System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the systems. Most of the conventional techniques of system identification, in general, require some amount of a priori knowledge about the structure of the systems. Also, they are only useful either with linear or linearized systems. There are numerous control principles working nicely in industry. However, they are less effective for MIMO systems or complex nonlinear systems. The need to control, in a better way, increasingly complex dynamical systems under significant uncertainty has made the need for new methods quite apparent. This thesis investigates different approaches for identification and control of complex nonlinear systems using neural networks. For system identification and control, ANN properties of generalization and their capability of extracting complex relationships among inputs presented to them are useful. Two different techniques, called whole region method (WRM) and the separate regions method (SRM) technique, have been developed and applied to two classes of nonlinear systems. Different connectionist control techniques such as adaptive control and neuro-PID control have been developed and applied to the robotic manipulators. / Master of Science

Modelling neuronal interactions using a directed network can be used to provide insight into the activity of the brain during experimental tasks. Magnetoencephalography (MEG) allows for the observation of the fast neuronal dynamics necessary to characterize the activity of sources and their interactions. A network representation of these sources and their connections can be formed by mapping them to nodes and their connection strengths to edge weights. Dynamic Causal Modelling (DCM) presents a Bayesian framework to estimate the parameters of these networks, as well as the ability to test hypotheses on the structure of the network itself using Bayesian model comparison. DCM uses a neurologically-informed representation of the active neural sources, which leads to an underdetermined system and increased complexity in estimating the network parameters. This work shows that inform- ing the MEG DCM source location with prior distributions defined using a MEG source localization algorithm improves model selection accuracy. DCM inversion of a group of candidate models shows an enhanced ability to identify a ground-truth network structure when source-localized prior means are used. / Master of Science

Good motion cueing in a flight simulator serves to enhance the overall simulation environment. However, poor motion cueing can greatly detract from the simulation and serve solely to distract the pilot. The latter was the case for Virginia Tech's three degree-of-freedom motion-base. The most common method of motion cueing is to use washout filters to produce the best motion cues within the physical limitations of the motion system. This algorithm is named the classical washout algorithm and its filters were studied first in this research, but initially yielded undesirable results. In efforts to greatly improve the acceleration response in the pitch axis, the concept of an acceleration feedback controller in conjunction with washout filters was investigated. In developing a mathematical model of the motion-base and its corresponding circuitry, corrections and modifications were made to the circuitry which served to improve the dynamic response of the motion-base and enhance motion sensations. Next, design and implementation of the acceleration feedback controller for the pitch axis was performed and tested using a pilot rating scale and time history responses. The parameters for the acceleration feedback algorithm and the classical washout algorithm were varied to find the most favorable algorithm and set of parameters. Results of this paper have demonstrated the successful implementation of acceleration feedback and that the motion system at Virginia Tech now serves to greatly enhance the simulation environment. / Master of Science

Identification of thermoacoustic dynamics that exhibit limit cycle behavior is needed to gain a better intuitive feel of the system, to design complex control strategies, and to validate modeling efforts. Limit cycle oscillations arise in thermoacoustic systems due to the coupling between a nonlinear heat release process and the acoustic dynamics of the combustor. This response arises in lean premixed gaseous power generating turbines and is a concern due to the detrimental effect of the pressure oscillations on the structural integrity of the combustor. Due to the volatile environment intrinsic in the combustor, multiple sensing apparatuses are not available. Therefore, in the current study, an identification approach is assessed considering only a single output from the thermoacoustic system. As a means to further investigate the thermoacoustic limit cycle behavior, a scaled version of the industry-based turbine was constructed. By anchoring a flame halfway from end-to-end of a closed-open tube, a similar nonlinear response is achieved. A harmonic balance technique that linearly incorporates the nonlinearity is developed which uses frequency entrainment to offer sufficient information for the identification. Its validity is assessed on a model, which is based on known dynamics of the thermoacoustic system. The structure of the identification algorithm is based on a two-mode acoustic model with both dynamics and nonlinearity in the feedback loop. The limitations of using only a two-mode identification structure for a system with more than two modes is discussed as well as future efforts that may alleviate this problem. / Master of Science

No description available.

For controller design, design of experiments, and other dynamic simulation purposes there is a need to be able to predict the dynamic response and joint reaction forces of a quarter-car suspension. This need is addressed by this study through development and system identification of both a linear and a non-linear multibody dynamics McPherson strut quarter-car suspension model. Both models are developed using a method customary to multibody dynamics so that the same numerical integrator can be used to compare their respective performances. This method involves using the Lagrange multiplier form of the constrained equations of motion to assemble a set of differential algebraic equations that characterize each model's dynamic response. The response of these models to a band-limited random tire displacement time array is then simulated using a Hilber-Hughes-Taylor integrator. The models are constructed to match the dynamic response of a state-of-the-art quarter-car test rig that was designed, constructed, and installed at the Institute for Advanced Learning and Research (IALR) for the Performance Engineering Research Lab (PERL). Attached to the experimental quarter-car rig was the front left McPherson strut suspension from a 2004 Porsche 996 Grand American Cup GS Class race car. This quarter-car rig facilitated acquisition of the experimental reference data to which the simulated data is compared. After developing these models their optimal parameters are obtained by performing system identification. The performance of both models using their respective optimal parameters is presented and discussed in the context of the basic linearity of the experimental suspension. Additionally, a method for estimating the loads applied to the experimental quarter-car rig bearings is developed. Finally, conclusions and recommendations for future research and applications are presented. / Master of Science

<p>Direkt viktoptimering (Direct Weight Optimization, DWO) är en ickeparamterisk systemidentifieringsmetod. DWO bygger på att man skattar ett funktionsvärde i en viss punkt genom en viktad summa av mätvärden, där vikterna optimeras fram. Det faktum att DWO har en inparameter som man måste veta i förväg leder till att man på något sätt vill skatta denna inparameter. Det finns många sätt man kan göra denna skattning på men det centrala i denna uppsats är att skatta inparametern lokalt. Fördelen med detta är att metoden anpassar sig om till exempel systemet ändrar beteende från att variera långsamt till att variera snabbare. Denna typ av metoder brukar kallas adaptiva metoder.Det finns flera metoder för att skatta en inparameter lokalt och anpassningen till DWO är redan klar för ett fåtal som lämpar sig bra. Det är dock inte undersökt vilken av dessa metoder som ger det bästa resultatet för just DWO. Syftet med denna uppsats är alltså att ta reda på hur man lokalt kan skatta en inparameter till DWO på bästa sätt och om DWO är en bra grund att basera en adaptiv metod på.Det har visat sig att DWO kanske är för känslig för en lokalt vald inparameter för att vara en bra grund att basera en adaptiv metod på. Däremot utmärker sig en av metoderna för att skatta inparametern genom att vara mycket bättre än de andra metoderna när den kanske inte borde vara det. Varför den är så bra kan vara ett bra ämne för vidare forskning.</p> / <p>Direct Weight Optimization (DWO) is a nonparametric system identification meth\-od. In DWO the value of a function in a certain point is estimated by a weighted sum of measured values. The weights are obtained as a solution to a convex optimization problem. DWO has a design parameter which has to be chosen or estimated a priori. There are many ways to estimate this parameter. The main focus of this thesis is to estimate this parameter locally. The advantage of estimating the parameter locally is that the estimate will adapt if the system changes behavior from slowly varying to rapidly varying. Estimation methods of this type are usually called adaptive estimation methods.There are a number of adaptive estimation methods and the adaptation of some of these methods to DWO has already been done. There are however no evaluation studies done. The goal with this thesis is therefore to find out how to estimate the parameter in DWO in the best way and to find out whether DWO is a good base for an adaptive method.It turned out that DWO might be too sensitive to local changes in the design parameter to be a good base for an adaptive method. However, one of the adaptive estimation methods stands out from the rest because it is much better than the other methods when it, perhaps, should not. Why this method is good might be a good subject for further research.</p>

Direkt viktoptimering (Direct Weight Optimization, DWO) är en ickeparamterisk systemidentifieringsmetod. DWO bygger på att man skattar ett funktionsvärde i en viss punkt genom en viktad summa av mätvärden, där vikterna optimeras fram. Det faktum att DWO har en inparameter som man måste veta i förväg leder till att man på något sätt vill skatta denna inparameter. Det finns många sätt man kan göra denna skattning på men det centrala i denna uppsats är att skatta inparametern lokalt. Fördelen med detta är att metoden anpassar sig om till exempel systemet ändrar beteende från att variera långsamt till att variera snabbare. Denna typ av metoder brukar kallas adaptiva metoder.Det finns flera metoder för att skatta en inparameter lokalt och anpassningen till DWO är redan klar för ett fåtal som lämpar sig bra. Det är dock inte undersökt vilken av dessa metoder som ger det bästa resultatet för just DWO. Syftet med denna uppsats är alltså att ta reda på hur man lokalt kan skatta en inparameter till DWO på bästa sätt och om DWO är en bra grund att basera en adaptiv metod på.Det har visat sig att DWO kanske är för känslig för en lokalt vald inparameter för att vara en bra grund att basera en adaptiv metod på. Däremot utmärker sig en av metoderna för att skatta inparametern genom att vara mycket bättre än de andra metoderna när den kanske inte borde vara det. Varför den är så bra kan vara ett bra ämne för vidare forskning. / Direct Weight Optimization (DWO) is a nonparametric system identification meth\-od. In DWO the value of a function in a certain point is estimated by a weighted sum of measured values. The weights are obtained as a solution to a convex optimization problem. DWO has a design parameter which has to be chosen or estimated a priori. There are many ways to estimate this parameter. The main focus of this thesis is to estimate this parameter locally. The advantage of estimating the parameter locally is that the estimate will adapt if the system changes behavior from slowly varying to rapidly varying. Estimation methods of this type are usually called adaptive estimation methods.There are a number of adaptive estimation methods and the adaptation of some of these methods to DWO has already been done. There are however no evaluation studies done. The goal with this thesis is therefore to find out how to estimate the parameter in DWO in the best way and to find out whether DWO is a good base for an adaptive method.It turned out that DWO might be too sensitive to local changes in the design parameter to be a good base for an adaptive method. However, one of the adaptive estimation methods stands out from the rest because it is much better than the other methods when it, perhaps, should not. Why this method is good might be a good subject for further research.

One of the difficulties of human figure tracking is that humans move their bodies in complex, non-linear ways. An effective computational model of human motion could therefore be of great benefit in figure tracking. We are interested in the use of a class of dynamic models called switching linear dynamic systems for figure tracking. This thesis makes two contributions. First, we present an empirical analysis of some of the technical issues involved with applying linear dynamic systems to figure tracking. The lack of high-level theory in this area makes this type of empirical study valuable and necessary. We show that sensitivity of these models to perturbations in input is a central issue in their application to figure tracking. We also compare different types of LDS models and identification algorithms. Second, we describe 2-DAFT, a flexible software framework we have created for figure tracking. 2-DAFT encapsulates data and code involved in different parts of the tracking problem in a number of modules. This architecture leads to flexibility and makes it easy to implement new tracking algorithms.

La localisation de dégâts basée sur les mesures de vibrations est devenue un axe de recherche important pour la surveillance de la santé structurale (SHM). En particulier, la Stochastic Dynamic Damage Locating Vector (SDDLV) est une méthode de localisation des dégâts basée sur le couplage entre un modèle aux éléments finis (FE) de la structure et des paramètres modaux estimés à partir des mesures dynamiques en excitation ambiante dans les états structuraux sain et endommagé, interrogeant les changements dans la matrice de transfert. Dans la première contribution, la méthode SDDLV est étendue avec une approche statistique conjointe utilisant plusieurs ensembles de modes, surmontant la limitation théorique sur le nombre minimal de paramètres. Un autre problème traité est la performance de la méthode en fonction du choix de la variable de Laplace où la fonction de transfert est évaluée. Une attention particulière est accordée à ce choix et à son optimisation. Dans la deuxième contribution, l'approche Influence Line Damage Location (ILDL), complémentaire à l’approche SDDLV est étendue avec un cadre statistique. Dans la dernière contribution, une approche de sensibilité pour les petits dommages est développée en fonction de la différence des matrices de transfert, permettant la localisation des dommages par des tests statistiques dans un cadre gaussien, et en plus la quantification des dommages dans une deuxième étape. Enfin, les méthodes proposées sont validées sur des simulations numériques et leurs performances sont testées dans de nombreuses études de cas sur des expériences de laboratoire. / Vibration-based damage localization has become an important issue for Structural Health Monitoring (SHM). Particularly, the Stochastic Dynamic Damage Locating Vector (SDDLV) method is an output-only damage localization method based on both a Finite Element (FE) model of the structure and modal parameters estimated from output-only measurements in the reference and damaged states of the system, interrogating changes in the transfer matrix. Firstly, the SDDLV method has been extended with a joint statistical approach for multiple mode sets, overcoming the theoretical limitation on the number of modes in previous works. Another problem is that the performance of the method can change considerably depending on the Laplace variable where the transfer function is evaluated. Particular attention is given to this choice and how to optimize it. Secondly, the Influence Line Damage Location (ILDL) approach which is complementary to the SDDLV approach has been extended with a statistical framework. Thirdly, a sensitivity approach for small damages has been developed based on the transfer matrix difference, allowing damage localization through statistical tests in a Gaussian framework, and in addition the quantification of the damage in a second step. Finally, the proposed methods are validated on numerical simulations and their performances are tested extensively in numerous case studies on lab experiments.