Mohamed Nounou

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

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Publications (109)65.43 Total impact

  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
    Fourth TAMUQ Annual Research and Industry Forum, Doha, Qatar; 04/2015
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    ABSTRACT: For computational modeling of biological systems, one of the major challenges is the identification of the model parameters. It is very beneficial to use easily obtained measurements and estimate variables and/or parameters in such systems. For instance, time-series dynamic genomic data can be used to develop models representing dynamic genetic regulatory networks. These models can be used to design intervention strategies such as understanding the biological system behavior and curing major illnesses. The study shown in this paper focuses on the parameter identification of biological phenomena modeled by S-systems using Particle Filter (PF). While the nonlinear observed system is assumed to progress according to a probabilistic state space model, the results show that the PF has good convergence properties. It is concluded that the good convergence is due to PF’s ability to deal with highly nonlinear process models.
    12th International Multi-Conference on Systems, Signals and Devices - Conference on Sensors, Circuits & Instrumentation Systems, Mahdia, Tunisia; 03/2015
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    ABSTRACT: In this study, the use of improved Unscented Kalman Filter algorithm based on iterated measurement updates is proposed in an attempt to estimate the nonlinear and non-Gaussian state variables (the concentration and temperature) of the Continuously Stirred Tank Reactor (CSTR) process. Various conventional and state-of-the-art state estimation techniques are compared based on their estimation performance on this objective. These techniques are the Unscented Kalman Filter (UKF), the Square-Root Unscented Kalman Filter (SRUKF), the Iterated Unscented Kalman Filter (IUKF) and the developed Iterated Square Root Unscented Kalman Filter (ISRUKF). The results of the study indicate that the ISRUKF technique has better convergence properties than the IUKF technique; and both of them can provide improved accuracy over the UKF and SRUKF techniques. Moreover, ISRUKF technique is able to provide accuracy related advantages over other estimation techniques. Since this approach re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state.
    12th International Multi-Conference on Systems, Signals and Devices - Conference on Sensors, Circuits & Instrumentation Systems, Mahdia, Tunisia; 03/2015
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    ABSTRACT: Researchers have been studying the uncertainties unique to civil infrastructure such as redundancy; nonlinearity; interaction with surrounding; heterogeneity; boundaries and support conditions; structural continuity, stability, integrity; life cycle performance expectations and so on. For incorporating such uncertainties, filtering techniques accounting for stochasticity can be implemented employing collected data from the structures. In this paper, an Iterated Square Root Unscented Kalman Filter (ISRUKF) method is proposed for the estimation of the nonlinear state variables of nonlinear structural systems, idealized herein in simplified spring-mass-dashpot. Various conventional and state-of-the-art state estimation methods are compared for the estimation performance, namely the Unscented Kalman Filter (UKF), the Square-Root Unscented Kalman Filter (SRUKF), the Iterated Unscented Kalman Filter (IUKF) and the Iterated Square Root Unscented Kalman Filter (ISRUKF) methods. The comparison reveals that the ISRUKF method provides a better estimation accuracy than the IUKF method; while both methods provide improved accuracy over the UKF and SRUKF methods. The benefit of the ISRUKF method lies in its ability to provide accuracy related advantages over other estimation methods since it re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state.
    IMAC XXXIII Conference and Exposition on Structural Dynamics, Orlando, FL, USA; 02/2015
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    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; DOI:10.1002/asjc.1069 · 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; 50(8). DOI:10.1016/j.automatica.2014.06.001 · 3.13 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; DOI:10.1002/acs.2511 · 1.66 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.
    06/2014; 2(2):308-318. DOI:10.1109/JESTPE.2014.2298613
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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
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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 05/2014; 28. DOI:10.1016/j.dsp.2014.01.012 · 1.50 Impact Factor
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    ABSTRACT: This paper proposes a new approach to the analysis and design of biological systems. It will be shown that, upon an application of Time-Scale Separation Principle to a nonlinear biochemical system at steady-state, a rational polynomial function relates the chemical characteristics of slow-rate substances. This functional dependency can be determined by a small set of measurements. With the functional dependency in hand, one can impose design constraints, such as limiting values for concentration of product substances, and extract corresponding values for the design parameters. Some important characteristics of this rational polynomial form will be also explored.
    2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP); 05/2014
  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
    3rd TAMUQ Annual Research and Industry Forum, Doha, Qatar; 04/2014
  • Majdi Mansouri, Hazem Nounou, Mohamed Nounou
    [Show abstract] [Hide abstract]
    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: 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
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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 03/2014; DOI:10.1016/j.mbs.2014.01.011 · 1.49 Impact Factor
  • Fouzi Harrou, Mohamed N. Nounou
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    ABSTRACT: The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.
    02/2014; 2(1):433-443. DOI:10.1080/21642583.2014.913821
  • 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; DOI:10.1007/s11265-013-0758-y · 0.56 Impact Factor
  • Nour Basha, Hazem N. Nounou, Mohamed N. Nounou
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    ABSTRACT: Adaptive fuzzy control is used here to enforce a concentration level of some metabolite of a biological system representing a purine metabolism pathway model to track a reference trajectory in the presence of uncertainties. In contrast to the direct fuzzy controller, the adaptive fuzzy controller is able to reduce the variance of both the system's response and the controller's output. In this paper, we will apply the adaptive fuzzy intervention strategy to the purine metabolism pathway model in the presence of output noise, which is the source of the model's uncertainties, and carry out a sensitivity analysis of the controller's behavior. The simulation will also be carried out using the direct fuzzy controllers, as described in [1], and the results will be compared and analyzed.
    2014 Middle East Conference on Biomedical Engineering (MECBME); 02/2014
  • 01/2014; 2(1):484-492. DOI:10.1080/21642583.2014.920281

Publication Stats

300 Citations
65.43 Total Impact Points

Institutions

  • 2007–2015
    • Texas A&M University at Qatar
      Ad Dawḩah, Baladīyat ad Dawḩah, Qatar
  • 2007–2013
    • Texas A&M University
      • • Department of Electrical and Computer Engineering
      • • Department of Chemical Engineering
      College Station, Texas, 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 Ain, Abu Dhabi, United Arab Emirates
  • 1999–2002
    • The Ohio State University
      • Department of Statistics
      Columbus, OH, United States