Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility. Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance. Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results. From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained. The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.

Methods are presented for parameter identification of an annular gas seal on a flexiblerotor test rig. Dynamic loads are applied by magnetic bearings (MBs) that support the rotor. MB forces are measured using fiber-optic strain gauges that are bonded to the poles of the MBs. In addition to force and position measurements, a finite element (FE) rotor model is required for the identification algorithms. The FE rotor model matches free-free characteristics of the test rotor. The addition of smooth air seals to the system introduces stiffness and damping terms for identification that are representative of reaction forces in turbomachines. Tests are performed to experimentally determine seal stiffness and damping coefficients for different running speeds and preswirl conditions. Stiffness and damping coefficients are determined using a frequency domain identification method. This method uses an iterative approach to minimize error between theoretical and experimental transfer functions. Several time domain approaches are also considered; however, these approaches do not produce valid identification results. Stiffness coefficients are measured using static test results and an MB current and position based model. Test results produce seal coefficients with low uncertainties for the frequency domain identification method. Static test uncertainties are an order of magnitude larger, and time domain attempts fail to produce sealIn addition to the primary identification research, an investigation of the relationships between MB force, strain, and magnetic field is conducted. The magnetic field of an MB is modeled using commercial FE software. The magnetic field model is used to predict strain measurements for quasi-static test conditions. The strain predictions are compared with experimental strain measurements. Strain predictions agree with experimental measurements, although strain is typically over-predicted. coefficient measurements.

O problema de identificação de parâmetros consiste em se determinar parâmetros que minimizem a diferença entre valores medidos e calculados de determinadas grandezas. Certamente, essa identificação é realizada para parâmetros que apresentam razoável grau de incerteza nos seus valores. Neste trabalho apresentam-se os principais conceitos e fundamentos matemáticos envolvidos no assunto, desenvolve-se um procedimento de identificação de parâmetros com base matemática sólida e aplica-se esse procedimento em problemas de interesse prático da engenharia. São estudados o Túnel de Hudvudsta e a barragem de Machadinho, nos quais são identificados parâmetros relacionados com as ações ou com as propriedades físicas dos materiais, considerando modelos hierárquicos para representar as estruturas. Utilizam-se os principais critérios de identificação para a definição das funções objetivo e métodos do tipo Newton para a minimização dessas funções. / The parameter identification problem consists of determining the values of the parameters that minimize the difference between measured and calculated values of some variables. Indeed, this identification is performed to parameters that present some uncertainty on their values. In this work the main mathematical concepts and fundaments related to back analysis are presented. A procedure for parameter identification with a consistent mathematical basis is developed and applied in practical engineering problems. The Hudvudsta tunnel and the Machadinho dam are studied to identify parameters related to loads or material physical properties by using hierarchical models to represent the structures. The objetive functions are defined with the main identification criteria and minimized with Newton´s methods.

Fractional order calculus (FOC) has wide applications in modeling natural behavior of systems related to different areas of engineering including bioengineering, viscoelasticity, electronics, robotics, control theory and signal processing. This thesis aims at modeling a lossy transmission line using fractional order calculus and identifying its parameters. A lossy transmission line is considered where its behavior is modeled by a fractional order transfer function. A semi-infinite lossy transmission line is presented with its distributed parameters R, L, C and ordinary AC circuit theory is applied to find the partial differential equations. Furthermore, applying boundary conditions and the Laplace transformation a generalized fractional order transfer function of the lossy transmission line is obtained. A finite length lossy transmission line terminated with arbitrary load is also considered and its fractional order transfer function has been derived. Next, the frequency responses of lossy transmission lines from their fractional order transfer functions are also derived. Simulation results are presented to validate the frequency responses. Based on the simulation results it can be concluded that the derived fractional order transmission line model is capable of capturing the phenomenon of a distributed parameter transmission line. The achievement of modeling a highly accurate transmission line requires that a realistic account needs to be taken of its parameters. Therefore, a parameter identification technique to identify the parameters of the fractional order lossy transmission line is introduced. Finally, a few open problems are listed as the future research directions. / Controls

No description available.

O problema de identificação de parâmetros consiste em se determinar parâmetros que minimizem a diferença entre valores medidos e calculados de determinadas grandezas. Certamente, essa identificação é realizada para parâmetros que apresentam razoável grau de incerteza nos seus valores. Neste trabalho apresentam-se os principais conceitos e fundamentos matemáticos envolvidos no assunto, desenvolve-se um procedimento de identificação de parâmetros com base matemática sólida e aplica-se esse procedimento em problemas de interesse prático da engenharia. São estudados o Túnel de Hudvudsta e a barragem de Machadinho, nos quais são identificados parâmetros relacionados com as ações ou com as propriedades físicas dos materiais, considerando modelos hierárquicos para representar as estruturas. Utilizam-se os principais critérios de identificação para a definição das funções objetivo e métodos do tipo Newton para a minimização dessas funções. / The parameter identification problem consists of determining the values of the parameters that minimize the difference between measured and calculated values of some variables. Indeed, this identification is performed to parameters that present some uncertainty on their values. In this work the main mathematical concepts and fundaments related to back analysis are presented. A procedure for parameter identification with a consistent mathematical basis is developed and applied in practical engineering problems. The Hudvudsta tunnel and the Machadinho dam are studied to identify parameters related to loads or material physical properties by using hierarchical models to represent the structures. The objetive functions are defined with the main identification criteria and minimized with Newton´s methods.

Designing high performance control of rotorcraft unmanned aerial vehicle (UAV) requires a mathematical model that describes the dynamics of the vehicle. The model is derived from first principle modelling, such as rigid-body dynamics, actuator dynamics and etc. It is found that simplified decoupled model of RUAV has slightly better data fitting compared with the complex model for helicopter attitude dynamics in hover or near hover flight condition. In addition, the simplified modelling approach has made the analysis of system dynamics easy. System identification method is applied to identify the unknown intrinsic parameters in the nominal model, where manual piloted flight experiment is carried out and input-output data about a nominal operating region is recorded for parameters identification process. Integral-based parameter identification algorithm is then used to identify model parameters that give the best matching between the simulation and measured output response. The results obtained show that the dominant dynamics is captured. The advantages of using integral-based method include the fast computation time, insensitive to initial parameter value and fast convergence rate in comparison with other contemporary system identification methods such as prediction error method (PEM), maximum likelihood method, equation error method and output error method. Besides, the integral-based parameter identification method can be readily extended to tackle slow time-varying model parameters and fast varying disturbances. The model prediction is found to be improved significantly when the iterative integral-based parameter identification is employed and thus further validates the minimal modelling approach. From the literature review, many control schemes have been designed and validated in simulation. However, few of them has really been implemented in real flight as well as under windy and severe conditions, where unpredictable large system parameters variations and unexpected disturbances are present. Therefore, the emphasis on this part will be on the control design that would have satisfactory reference sequence tracking or regulation capability in the presence of unmodelled dynamics and external disturbances. Generalised Predictive Controller (GPC) is particularly considered as the helicopter attitude dynamics control due to its insensitivity with respect to model mismatch and its capability to address the control problem of nominal model with deadtime. The robustness analysis shows that the robustness of the basic GPC is significantly improved using the Smith Predictor (SP) in place of optimal predictor in basic GPC. The effectiveness of the proposed robust GPC was well proven with the control of helicopter heading on the test rig in terms of the reference sequence tracking performance and the input disturbance rejection capability. The second motivation is the investigation of adaptive GPC from the perspective of performance improvements for the robust GPC. The promising experimental results prove the feasibility of the adaptive GPC controller, and especially evident when the underlying robust GPC is tuned with low robustness and legitimates the use of simplified model. Another approach of robust model predictive control is considered where disturbance is identified in real‐time using an iterative integral‐based method.

This thesis focusses on the development and testing of optimization methods for parameter identification in cardiac electrophysiology models. Cardiac electrophysiology models are systems of differential equations representing the evolution of the trans-membrane potential of cardiac cells. The Mitchell-Schaeffer model is chosen for this thesis. The parameters included in the Mitchell-Schaeffer model are optimally adjusted so that the solution of the model has desired properties. Two optimization problems are formulated using least-square functions to identify parameters that match phase durations and parameters that fit entire potential recordings of swine heart tissue acquired via optical imaging techniques at different stimulation frequencies. The non-differentiable optimization methods (Compass Search and three other variants) are applied to solving both optimization problems for two reasons; First, the methods are studied to evaluate performance and second, the optimization process is evaluated to confirm its ability to identify parameters for the Mitchell-Schaeffer model.

Precise positioning is crucial for the success of navigation of underwater vehicles. At present, different instruments and methods are available for underwater positioning but few of them are reliable for three-dimensional position sensing of underwater vehicles. Long baseline (LBL) positioning is the standard method for three-dimensional underwater navigation. However, the accuracy of LBL positioning suffers from its own drawback of relatively low update rates. To improve the accuracy in positioning an underwater vehicle, integration of additional sensing measurements in a LBL navigation system is necessary. In this study, numerical simulation and experiment are conducted to investigate the effect of interrogate rate on the accuracy of LBL positioning. Numerical and experimental results show that the longer the interrogate rate, the greater the LBL positioning error. In addition, no reply from a transponder to transceiver interrogation is another major error source in LBL positioning. The experimental result also shows that the accuracy of LBL positioning can be significantly improved by the integration of velocity sensing. Therefore, based on Kalman filter, this study integrates a LBL system with vehicle dynamic model to improve the accuracy of positioning an underwater vehicle. For conducting the positioning experiments, a remotely operated vehicle (ROV) with dedicated Graphic User Interface (GUI) is designed, constructed, and tested. To have a precise motion simulation of ROV, a nonlinear dynamic model of ROV with six degrees of freedom (DOF) is used and its hydrodynamic parameters are identified. Finally, the positioning experiment is run by maneuvering the ROV to move along an ¡§S¡¨ trajectory, and Kalman filter is adopted to propagate the error covariance, to update the measurement errors, and to correct the state equation when the measurements of range, depth, and thruster command are available. The experimental result demonstrates the effectiveness of the integrated LBL system with the ROV dynamic model on the improvement of accuracy of positioning an underwater vehicle.

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