Mohamed N. Nounou

Texas A&M University at Qatar, Ad Dawḩah, Ad Dawḩah, Qatar

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Publications (95)52.03 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new technique to design fixed-structure controllers for linear unknown systems using a set of measurements. In model-based approaches, the measured data are used to identify a model of the plant for which a suitable controller can be designed. Due to the fact that real processes cannot be described perfectly by mathematical models, designing controllers using such models to guarantee some desired closed-loop performance is a challenging task. Hence, a possible alternative to model-based methods is to directly utilize the measured data in the design process. We propose an approach to designing structured controllers using a set of closed-loop frequency-domain data. The principle of such an approach is based on computing the parameters of a fixed-order controller for which the closed-loop frequency response fits a desired frequency response that describes some desired performance indices. This problem is formulated as an error minimization problem, which can be solved to find suitable values of the controller parameters. The main feature of the proposed control methodology is that it can be applied to stable and unstable plants. Additionally, the design process depends on a pre-selected controller structure, which allows for the selection of low-order controllers. An application of the proposed method to a DC servomotor system is presented to experimentally validate and demonstrate its efficacy.
    Asian Journal of Control 11/2014; · 1.41 Impact Factor
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    ABSTRACT: This paper presents a new model-free technique to design fixed-structure controllers for linear unknown systems. In the current control design approaches, measured data are used to first identify a model of the plant, then a controller is designed based on the identified model. Due to errors associated with the identification process, degradation in the controller performance is expected. Hence, we use the measured data to directly design the controller without the need for model identification. Our objective here is to design measurement-based controllers for stable and unstable systems, even when the closed-loop architecture is unknown. This proposed method can be very useful for many industrial applications. The proposed control methodology is a reference model design approach which aims at finding suitable parameter values of a fixed-order controller so that the closed-loop frequency response matches a desired frequency response. This reference model design problem is formulated as a nonlinear programming problem using the concept of bounded error, which can then be solved to find suitable values of the controller parameters. In addition to the well-known advantages of data-based control methods, the main features of our proposed approach are: (1) the error is guaranteed to be bounded, (2) it enables us to avoid issues related to the use of minimization methods, (3) it can be applied to stable and unstable plants and does not require any knowledge about the closed-loop architecture, and (4) the controller structure can be selected a priori, which means that low-order controllers can be designed. The proposed technique is experimentally validated through a real position control problem of a DC servomotor, where the results demonstrate the efficacy of the proposed method.
    Automatica 08/2014; · 2.92 Impact Factor
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    ABSTRACT: This paper addresses the problem of nonlinear time-varying state and parameter estimation of induction machines (IMs) on the basis of a third-order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non-Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results. Copyright © 2014 John Wiley & Sons, Ltd.
    International Journal of Adaptive Control and Signal Processing 08/2014; · 1.22 Impact Factor
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    ABSTRACT: This paper presents an approach for designing gain-scheduled controllers for unknown systems using a set of measured data. In most control approaches, controllers are designed using a mathematical model, which is often obtained on the basis of some simplifying assumptions. Thus, controllers designed through model-based methods may result in degradation of the desired closed-loop performance due to complex dynamics. Hence, the proposed approach is motivated by the fact that: 1) errors associated with the modeling process are avoided since no mathematical model is required for the controller design, 2) the designed adaptive controllers are able to ensure desired performance specifications for the plant operated not only at a given operating point but over a range of operating conditions, and 3) the controller structure can be selected a priori. A simulation example to control a water heating system is presented to validate the proposed method.
    2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP); 05/2014
  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
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    ABSTRACT: This paper addresses the problem of states and parameters estimation for a continuously stirred tank reactor using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Particle Filter (PF), and the developed improved particle filter (IPF). Unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The proposal sampling distribution is obtained by minimizing the Kullback-Leibler divergence (KLD) distance. The simulation results show that the new improved particle filter superiors to the standard particle filter. In addition, IPF can still provide both convergence as well as accuracy related advantages over other estimation methods.
    Multi-Conference on Systems, Signals & Devices (SSD), 2014 11th International; 03/2014
  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
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    ABSTRACT: In this paper, we develop an improved particle filtering algorithm for nonlinear states estimation. In case of standard particle filter, the latest observation is not considered for the evaluation of the weights of the particles as the importance function is taken to be equal to the prior density function. This choice of importance sampling function simplifies the computation but can cause filtering divergence. In cases where the likelihood function is too narrow as compared to the prior function, very few particles will have significant weights. Hence a better proposal distribution that takes the latest observation into account is desired. The proposed algorithm consists of a particle filter based on minimizing the Kullback-Leibler divergence distance to generate the optimal importance proposal distribution. The proposed algorithm allows the particle filter to incorporate the latest observations into a prior updating scheme using the estimator of the posterior distribution that matches the true posterior more closely. In the comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The simulation results show that the proposed algorithm, outperforms the standard particle filter, the unscented Kalman filter, and the extended Kalman filter algorithms.
    Multi-Conference on Systems, Signals & Devices (SSD); 03/2014
  • Majdi Mansouri, hazem Nounou, Mohamed Nounou
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    ABSTRACT: Induction machine is highly nonlinear model with states that change with operating point and temperature. In these cases, estimating these variables from other easily obtained measurements can be extremely useful. This paper deals with the problem of state estimation of induction machine on the basis of a third-order electrical model using Bayesian methods. The performances of Bayesian estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the particle filter (PF), and the developed improved particle filter (IPF). The estimation results, which are validated using simulations, show that IPF provides improved estimation performance over PF, even with abrupt changes in estimated states, and both of them can provide improved accuracy over UKF and EKF. These advantages of the IPF are due to the fact that it uses a better proposal distribution that takes the latest observation into account.
    IEEE Multi-Conference on Systems, Signals & Devices (SSD), 2014 11th International, Castelldefels-Barcelona, Spain; 02/2014
  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
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    ABSTRACT: In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.
    Journal of Signal Processing Systems 02/2014; · 0.55 Impact Factor
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    ABSTRACT: Biological pathways can be modeled as a nonlinear system described by a set of nonlinear ordinary differential equations (ODEs). A central challenge in computational modeling of biological systems is the determination of the model parameters. In such cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. For example, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks, which can be used to design intervention strategies to cure major diseases and to better understand the behavior of biological systems. Unfortunately, biological measurements are usually highly affected by errors that hide the important characteristics in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. This paper addresses the problem of state and parameter estimation of biological phenomena modeled by S-systems using Bayesian approaches, where the nonlinear observed system is assumed to progress according to a probabilistic state space model. The performances of various conventional and state-of-the-art state estimation techniques are compared. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the developed improved particle filter (IPF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the enzyme CadA, the transport protein CadB, the regulatory protein CadC and lysine Lys for a model of the Cad System in E. coli (CSEC)) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the IPF provides a significant improvement over PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the IPF can still provide both convergence as well as accuracy related advantages over other estimation methods.
    Digital Signal Processing 01/2014; · 1.92 Impact Factor
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    ABSTRACT: A central challenge in computational modeling of biological systems is the determination of the model parameters. In such cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. For example, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks, which can be used to design intervention strategies to cure major diseases and to better understand the behavior of biological systems. Unfortunately, biological measurements are usually highly infected by errors that hide the important characteristics in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. This paper addresses the problem of state and parameter estimation of biological phenomena modeled by S-systems using Bayesian approaches, where the nonlinear observed system is assumed to progress according to a probabilistic state space model. The performances of various conventional and state-of-the-art state estimation techniques are compared. These techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and the developed variational Bayesian filter (VBF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the enzyme CadA, the model cadBA, the cadaverine Cadav and the lysine Lys for a model of the Cad System in E. coli (CSEC)) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the VBF provides a relative improvement over PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VBF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VBF can still provide both convergence as well as accuracy related advantages over other estimation methods.
    Mathematical biosciences 01/2014; · 1.30 Impact Factor
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    ABSTRACT: This paper presents an approach to design a measurement-based controller for induction machines. The proposed control approach is motivated by the fact that developing an appropriate mechanical model of such induction machines is a challenging task. Since our proposed control methodology is only on the basis of measured data, the controller design does not require any information about the model of the mechanical part. The control of motor drive is often based on sensorless field-oriented control techniques because of their advantages such as noise and cost reductions and high reliability. Hence, we assume here that measurements used for the controller design are collected using an estimator based on the electrical equations of the induction machine. A practical application to control the speed of an induction machine is presented to validate and demonstrate the efficiency of the proposed method.
    Emerging and Selected Topics in Power Electronics, IEEE Journal of. 01/2014; 2(2):308-318.
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    ABSTRACT: The objective of this paper is to present a measurement-based control-design approach for single-input single-output linear systems with guaranteed bounded error. A wide range of control-design approaches available in the literature are based on parametric models. These models can be obtained analytically using physical laws or via system identification using a set of measured data. However, due to the complex properties of real systems, an identified model is only an approximation of the plant based on simplifying assumptions. Thus, the controller designed based on a simplified model can seriously degrade the closed-loop performance of the system. In this paper, an alternative approach is proposed to develop fixed-order controllers based on measured data without the need for model identification. The proposed control technique is based on computing a suitable set of fixed-order controller parameters for which the closed-loop frequency response fits a desired frequency response that meets the desired closed-loop performance specifications. The control-design problem is formulated as a nonlinear programming problem using the concept of bounded error. The main advantages of our proposed approach are: (1) it guarantees that the error between the computed and the desired frequency responses is less than a small value; (2) the difficulty of finding the globally optimal solution in the error minimisation problem is avoided; (3) the controller can be designed without the use of any analytical model to avoid errors associated with the identification process; and (4) low-order controllers can be designed by selecting a fixed low-order controller structure. To experimentally validate and illustrate the efficacy of the proposed approach, proportional-integral measurement-based controllers are designed for a DC (direct current) servomotor.
    International Journal of Control 09/2013; 9(9). · 1.01 Impact Factor
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    ABSTRACT: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF) - gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared to FastNCA and the Non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces Cerevisiae data and Eschericia coli data and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable to that of FastNCA, and is hundreds of times faster than NI-NCA. The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA. serpedin@ece.tamu.edu.
    Bioinformatics 08/2013; · 5.47 Impact Factor
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    ABSTRACT: SUMMARY This paper proposes a new measurement-based approach that can solve synthesis problems in unknown linear circuits. The method makes use of a small number of measurements to determine the functional dependency of any circuit signal or variable on any set of design variables. Once the functional dependency is obtained, the design requirements can be applied to find the design parameter values. The results are described for linear direct current and alternating current circuits. Copyright © 2013 John Wiley & Sons, Ltd.
    International Journal of Circuit Theory and Applications 07/2013; · 1.29 Impact Factor
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    ABSTRACT: This paper presents a new control design approach for unknown SISO systems by using measurements. In control approaches existing in the literature, controllers are usually designed on the basis of mathematical models obtained by either using physical laws or via identification system using a set of measured data. However, due to the complex dynamics of real systems, such parametric models are only an approximation obtained after some simplifying assumptions. Therefore, the design of controllers based on a simplified model leads to a degradation in the expected performance for the closed-loop system. Our proposed approach is based on measurements to directly design controllers without going through the use of mathematical models. The principle of the proposed control methodology is to design fixed-structure controllers for which the error modulus between the closed-loop frequency response and a desired frequency response is bounded by given quantity. This problem is formulated as a nonlinear programming problem based on inequality constraints. The main advantage of our proposed approach is that the controller design is based only on a set of measurements, which allows to avoid errors associated with the identification process. Moreover, with such a proposed control method, it is guaranteed that the error between the computed and desired closed-loop frequency responses is less than a small quantity. Another feature of the proposed technique is that the structure of the controller can be selected a priori, which allows to design low-order controllers. A simulation application to measurement-based controller design for DC servomotors is presented to validate and illustrate the efficacy of the proposed approach.
    American Control Conference (ACC), 2013; 06/2013
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    ABSTRACT: This paper presents a new measurement-based technique to design fixed-structure controllers for unknown SISO systems. Most control design approaches existing in the literature are based on parametric models. Such models can be obtained using physical laws or via system identification using a set of measured data. Model-based control approaches have proven successful only when mathematical models used in the controller design provide a perfect description of the physical system behavior. In many practical situations, it has been shown that the derivation of models using either physical laws or system identification is usually based on simplifying assumptions due to complex dynamics characterizing real systems. Hence, the use of such simplified models in the design process may result in closed-loop performance deterioration. In this paper, an alternative approach is proposed to design controllers based on measured data without the need for model identification. Our proposed technique is novel in the sense that it can be applied even when the controller placement within a complex control system configuration is unknown. The principle of the proposed control methodology is to find the controller parameters so that the closed-loop frequency response is close to a desired frequency response. This problem is formulated as an error minimization problem. The main advantages of our proposed approach are: 1) its applicability to any complex and unknown control system configuration and without the use of any mathematical model, and 2) the design process is based on a pre-selected controller structure, which allows for the selection of low-order controllers. For simulation purposes, a PID measurement-based controller is designed to illustrate the feasibility and the efficacy of the proposed technique.
    American Control Conference (ACC), 2013; 06/2013
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    ABSTRACT: This work deals with the design of robust fixed-structure controllers for uncertain systems based on a finite set of measured data. This set of measurements is given in the frequency domain. In the current control design approaches, controllers are usually designed based on plant models obtained on the basis of measured data. However, due to various forms of uncertainties such as: plant parameter variations, external disturbances, measurement noise, etc, such models are unable to perfectly describe the behavior of the physical system. Hence, degradation in the controller performance is expected due to such uncertainties and errors associated with the identification process. For that, we propose a new control technique that uses the uncertain measurements to directly design robust controllers, for a class of uncertainties, without going through the use of identification process. In such a proposed design method, interval techniques are introduced to bound plant uncertainties. Its main principle is to find the set of admissible values of the controller parameters so that the family of all possible frequency responses of the closed-loop system lies between an upper and lower bounds of a desired frequency response. This problem is formulated as a nonlinear programming problem which can easily be solved to characterize the solution set of the controller parameters. The main feature of our proposed approach is that it enables to design robust fixed-structure controllers by taking into account the plant uncertainties. Moreover, since no mathematical model is needed in the controller synthesis, the design process does not depend on the increasing order and complexity of the system. A simulation example is presented to illustrate and validate the efficacy of the proposed method.
    American Control Conference (ACC), 2013; 06/2013
  • Muddu Madakyaru, Mohamed N. Nounou, Hazem N. Nounou
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    ABSTRACT: Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of these models. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.
    Modelling and Simulation in Engineering 04/2013; 2013.
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    N Meskin, H Nounou, M Nounou, A Datta
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    ABSTRACT: Recent advances in high-throughput technologies for biological data acquisition have spurred a broad interest in the construction of mathematical models for biological phenomena. The development of such mathematical models relies on the estimation of unknown parameters of the system using the time-course profiles of different metabolites in the system. One of the main challenges in the parameter estimation of biological phenomena is the fact that the number of unknown parameters is much more than the number of metabolites in the system. Moreover, the available metabolite measurements are corrupted by noise. In this paper, a new parameter estimation algorithm is developed based on the stochastic estimation framework for nonlinear systems, namely the unscented Kalman filter (UKF). A new iterative UKF algorithm with covariance resetting is developed in which the UKF algorithm is applied iteratively to the available noisy time profiles of the metabolites. The proposed estimation algorithm is applied to noisy time-course data synthetically produced from a generic branched pathway as well as real time-course profile for the Cad system of E. coli. The simulation results demonstrate the effectiveness of the proposed scheme.
    IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM 03/2013; 10(2):537-43. · 2.25 Impact Factor
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    ABSTRACT: The utilization of mathematical tools in the analysis and synthesis of models representing biological phenomena is rapidly growing. Adding to these efforts, in this paper, a mathematical method based on the sliding mode control approach will be used for the purpose of developing a therapeutic intervention strategy for a class of biological phenomena. Such an intervention scheme aims at moving an undesirable state of a diseased network towards a more desirable state using drugs to act on some genes/metabolites that characterize the undesirable behavior. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena as well as genetic regulatory networks. Since biological phenomena modeled by S-systems are complex nonlinear processes, the need for robust nonlinear intervention strategies that are capable of guiding the target variables to their desired values often arises. The main objective of this paper is to develop an intervention scheme based on sliding mode control theory, sometimes referred to as variable structure control theory, and evaluate the robustness of the sliding mode intervention scheme in the presence of model parameter uncertainties. The proposed intervention strategy is applied to a glycolytic-glycogenolytic pathway model and the simulation results demonstrate the effectiveness of the proposed scheme.
    Journal of Biological Systems 02/2013; 20(04). · 0.73 Impact Factor

Publication Stats

187 Citations
52.03 Total Impact Points

Institutions

  • 2007–2014
    • Texas A&M University at Qatar
      Ad Dawḩah, Ad Dawḩah, Qatar
  • 2007–2012
    • Texas A&M University
      • • Department of Electrical and Computer Engineering
      • • Department of Chemical Engineering
      College Station, TX, United States
  • 2011
    • Qatar University
      • Department of Electrical Engineering
      Doha, Baladiyat ad Dawhah, Qatar
  • 2004–2006
    • United Arab Emirates University
      • Department of Chemical and Petroleum Engineering
      Al ‘Ayn, Abu Zaby, United Arab Emirates
  • 1999–2002
    • The Ohio State University
      • Department of Statistics
      Columbus, OH, United States