[pt] O presente trabalho é inteiramente baseado na teoria de modelagem de series temporais, proposta por BOX & JENKINS em Time Series Analysis: Forecasting and Control. (1970). E dado ênfase ao problema de transformação e estimação de parâmetros, com vistas a previsão de series temporais. O trabalho apresenta um conjunto de programas para aplicação das técnicas desenvolvidas. Em particular é tratado o caso de uma serie hidrológica de vazões do Rio Grande, Brasil, nos últimos 40 anos. / [en] The present paper is totally based upon the theory of time series modeling, presented by BOX & JENKINS in Time Series Analysis: Forecasting and Control. (1970). Enphasis is given to the problem of tranformation and estimation of parameters, with the objective to forecast time series. This paper presents a set of programs for pratical applications of the techniques developped. The case of a hidrologic time series of inflows of Rio Grande, Brasil is included.

Earth radiation budget instruments are devices designed to study global climate change. These instruments use telescopes embarked on low-earth-orbit satellites to measure Earth emitted and reflected solar radiation. Radiation is sensed as temperature changes caused by radiation absorbed during scans of the earth on a delicate gold-black coated detector. This work is part of a larger effort to develop an end-to-end dynamic electro-thermal model, based on first-principles, for the next generation of earth radiation budget instruments, the Radiation Budget Instrument (RBI). A primary objective of this effort is to develop a numerical model of the detector to be used on RBI. Specifically, the sensor model converts radiation arriving at the detector, collimated and focused through telescopes, into sensible heat; thereby producing a voltage. A mathematical model characterizing this sensor is developed. Using a MATLAB algorithm, an implicit finite-volume scheme is implemented to determine the model solution. Model parameters are tuned to replicate experimental data using a robust parameter estimation scheme. With these model parameters defined, the electro-thermal sensor model can be used, in conjunction with the remaining components of the end-to-end model, to provide insight for future interpretation of data produced by the RBI. / Master of Science / Earth radiation budget instruments are devices designed to study global climate change. These instruments use telescopes embarked on low-earth-orbit satellites to measure radiation exiting the atmosphere of the Earth. As the atmospheric science community works to design and develop the next generation of these instruments, a need for a model capable of simulating operating performance has arisen. This work is part of a larger effort to develop a complete model for the next generation of Earth radiation budget instruments, the Radiation Budget Instrument (RBI). A primary objective of this effort is to develop a model of a detector to be used on the RBI. The modelling techniques used to characterize the detector are presented in this work. Once the model has been developed, optimal model parameters are determined to tune the model. With these model parameters defined, the detector model can be used, in conjunction with the remaining pieces of the overall end-to-end model, to provide insight for future interpretation of data produced by the RBI.

This thesis describes the development of a nonlinear flight dynamics model for a small, fixed-wing unmanned aerial vehicle (UAV). Models developed for UAVs can be used for many applications including risk analysis, controls system design and flight simulators. Several challenges exist for system identification of small, low-cost aircraft including an increased sensitivity to atmospheric disturbances and decreased data quality from a cost-appropriate instrumentation system. These challenges result in difficulties in development of the model structure and parameter estimation. The small size may also limit the scope of flight test experiments and the consequent information content of the data from which the model is developed. Methods are presented to improve the accuracy of system identification which include data selection, data conditioning, incorporation of information from computational aerodynamics and synthesis of information from different flight test maneuvers. The final parameter estimation and uncertainty analysis was developed from the time domain formulation of the output-error method using the fully nonlinear aircraft equations of motion and a nonlinear aerodynamic model structure. The methods discussed increased the accuracy of parameter estimates and lowered the uncertainty in estimates compared to standard procedures for parameter estimation from flight test data. The significant contributions of this thesis are a detailed explanation of the entire system identification process tailored to the needs of a small UAV and incorporation of unique procedures to enhance identification results. This work may be used as a guide and list of recommendations for future system identification efforts of small, low-cost, minimally instrumented, fixed-wing UAVs. / MS / This thesis describes identification of a series of equations to model the flight motion of a small unmanned airplane. Model development for small unmanned aerial vehicles (UAVs) is a challenging process because they are significantly affected by small amounts of wind and they usually contain inexpensive, lower quality sensors. This results in lower quality data measured from flying a small UAV, which is subsequently used in the process to develop a model for the aircraft. In this work, techniques are discussed to improve estimation of model parameters and increase confidence in the validity of the final model. The significant contributions of this thesis are a comprehensive explanation of the model development process specific to a small UAV and implementation of unique procedures to enhance the resulting model. This work as a whole may be used as a guide and list of recommendations for future model development efforts of small, low-cost, unmanned aircraft.

A spacecraft formation possesses several benefits over a single-satellite mission. However, launching a fleet of satellites is a high-cost, high-risk venture. One way to mitigate much of this risk is to demonstrate hardware and algorithm performance in groundbased testbeds. It is typically difficult to experimentally replicate satellite dynamics in an Earth-bound laboratory because of the influences of gravity and friction. An air bearing provides a very low-torque environment for experimentation, thereby recapturing the freedom of the space environment as effectively as possible. Depending upon con- figuration, air-bearing systems provide some combination of translational and rotational freedom; the three degrees of rotational freedom provided by a spherical air bearing are ideal for investigation of spacecraft attitude dynamics and control problems. An interest in experimental demonstration of formation flying led directly to the development of the Distributed Spacecraft Attitude Control System Simulator (DSACSS). The DSACSS is a unique facility, as it uses two air-bearing platforms working in concert. Thus DSACSS provides a pair of "spacecraft" three degrees of attitude freedom each. Through use of the DSACSS we are able to replicate the relative attitude dynamics between nodes of a formation such as might be required for co-observation of a terrestrial target. Many dissertations present a new mathematical technique or prove a new theory. This dissertation presents the design and development of a new experimental system. Although the DSACSS is not yet fully operational, a great deal of work has gone into its development thus far. This work has ranged from configuration design to nonlinear analysis to structural and electrical manufacturing. In this dissertation we focus on the development of the attitude determination subsystem. This work includes development of the equations of motion and analysis of the sensor suite dynamics. We develop nonlinear filtering techniques for data fusion and attitude estimation, and extend this problem to include estimation of the mass properties of the system. We include recommendations for system modifications and improvements. / Ph. D.

The thesis presents algorithms based on a linear algebraic solution for the identification of the parameters of the FIR system using only higher order statistics when only the output of the system corrupted by additive Gaussian noise is observed. All the traditional parametric methods of estimating the parameters of the system have been based on the 2nd order statistics of the output of the system. These methods suffer from the deficiency that they do not preserve the phase response of the system and hence cannot identify non-minimum phase systems. To circumvent this problem, higher order statistics which preserve the phase characteristics of a process and hence are able to identify a non-minimum phase system and also are insensitive to additive Gaussian noise have been used in recent years. Existing algorithms for the identification of the FIR parameters based on the higher order cumulants use the autocorrelation sequence as well and give erroneous results in the presence of additive colored Gaussian noise. This problem can be overcome by obtaining algorithms which do not utilize the 2nd order statistics. An existing relationship between the 2nd order and any Ith order cumulants is generalized to a relationship between any two arbitrary k, Ith order cumulants. This new relationship is used to obtain new algorithms for FIR system identification which use only cumulants of order > 2 and with no other restriction than the Gaussian nature of the additive noise sequence. Simulation studies are presented to demonstrate the failure of the existing algorithms when the imposed constraints on the 2nd order statistics of the additive noise are violated while the proposed algorithms perform very well and give consistent results. Recently, a new algebraic approach for parameter estimation method denoted the Linear Combination of Slices (LCS) method was proposed and was based on expressing the FIR parameters as a linear combination of the cumulant slices. The rank deficient cumulant matrix S formed in the LCS method can be expressed as a product of matrices which have a certain structure. The orthogonality property of the subspace orthogonal to S and the range space of S has been exploited to obtain a new class of algorithms for the estimation of the parameters of a FIR system. Numerical simulation studies have been carried out to demonstrate the good behaviour of the proposed algorithms. Analytical expressions for the covariance of the estimates of the FIR parameters of the different algorithms presented in the thesis have been obtained and numerical comparison has been done for specific cases. Numerical examples to demonstrate the application of the proposed algorithms for channel equalization in data communication and as an initial solution to the cumulant matching nonlinear optimization methods have been presented.

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.

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho, abordamos um problema de otimização de parâmetros da biofísica em que o objetivo é a obtenção da taxa média de concentração de substrato no fígado. Este problema é altamente não-linear, multimodal e com função-objetivo não-diferenciável. Resolvemos o mesmo através de métodos de otimização da literatura e introduzimos três métodos de otimização. Os métodos introduzidos neste trabalho são baseados na hibridização de um método estocástico, que explora o espaço de busca, com um método determinístico de busca direta, que faz uma busca local mais refinada nas áreas mais promissoras deste espaço. Os novos métodos são comparados aos da literatura e é verificado que o desempenho dos primeiros é superior. / In this work, we attack a parameter optimization problem from Biophysics, where the aim is to obtain the substrate concentration rate of a liver. This problem is highly non-linear, multimodal, and with non-differentiable objective-function. We solve it using optimization methods from the literature and three methods introduced in this work. The latter methods are based on the hybridization of a stochastic technique which explores the search space, with a direct search deterministic technique which exploits the most promising areas. Our results show that the new optimization methods perform better than those from the literature.

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho, abordamos um problema de otimização de parâmetros da biofísica em que o objetivo é a obtenção da taxa média de concentração de substrato no fígado. Este problema é altamente não-linear, multimodal e com função-objetivo não-diferenciável. Resolvemos o mesmo através de métodos de otimização da literatura e introduzimos três métodos de otimização. Os métodos introduzidos neste trabalho são baseados na hibridização de um método estocástico, que explora o espaço de busca, com um método determinístico de busca direta, que faz uma busca local mais refinada nas áreas mais promissoras deste espaço. Os novos métodos são comparados aos da literatura e é verificado que o desempenho dos primeiros é superior. / In this work, we attack a parameter optimization problem from Biophysics, where the aim is to obtain the substrate concentration rate of a liver. This problem is highly non-linear, multimodal, and with non-differentiable objective-function. We solve it using optimization methods from the literature and three methods introduced in this work. The latter methods are based on the hybridization of a stochastic technique which explores the search space, with a direct search deterministic technique which exploits the most promising areas. Our results show that the new optimization methods perform better than those from the literature.

Real-Time Rollover prevention of Unmanned Ground Vehicle (UGV) is very paramount to its reliability and survivability mostly when operating on unknown and rough terrains like mines or other planets.Therefore this research presents the method of real-time rollover prevention of UGVs making use of Adaptive control techniques based on Recursive least Squares (RLS) estimation of unknown parameters, in order to enable the UGVs to adapt to unknown hush terrains thereby increasing their reliability and survivability. The adaptation is achieved by using indirect adaptive control technique where the controller parameters are computed in real time based on the online estimation of the plant’s (UGV) parameters (Rollover index and Roll Angle) and desired UGV’s performance in order to appropriately adjust the UGV speed and suspension actuators to counter-act the vehicle rollover. A great challenge of indirect adaptive control system is online parameter identification, where in this case the RLS based estimator is used to estimate the vehicles rollover index and Roll Angle from lateral acceleration measurements and height of the centre of gravity of the UGV. RLS is suitable for online parameter identification due to its nature of updating parameter estimate at each sample time. The performance of the adaptive control algorithms and techniques is evaluated using Matlab Simulink® system model with the UGV Model built using SimMechanics physical modelling platform and the whole system runs within Simulink environment to emulate real world application. The simulation results of the proposed adaptive control algorithm based on RLS estimation, show that the adaptive control algorithm does prevent or minimize the likely hood of vehicle rollover in real time. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Trial and error and the use of highly time-consuming methods are often necessary for modeling, simulating and characterizing nonlinear dynamical systems. However, for the rather common special case when a nonlinear system has linear relations between many of its degrees of freedom there are particularly interesting opportunities for more efficient approaches. The aim of this thesis is to develop and validate new efficient methods for the theoretical and experimental study of mechanical systems that include significant zero-memory or hysteretic nonlinearities related to only small parts of the whole system. The basic idea is to take advantage of the fact that most of the system is linear and to use much of the linear theories behind forced response simulations. This is made possible by modeling the nonlinearities as external forces acting on the underlying linear system. The result is very fast simulation routines where the model is based on the residues and poles of the underlying linear system. These residues and poles can be obtained analytically, from finite element models or from experimental measurements, making these forced response routines very versatile. Using this approach, a complete nonlinear model contains both linear and nonlinear parts. Thus, it is also important to have robust and accurate methods for estimating both the linear and nonlinear system parameters from experimental data. The results of this work include robust and user-friendly routines based on sinusoidal and random noise excitation signals for characterization and description of nonlinearities from experimental measurements. These routines are used to create models of the studied systems. When combined with efficient simulation routines, complete tools are created which are both versatile and computationally inexpensive. The developed methods have been tested both by simulations and with experimental test rigs with promising results. This indicates that they are useful in practice and can provide a basis for future research and development of methods capable of handling more complex nonlinear systems.