarchical Bayesian method. And the experimental results prove that the chaotic sensing matrix is with the similar property to sub-Gaussian random matrix and the additional consideration on structures underlying sparsity patterns largely improves the performances of reconstruction and robustness.

Models are of great interest in many fields of engineering as they enable prediction of a systems behaviour, given an initial mode of the system. However, in the field of model-based diagnosis the models are used in a reverse manner, as they are combined with the observations of the systems behaviour in order to estimate the system mode. This thesis describes computation of diagnostic systems based on models implemented in Dymola. Dymola is a program that uses the language Modelica. The Dymola models are translated to Matlab, where an application called Fault Diagnosis Toolbox, FDT is applied. The FDT has functionality for pinpointing minimal overdetermined sets of equations, MSOs, which is developed further in this thesis. It is shown that the implemented algorithm has exponential time complexity with regards to what level the system is overdetermined,also known as the degree of redundancy. The MSOs are used to generate residuals, which are functions that are equal to zero given that the system is fault-free. Residual generation in Dymola is added to the original methods of the FDT andthe results of the Dymola methods are compared to the original FDT methods, when given identical data. Based on these tests it is concluded that adding the Dymola methods to the FDT results in higher accuracy, as well as a new way tocompute optimal observer gain. The FDT methods are applied to 2 models, one model is based on a system ofJAS 39 Gripen; SECS, which stands for Secondary Enviromental Control System. Also, applications are made on a simpler model; a Two Tank System. It is validated that the computational properties of the developed methods in Dymolaand Matlab differs and that it therefore exists benefits of adding the Dymola implementations to the current FDT methods. Furthermore, the investigation of the potential isolability based on the current setup of sensors in SECS shows that full isolability is achievable by adding 2 mass flow sensors, and that the isolability is not limited by causality constraints. One of the found MSOs is solvable in Dymola when given data from a fault-free simulation. However, if the simulation is not fault-free, the same MSO results in a singular equation system. By utilizing MSOs that had no reaction to any modelled faults, certain non-monitored faults is isolated from the monitored ones and therefore the risk of false alarms is reduced. Some residuals are generated as observers, and a new method for constructing observers is found during the thesis by using Lannerheds theorem in combination with Pontryagin’s Minimum Priniple. This method enables evaluation of observer based residuals in Dymola without any selection of a specific operating point, as well as evaluation of observers based on high-index Differential Algebraic Equations, DAEs. The method also results in completely different behaviourof the estimation error compared to the method that is already implemented inthe FDT. For example, one of the new observer-implementations achieves both an estimation error that converges faster towards zero when no faults are implementedin the monitored system, and a sharper reaction to implemented faults.

With numerous distinct advantages, quadrotors have found a wide range of applications, such as structural inspection, traffic control, search and rescue, agricultural surveillance, etc. To better serve applications in cluttered environment, quadrotors are further equipped with vision sensors to enhance their state sensing and environment perception capabilities. Moreover, visual information can also be used to guide the motion control of the quadrotor. This is referred to as visual servoing of quadrotor. In this thesis, we identify the challenging problems arising in the area of visual servoing of the quadrotor and propose effective control strategies to address these issues. The control objective considered in this thesis is to regulate the relative pose of the quadrotor to a ground target using a limited number of sensors, e.g., a monocular camera and an inertia measurement unit. The camera is attached underneath the center of the quadrotor and facing down. The ground target is a planar object consisting of multiple points. The image features are selected as image moments defined in a ``virtual image plane". These image features offer an image kinematics that is independent of the tilt motion of the quadrotor. This independence enables the separation of the high level visual servoing controller design from the low level attitude tracking control. A high-gain observer-based model predictive control (MPC) scheme is proposed in this thesis to address the image-based visual servoing of the quadrotor. The high-gain observer is designed to estimate the linear velocity of the quadrotor which is part of the system states. Due to a limited number of sensors on board, the linear velocity information is not directly measurable. The high-gain observer provides the estimates of the linear velocity and delivers them to the model predictive controller. On the other hand, the model predictive controller generates the desired thrust force and yaw rate to regulate the pose of the quadrotor relative to the ground target. By using the MPC controller, the tilt motion of the quadrotor can be effectively bounded so that the scene of the ground target is well maintained in the field of view of the camera. This requirement is referred to as visibility constraint. The satisfaction of visibility constraint is a prerequisite of visual servoing of the quadrotor. Simulation and experimental studies are performed to verify the effectiveness of the proposed control strategies. Moreover, image processing algorithms are developed to extract the image features from the captured images, as required by the experimental implementation. / Graduate / 2020-12-11

Infants begin to learn important skills, such as contingency learning, social referencing, and joint attention through everyday interactions with their environment. When infants learn that their behavior produces a change in the environment (e.g., attention from others), infants engage in behavior that produces that effect (e.g., increases in smiling sustained engagement. When mothers and other caregivers respond immediately to infant behavior, they help their infant learn that the infant’s own behavior is effective, producing a change in the environment. The current investigation evaluated the effect of a computer-based training that aimed at teaching mothers to play a vocal-imitation contingency-learning game. The training included observer-effect methodology, meaning the mothers engaged in observation and evaluation of other mothers engaging in vocal imitation but did not themselves receive any direct coaching or feedback. All mothers completed the training during one session and in less than 45 min. Results indicate that all mothers increased their use of vocal imitation post training and maintained their performance at a two-week follow-up. Results are discussed in terms of how computer training may facilitate dissemination of responsive caregiver training.

The topic of this thesis is temporal logical inference in atemporal models of time. The thesis presents a branching logic with observers for the purpose of this investigation. A series of theorems rigorously demonstrate that temporal reasoning is possible also in an atemporal world. 1