This thesis presents the modeling of tool data produced during ion implantation for the prediction of wafer sheet resistance. In this work, we will use various statistical techniques to address challenges due to the nature of equipment data: high dimensionality, colinearity, parameter interactions, and non-linearities. The emphasis will be data integrity, variable selection, and model building methods. Different variable selection and modeling techniques will be evaluated using an industrial data set. Ion implant processes are fast and depending on the monitoring frequency of the equipment, late detection of a process shift could lead to the loss of a significant amount of product. The main objective of the research presented in this thesis is to identify any ion implant parameters that can be used to formulate a virtual metrology model. The virtual metrology model would then be used for process monitoring to ensure stable processing conditions and consequent yield guarantees. / text

This thesis focuses particularly on the application of chemometrics in the field of analytical chemistry. Chemometrics (or multivariate analysis) consists in finding a relationship between two groups of variables, often called dependent and independent variables. In infrared spectroscopy for instance, chemometrics consists in the prediction of a quantitative variable (the obtention of which is delicate, requiring a chemical analysis and a qualified operator), such as the concentration of a component present in the studied product from spectral data measured on various wavelengths or wavenumbers (several hundreds, even several thousands). In this research we propose a methodology in the field of chemometrics to handle the chemical data (spectrophotometric data) which are often in high dimension. To handle these data, we first propose a new incremental method (step-by-step) for the selection of spectral data using linear and non-linear regression based on the combination of three principles: linear or non-linear regression, incremental procedure for the variable selection, and use of a validation set. This procedure allows on one hand to benefit from the advantages of non-linear methods to predict chemical data (there is often a non-linear relationship between dependent and independent variables), and on the other hand to avoid the overfitting phenomenon, one of the most crucial problems encountered with non-linear models. Secondly, we propose to improve the previous method by a judicious choice of the first selected variable, which has a very important influence on the final performances of the prediction. The idea is to use a measure of the mutual information between the independent and dependent variables to select the first one; then the previous incremental method (step-by-step) is used to select the next variables. The variable selected by mutual information can have a good interpretation from the spectrochemical point of view, and does not depend on the data distribution in the training and validation sets. On the contrary, the traditional chemometric linear methods such as PCR or PLSR produce new variables which do not have any interpretation from the spectrochemical point of view. Four real-life datasets (wine, orange juice, milk powder and apples) are presented in order to show the efficiency and advantages of both proposed procedures compared to the traditional chemometric linear methods often used, such as MLR, PCR and PLSR.

Karst rocky desertification is a progressive process of land degradation in karst regions in which soil is severely, or completely, eroded. This process may be caused by natural factors, such as geological structure, and population pressure leading to poor ecosystem health and lagging economic development. Karst rocky desertification is therefore a significant obstacle to sustainable development in southwest China. We applied a radial basis function network model to assess the risk of karst rocky desertification in northwest Guangxi, a typical karst region located in southwest China. Factors known to influence karst rocky desertification were evaluated using remote sensing and geographic information systems techniques to classify the 23 counties in the study area from low to extreme risk of karst rocky desertification. Counties with extreme or strong karst rocky desertification risk (43.48%, nearly half of the study area) were clustered in the north, central and southeast portions of the study area. Counties with low karst rocky desertification (30.43%) were located in the west, northeast and southwest of the study area. The spatial distribution of karst rocky desertification was moderately correlated to population density.

Les progrès de la mécatronique ont permis d’améliorer les prothèses du membre supérieur en augmentant le catalogue des mouvements prothétiques. Cependant, un fossé se creuse entre les capacités technologiques de la prothèse et leur méthode de contrôle. La commande myoélectrique, qui est la méthode la plus répandue, reste complexe, notamment pour les personnes amputées au niveau trans-huméral qui peuvent avoir un coude actif en plus de la main et du poignet motorisés. Une approche intéressante consiste à utiliser la mobilité du membre résiduel, présente chez la plupart des amputés trans-huméraux, pour contrôler des articulations prothétiques distales comme le coude. Les mouvements du coude sont couplés aux mouvements du membre résiduel selon un modèle de coordination épaule/coude saine. Cette thèse étudie une stratégie de commande d’un coude prothétique utilisant les mouvements du membre résiduel, mesuré par des centrales inertielles, et nos connaissances du contrôle moteur humain. Pour cela, un modèle de la coordination épaule/coude a été construit à partir d’enregistrements de gestes sains de préhension. Ce modèle, implémenté sur un prototype de prothèse, a été testé par 10 individus sains équipés du prototype afin de valider le concept, puis par 6 personnes amputées. Ces dernières ont aussi réalisé la tâche avec une commande myoélectrique conventionnelle afin de comparer les résultats. La commande couplant automatiquement les mouvements de l’épaule et du coude s’est montrée satisfaisante en termes de facilité d’utilisation et de réduction des stratégies de compensation. / Progress in mechatronics has enabled the improvement of upper limb prosthetics increasing the grasps catalog. However, a gap has been growing between the prosthesis technological possibilities and the methods to control it. Indeed, common myoelectric control strategy remains complex, especially for transhumeral amputees who can have an active elbow in addition to a prosthetic wrist and hand. Since most transhumeral amputees have a mobile residual limb, an interesting approach aims at utilizing this mobility to control intermediate prosthetic joints, like the elbow, based on the shoulder/elbow coordination observed in healthy movements. This thesis investigates the possibility of controlling an active prosthetic elbow using the residual limb motion, measured with inertial measurement units, and knowledge of the human motor control. A primary focus has been targeting the reaching movement for which a model has been built using regression tools and kinematic data from several healthy individuals. The model, implemented on a prosthesis prototype, has been tested with 10 healthy participants wearing the prototype to validate the concept, and with 6 amputated individuals. These participants also performed the task with a conventional myoelectric control strategy for comparison purpose. The results show that the inter-joint coordination-based control strategy is satisfying in terms of intuitiveness and reduction of the compensatory strategies.

Countries world-over have thousands of critical structures and bridges which have been built decades back when strength-based designs were the order of the day. Over the years, magnitude and frequency of loadings on these have increased. Also, these structures have been exposed to environmental degradation during their service life. Hence, structural health monitoring (SHM) has attracted the attention of researchers, world over. Structural health monitoring is recommended both for vulnerable old bridges and structures as well as for new important structures. Structural health monitoring as a principle is derived from condition monitoring of machinery, where the day-to-day recordings of sound and vibration from machinery is compared and sudden changes in their features is reported for inspection and trouble-shooting. With the availability of funds for repair and retrofitting being limited, it has become imperative to rank buildings and bridges that require rehabilitation for prioritization. Visual inspection and expert judgment continues to rule the roost. Non-destructive testing techniques though have come of age and are providing excellent inputs for judgment cannot be carried out indiscriminately. They are best suited for evaluating local damage when restricted areas are investigated in detail. A few modern bridges, particularly long-span bridges have been provided with sophisticated instrumentation for health monitoring. It is necessary to identify local damages existing in normal bridges. The methodology adopted for such identification should be simple, both in terms of investigations involved and the instrumentation. Researchers have proposed various methodologies including damage identification from mode shapes, wavelet-based formulations and optimization-based damage identification and instrumentation schemes and so on. These are technically involved but may be difficult to be applied for all critical bridges, where the sheer volume of number of bridges to be investigated is enormous. Ideally, structural health monitoring has to be carried out in two stages: (a) Stage-1: Remote monitoring of global damage indicators and inference of the health of the structure. Instrumentation for this stage should be less, simple, but at critical locations to capture the global damage in a reasonable sense. (b) Stage -2: If global indicators show deviation beyond a specified threshold, then a detailed and localized instrumentation and monitoring, with controlled application of static and dynamic loads is to be carried out to infer the health of the structure and take a decision on the repair and retrofit strategies. The thesis proposes the first stage structural health monitoring methodology using natural frequencies and static deflections as damage indicators. The idea is that the stage-1 monitoring has to be done for a large number of bridges and vulnerable structures in a remote and wire-less way and a centralized control and processing unit should be able to number-crunch the in-coming data automatically and the features extracted from the data should help in determining whether any particular bridge warrants second stage detailed investigation. Hence, simple and robust strategies are required for estimating the health of the structure using some of the globally available response data. Identification methodology developed in this thesis is applicable to distributed smeared damage, which is typical of reinforced concrete structures. Simplified expressions and methodologies are proposed in the thesis and numerically and experimentally validated towards damage estimation of typical structures and elements from measured natural frequencies and static deflections. The first-order perturbation equation for a dynamical system is used to derive the relevant expressions for damage identification. The sensitivity of Eigen-value-cumvector pair to damage, modeled as reduction in flexural rigidity (EI for beams, AE for axial rods and Et 12(1 2 )3− μ for plates) is derived. The forward equation relating the changes in EI to changes in frequencies is derived for typical structural elements like simply-supported beams, plates and axial rods (along with position and extent of damage as the other controlling parameters). A distributed damage is uniquely defined with its position, extent and magnitude of EI reduction. A methodology is proposed for the inverse problem, making use of the linear relationship between the reductions in EI (in a smeared sense) to Eigen-values, such that multiple damages could be estimated using changes in natural frequencies. The methodology is applied to beams, plates and axial rods. The performance of this inverse methodology under influence of measurement errors is investigated for typical error profiles. For a discrete three dimensional structure, computationally derived sensitivity matrix is used to solve the damages in each floor levels, simulating the post-earthquake damage scenario. An artificial neural network (ANN) based Radial basis function network (RBFN) is also used to solve the multivariate interpolation problem, with appropriate training sets involving a number of pairs of damage and Eigen-value-change vectors. The acclaimed Cawley-Adams criteria (1979) states that, “the ratio of changes in natural frequencies between two modes is independent of the damage magnitude” and is governed only by the position (or location) and extent of damage. This criterion is applied to a multiple damage problem and contours with equal frequency change ratios, termed as Iso_Eigen_value_change contours are developed. Intersection of these contours for different pairs of frequencies shows the position and extent of damage. Experimental and analytical verification of damage identification methodology using Cawley-Adams criteria is successfully demonstrated. Sensitivity expressions relating the damages to changes in static deflections are derived and numerically and experimentally proved. It is seen that this process of damage identification from static deflections is prone to more errors if not cautiously exercised. Engineering and physics based intuition is adopted in setting the guidelines for efficient damage detection using static deflections. In lines of Cawley-Adams criteria for frequencies, an invariant factor based on static deflections measured at pairs of symmetrical points on a simply supported beam is developed and established. The power of the factor is such that it is governed only by the position of damage and invariant with reference to extent and magnitude of damage. Such a revelation is one step ahead of Caddemi and Morassi’s (2007) recent paper, dealing with static deflection based damage identification for concentrated damage. The invariant factor makes it an ideal candidate for base-line-free measurement, if the quality and resolution of instrumentation is good. A moving damage problem is innovatively introduced in the experiment. An attempt is made to examine wave-propagation techniques for damage identification and a guideline for modeling wave propagation as a transient dynamic problem is done. The reflected-wave response velocity (peak particle velocity) as a ratio of incident wave response is proposed as a damage indicator for an axial rod (representing an end-supported pile foundation). Suitable modifications are incorporated in the classical expressions to correct for damping and partial-enveloping of advancing wave in the damage zone. The experimental results on axial dynamic response of free-free beams suggest that vibration frequency based damage identification is a viable complementary tool to wave propagation. Wavelet-multi-resolution analysis as a feature extraction tool for damage identification is also investigated and structural slope (rotation) and curvatures are found to be the better indicators of damage coupled with wavelet analysis. An adaptive excitation scheme for maximizing the curvature at any arbitrary point of interest is also proposed. However more work is to be done to establish the efficiency of wavelets on experimentally derived parameters, where large noise-ingression may affect the analysis. The application of time-period based damage identification methodology for post-seismic damage estimation is investigated. Seismic damage is postulated by an index based on its plastic displacement excursion and the cumulative energy dissipated. Damage index is a convenient tool for decision making on immediate-occupancy, life-safety after repair and demolition of the structure. Damage sensitive soft storey structure and a weak story structure are used in the non-linear dynamic analysis and the DiPasquale-Cakmak (1987) damage index is calibrated with Park-Ang (1985) damage index. The exponent of the time-period ratio of DiPasquale-Cakmak model is modified to have consistency of damage index with Park-Ang (1985) model.

The present work focuses on the system identification method of aerodynamic parameter estimation which is used to calculate the stability and control derivatives required for aircraft flight mechanics. A new rotorcraft parameter estimation technique is proposed which uses a type of artificial neural network (ANN) called radial basis function network (RBFN). Rotorcraft parameter estimation using ANN is an unexplored research topic and the earlier works in this area have used the output error, equation error and filter error methods which are conventional parameter estimation methods. However, the conventional methods require an accurate non-linear rotorcraft simulation model which is not required by the ANN based method. The application of RBFN overcomes the drawbacks of multilayer perceptron (MLP) based delta method of parameter estimation and gives satisfactory results at either end of the ordered set of estimates. This makes the RBFN based delta method for parameter estimation suitable for rotorcraft studies, as both transition and high speed flight regime characteristics can be studied. The RBFN based delta method for parameter estimation is used for computation of aerodynamic parameters from both simulated and real time flight data. The simulated data is generated from an 8-DoF non-linear simulation model based on the Level-1 criteria of rotorcraft simulation modeling. The generated simulated data is used for computation of the quasi-steady and the time-variant stability and control parameters for different flight conditions using the RBFN based delta method. The performance of RBFN based delta method is also analyzed in the presence of state and measurement noise as well as outliers. The established methodology is then applied to compute parameters directly from real time flight test data for a BO 105 S123 helicopter obtained from DLR (German Aerospace Center). The parameters identified using the RBFN based delta method are compared with the identified values for the BO 105 helicopter from published literature which have used conventional parameter estimation techniques for parameter estimation using a 6-DoF and a 9-DoF rotorcraft simulation model. Finally, the estimated parameters are verified from the flight data generated by a frequency sweep pilot control input for assessing the predictive capability of the RBFN based delta method. Since the approach directly computes the parameters from flight data, it can be used for a reliable description of the higher frequency range, which is needed for high bandwidth flight control and in-flight simulation.

The present work focuses on the two areas of investigation: system identification of helicopter and design of controller for the helicopter. Helicopter system identification, the first subject of investigation in this thesis, can be described as the extraction of system characteristics/dynamics from measured flight test data. Wind tunnel experimental data suffers from scale effects and model deficiencies. The increasing need for accurate models for the design of high bandwidth control system for helicopters has initiated a renewed interest in and a more active use of system identification. Besides, system identification is likely to become mandatory in the future for model validation of ground based helicopter simulators. Such simulators require accurate models in order to be accepted by pilots and regulatory authorities like Federal Aviation Regulation for realistic complementary helicopter mission training. Two approaches are widely used for system identification, namely, black box and gray box approach. In the black-box approach, the relationship between input-output data is approximated using nonparametric methods such as neural networks and in such a case, internal details of the system and model structure may not be known. In the gray box approach, parameters are estimated after defining the model structure. In this thesis, both black box and gray box approaches are investigated. In the black box approach, in this thesis, a comparative study and analysis of different Recurrent Neural Networks(RNN) for the identification of helicopter dynamics using flight data is investigated. Three different RNN architectures namely, Nonlinear Auto Regressive eXogenous input(NARX) model, neural network with internal memory known as Memory Neuron Networks(MNN)and Recurrent MultiLayer perceptron (RMLP) networks are used to identify dynamics of the helicopter at various flight conditions. Based on the results, the practical utility, advantages and limitations of the three models are critically appraised and it is found that the NARX model is most suitable for the identification of helicopter dynamics. In the gray box approach, helicopter model parameters are estimated after defining the model structure. The identification process becomes more difficult as the number of degrees-of-freedom and model parameters increase. To avoid the drawbacks of conventional methods, neural network based techniques, called the delta method is investigated in this thesis. This method does not require initial estimates of the parameters and the parameters can be directly extracted from the flight data. The Radial Basis Function Network(RBFN)is used for the purpose of estimation of parameters. It is shown that RBFN is able to satisfactorily estimate stability and control derivatives using the delta method. The second area of investigation addressed in this thesis is the control of helicopter in flight. Helicopter requires use of a control system to achieve satisfactory flight. Designing a classical controller involves developing a nonlinear model of the helicopter and extracting linearized state space matrices from the nonlinear model at various flight conditions. After examining the stability characteristics of the helicopter, the desired response is obtained using a feedback control system. The scheduling of controller gains over the entire envelope is used to obtain the desired response. In the present work, a helicopter having a soft inplane four bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is considered. For this helicopter, a mathematical model and also a model based on neural network (using flight data) has been developed. As a precursor, a feed back controller, the Stability Augmentation System(SAS), is designed using linear quadratic regulator control with full state feedback and LQR with out put feedback approaches. SAS is designed to meet the handling qualities specification known as Aeronautical Design Standard ADS-33E-PRF. The control gains have been tuned with respect to forward speed and gain scheduling has been arrived at. The SAS in the longitudinal axis meets the requirement of the Level1 handling quality specifications in hover and low speed as well as for forward speed flight conditions. The SAS in the lateral axis meets the requirement of the Level2 handling quality specifications in both hover and low speed as well as for forward speed flight conditions. Such conventional design of control has served useful purposes, however, it requires considerable flight testing which is time consuming, to demonstrate and tune these control law gains. In modern helicopters, the stringent requirements and non-linear maneuvers make the controller design further complicated. Hence, new design tools have to be explored to control such helicopters. Among the many approaches in adaptive control, neural networks present a potential alternative for modeling and control of nonlinear dynamical systems due to their approximating capabilities and inherent adaptive features. Furthermore, from a practical perspective, the massive parallelism and fast adaptability of neural network implementations provide more incentive for further investigation in problems involving dynamical systems with unknown non-linearity. Therefore, adaptive control approach based on neural networks is proposed in this thesis. A neural network based Feedback Error Neural adaptive Controller(FENC) is designed for a helicopter. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non-linearity and parameter un-certainties. Nonlinear Auto Regressive eXogenous input(NARX)neural network architecture is used to approximate the control law and the controller network parameters are adapted using updated rules Lyapunov synthesis. An offline (finite time interval)and on-line adaptation strategy is used to approximate system uncertainties. The results are validated using simulation studies on helicopter undergoing an agile maneuver. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications. Even though the tracking error is less in FENC scheme, the control effort required to follow the command is very high. To overcome these problems, a Direct Adaptive Neural Control(DANC)scheme to track the rate command signal is presented. The neural controller is designed to track rate command signal generated using the reference model. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using back propagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval)network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller is compared with feedback error learning neural controller. The performance of the controller has been validated at various flight conditions. The theoretical results are validated using simulation studies based on a nonlinear six degree-of-freedom helicopter undergoing an agile maneuver. Realistic gust and sensor noise are added to the system to study the disturbance rejection properties of the neural controllers. To investigate the on-line learning ability of the proposed neural controller, different fault scenarios representing large model error and control surface loss are considered. The performances of the proposed DANC scheme is compared with the FENC scheme. The study shows that the neuro-controller meets the requirements of ADS-33 handling quality specifications.