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  • 1.
    Castaño Arranz, Miguel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kadhim, Ali
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    On Guided and Automatic Control Configuration Selection: Application on a Secondary Heating System2017Rapport (Annet vitenskapelig)
    Abstract [en]

    This technical report provides supplementary materialto the research paper entitled ”On Guided and AutomaticControl Configuration Selection”, presented at the ETFA 2017.In that paper, different Control Configuration Selection (CCS)tools are reviewed and integrated into guided and automaticCCS methodologies. The guided CCS is a heuristic step-by-stepmethodology to be applied by practitioners, while the automaticCCS methodologies target the adaptation of such heuristicsinto algorithms which can be run in a computer and assist thepractitioners in the decision making. This report summarizesthe results of applying the introduced methodologies to a reallifeprocess: the Secondary Heating System. For an introductorybackground, preliminaries, and details on the methodologies,the reader is referred to the original research paper.

    Fulltekst (pdf)
    fulltext
  • 2.
    Castaño Arranz, Miguel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kadhim, Ali
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    On Guided and Automatic Control Configuration Selection2018Inngår i: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Piscataway, Nj: Institute of Electrical and Electronics Engineers (IEEE), 2018, Vol. F134116Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper discusses the guided and automatic control configuration selection in large scale complex systems. Due to the trend of increasesd level of automation and connectedness which is promoted by the Industry 4.0 strategy and supported by technologies relating to cyber-physical systems and the industrial internet of things, selecting appropriate control strategies becomes increasingly important and complex. This is especially important as a control strategies will limit the achievable performance of the process system, and there are  trade-offs between complexity of the control strategies, achievable performance, vulnerability and maintainability.

    The paper reviews the state of the art of methodologies that support the practitioners in taking decisions on control strategies, where two main approaches are considered, the guided one and a fully automatic one. It is shown how both approached can be conducted and examples are used to clarify the selection process.

    Fulltekst (pdf)
    fulltext
  • 3.
    Kadhim, Ali
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Estimation of the Dynamic Relative Gain Array for Control Configuration Selection2015Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    The control of multi-input multi-output systems (MIMO) is more difficult than for single-input single-output systems (SISO) due to the multitude of input-output couplings. Coupling, simply means that a change in any input leads to changes in many outputs. Nevertheless, in many cases, a simple decentralised controller is usually sufficient to achieve desired performance goals. However, there is a need for systematic techniques that can suggest the most promising configurations or pairings for the decentralized controller.The relative gain array (RGA) has proven itself to be an efficient tool to solve the pairing problem. It is easily calculated and does not depend on input-output scaling. However, it gives misleading results in some cases where system dynamics are involved and hence Dynamic Relative Gain Array (DRGA) used instead. The commonplace procedure to estimate DRGA values from the input-output data is to identify a parametric system model. Thus, the user needs to decide a model structure and a model order to calculate the system frequency response. Eventually, DRGA values are obtained based on that system frequency response over the frequency range of interest. In this work, a method which requires less user interaction is proposed. The system frequency response, and subsequently the DRGA, is directly estimated from the input-output data by employing a non-parametric identification approach. Such an approach reduces the uncertainties arising from incorrect user decisions by avoiding the parametric model identification. However, DRGA values obtained by the nonparametric identification are subject to different uncertainty sources such as system nonlinearity and noise. In this thesis various strategies are presented to reduce the effect of these uncertainties. In that direction, RGA (DRGA) of linear systems is first analysed using a random excitation signal. Due to the nonperiodic nature of the random signal, the frequency response is susceptible to leakage. To reduce the leakage effect, data is divided into sub-records and the frequency response was averaged over these sub-records. Although the data division proved to be efficient in limiting the leakage effect it has a drawback of reducing the frequency resolution. Moreover, RGA (DRGA) of weakly nonlinear systems is analysed using a multisine excitation signal. The multisine excitation is used to distinguish between the nonlinear distortion and the output noise. It is very difficult to make such distinction using the random excitation. However, long experimental time is needed in returns. To overcome the shortcomings represented by low frequency resolution and the experiment running time, local polynomial approximation approach (LPA) is investigated using both random and multisine excitation.In that direction, RGA (DRGA) of linear systems is first analysed using a random excitation signal. Due to the nonperiodic nature of the random signal, the frequency response is susceptible to leakage. To reduce the leakage effect, data is divided into sub-records and the frequency response was averaged over these sub-records. Although the data division proved to be efficient in limiting the leakage effect it has a drawback of reducing the frequency resolution. Moreover, RGA (DRGA) of weakly nonlinear systems is analysed using a multisine excitation signal. The multisine excitation is used to distinguish between the nonlinear distortion and the output noise. It is very difficult to make such distinction using the random excitation. However, long experimental time is needed in returns. To overcome the shortcomings represented by low frequency resolution and the experiment running time, local polynomial approximation approach (LPA) is investigated using both random and multisine excitation.It can be concluded that the proposed approach achieves quite accurate RGA values with the advantage of exempting the user from deriving a complete parametric model of the plant. Hence, efforts of identifying the parameters of all MIMO subsystems can be saved by finding the parameters of the most significant subsystems of a multivariable system.

    Fulltekst (pdf)
    FULLTEXT01
  • 4.
    Kadhim, Ali
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Selection of Decentralized Control Configuration for Uncertain Systems2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Industrial processes nowadays involve hundreds or more of variables to be maintained within predefined ranges to achieve the production demands. However, the lack of accurate models and practical tools to design controllers for such large processes motivate the engineers/practitioners to break the processes down into smaller subsystems and applying decentralized controllers.

    In contrast to the centralized controller, the decentralized controller is favourable in large-scale systems due to its robustness against loop failures and model uncertainties as well as being easier to tune and update. Yet, two steps are required prior to synthesizing these single-input single-output (SISO) controllers that comprise the decentralized controller. In the first step, a set of manipulated and the controlled variables need to be selected while the second step deals with pairing these variables to close the SISO control loops in a manner that limits the interaction between the loops. The latter step, called "input-output pairing", is usually performed by means of interaction measures (IM) tools using a nominal system model. Taking model uncertainties into consideration when deciding the pairing selection of the decentralized controller is necessary since adopting the pairing based on the nominal system model might be misleading and resulting in poor system performance or instability. It is therefore essential to have tools indicating the extent to which the pairing based on the nominal model persists against gain variations due to uncertainties.

    The work in this thesis presents a methodology that determines whether the effect of gain uncertainty would invalidate the selected pairing. This has been done following the definition of the most established IM tool used in the industry, the relative gain array (RGA), and some of its variants. Further, a procedure has been developed to automatically obtain the optimal input-output pairing by formulating the pairing rules of relative interaction array (RIA) method as an \textit{assignment problem} (AP), and thus, simplifying the pairing selection for large-scale systems. Thereafter, uncertainty bounds of the RIA elements are employed to validate the pairing selection under the effect of given variations of the system gain. Moreover, following the RIA pairing rules, a method is proposed to calculate a minimum amount of uncertainty that renders a perturbed system for which the pairing, obtained from the nominal system model, becomes invalid.

    In the aforementioned methodologies, a parametric system model is assumed to be known. To relax this constraint, an approach is therefore proposed and evaluated which identifies the pairing of the decentralized controller directly from the input-output data. This approach has the advantage of exempting the user from deriving a complete parametric model of the plant to decide the input-output pairing, and hence saves the efforts by finding the parameters of the most significant subsystems in a multivariable system. The frequency response of the system and its covariance, and subsequently the dynamic RGA (DRGA) and corresponding uncertainty bounds, are estimated from the input-output data by employing a nonparametric system identification approach. 

    In short, the work presented in this thesis provides beneficial methodologies for researchers in academia as well as engineers in industry to predict the influence of the system gain uncertainty on the pairing selection of decentralized controllers.

    Fulltekst (pdf)
    fulltext
  • 5.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Castaño Arranz, Miguel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Dynamic Relative Gain Array Estimation using Local Polynomial Approximation Approach2016Inngår i: Modeling, Identification and Control, ISSN 0332-7353, E-ISSN 1890-1328, Vol. 37, nr 4, s. 247-259Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article presents a procedure that utilizes the local polynomial approximation approach in the estimation of the Dynamic Relative Gain Array (DRGA) matrix and its uncertainty bounds for weakly nonlinear systems. This procedure offers enhanced frequency resolution and noise reduction when random excitation is used. It also allows separation of nonlinear distortions with shorter measuring time when multisine excitation is imposed. The procedure is illustrated using the well-known quadruple tank process as a case study in simulation and in real life. Besides, a comparison with the pairing results of the static RGA, nonlinear RGA and DRGA based on linearized quadruple tank model for different simulation cases is performed.

    Fulltekst (pdf)
    fulltext
  • 6.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Calculation of Relative Gain Array Based on Nonparametric Process Identification: A Frequency Domain Approach2014Konferansepaper (Annet vitenskapelig)
    Fulltekst (pdf)
    FULLTEXT01
  • 7.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Evaluation of vehicle tracking for traffic monitoring based on road surface mounted magnetic sensors2013Inngår i: The Third IFAC Symposium on Telematics Applications (TA 2013)Applications: November 11‒13, 2013, Yonsei University, Seoul, Korea / [ed] Jang-Won Lee, Red Hook, NY: Curran Associates, Inc., 2013, s. 13-18Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The aim of this work is to evaluate a vehicle tracking scheme as a means of monitoring traffic on roads. The scheme can be used as a component in a traffic monitoring system which can provide traffic management systems and road maintainers with traffic information. Vehicle tracking is achieved by determining vehicle position, velocity and magnetic moment using a nonlinear weighted least squares method (NWLS) on readings from two 3-axes magnetic sensors.The tracking was performed both in simulation and in real life. The traffic monitoring system is composed of two adjacently glue attached wireless sensor nodes, which are placed at a distance of 1 m along the road. A potential misalignment of the sensors due to placement errors is analysed in simulation and addressed.

    Fulltekst (pdf)
    FULLTEXT01
  • 8.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Relative gain array estimation based on non-parametric frequency domain system identification2014Konferansepaper (Fagfellevurdert)
    Fulltekst (pdf)
    FULLTEXT01
  • 9.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Relative Gain Array Estimation Based on Non-Parametric Frequency Domain System Identification2014Inngår i: 2014 IEEE International Conference on Control Applications (CCA 2014): Juan Les Antibes, France, 8 -10 October 2014, Piscataway, NJ: IEEE Communications Society, 2014, s. 110-115Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Since the introduction of the Relative Gain Array (RGA) by Bristol in 1966, it has received a high level of attention as a practical tool for solving the input-output pairing problem in decentralized control. Moreover, many extensions have been proposed like e.g. for the dynamic case and non-square system matrices. Recently, extensions that provide tools for uncertain parametric process models were suggested. In order to removethe dependency of these tools on a parametric description and accurate knowledge of a nominal model this paper proposes a method to calculate the RGA directly from a non-parametric frequency response matrix (FRM), derived from frequency domain system identification approach. The proposed method reduces the influence of model uncertainties on the calculation of the RGA and derives the RGA at frequencies of interest. Using Monte-Carlo principles, the variance of the estimated RGA is derived and compared with recently proposed methods. The results are exemplified on 2 by 2, 2 by 3 and 3 by 3 systems. It concluded that the proposed methods performs well and robust, while simplifying the estimation of the RGA.

  • 10.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Relative Gain Array Estimation Based on Non-parametric Process Identification for Uncertain Systems2015Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Since the introduction of the Relative Gain Array (RGA) by Bristol in 1966, it has become awidely used practical tool for solving the input-output pairing problems in decentralized control. In order to remove the dependency of this tool on a parametric description and accurate knowledge of a nominal model, this work proposes a method to estimate the RGA directly from a non-parametric frequency response matrix (FRM) derived from a frequency domain system identification approach. The proposed method reduces the influence of model uncertainties on the calculation of the RGA and derives the RGA at frequencies of interest. The results are exemplified using a 2x3 LTI systems and a 2x2 uncertain system.

    Fulltekst (pdf)
    FULLTEXT01
  • 11.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Relative Gain Array Variation for Norm Bounded Uncertain Systems2015Inngår i: IEEE 54th Annual Conference on Decision and Control (CDC): Osaka, Japan, 15-18 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, s. 5959-5965, artikkel-id 7403156Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This article proposes computationally tractable, easyto expand and tight relative gain variation bound for uncertain systems. The proposed bound is a further development of previous work, which is summarized anddiscussed. Using several examples, the new method is compared with previous results and the advantages are highlighted. The prediction of sign changes in relative gain array elements due to uncertainties is important for pairing decisions. Based on the proposed bound,a method for the prediction of the uncertainty levels which render sign changes is suggested. The prediction method is currently limited to certain classes of systems.In this prediction method neither prior knowledge of the uncertainty nor numerous calculations are needed.

  • 12.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Castano, Miguel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Gustafsson, Thomas
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Relative Gain Array of Weakly Nonlinear Systems using a Nonparametric Identification Approach2015Inngår i: 2015 IEEE International Conference on Control Applications (CCA 2015): Sydney, Australia, September 21-23, Piscataway, NJ: IEEE Communications Society, 2015, s. 1612-1617, artikkel-id 7320840Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This article presents a procedure to estimate the relative gain array (RGA) matrix for weakly nonlinear systems by means of nonparametric identification of the frequency response matrix (FRM). Specifically, the best linear approximation of nonlinear systems and the covariance of the nonlinear distortions are used in the relative gain array estimation. For the estimation neither process model nor model structure need to be known which is an advantage over methods that require accurate knowledge of a parametric process model. The proposed approach is compared with the original RGA and a nonlinear RGA calculation using the well-known quadruple tank process as a case

  • 13.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system. Department of Electrical Engineering, University of Kufa, Najaf, Iraq.
    Castaño Arranz, Miguel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Automated Control Configuration Selection Considering System Uncertainties2017Inngår i: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 56, nr 12, s. 3347-3359Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper proposes an automated pairing approach for configuration selection of decentralized controllers which considers system uncertainties. Following the Relative Interaction Array (RIA) pairing rules, the optimal control configuration, i.e. the configuration that fits best the pairing rules, is obtained automatically by formulating the control configuration selection problem as an Assignment Problem (AP). In this AP, the associated costs related to each input-output pairing are given by the RIA coefficients. The Push-Pull algorithm is used to solve the AP for the nominal system and to obtain the set of costs for which the resulting configuration remains optimal, also called the perturbation set. The introduction of uncertainty bounds on the RIA-based costs enables the testing of the possible violation of the optimality conditions. Examples to illustrate the proposed approach for a 3×3 system and 4×4 gasifier plant are given.

    Fulltekst (pdf)
    fulltext
  • 14.
    Kadhim, Ali
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Castaño Arranz, Miguel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Birk, Wolfgang
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    System Uncertainty Effect on Optimal Control Configuration Selection2016Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    An approach to investigate the effect of system uncertainty on the optimal controlconfiguration selection in multivariable systems is proposed. An optimal control configuration,i.e the configuration which best agrees with input-output pairing rules according to certaininteraction measure (IM) can be obtained automatically by formulating the control configurationselection as a Transportation Problem (TP). The proposed approach then checks whetherthis optimal control configuration is valid for given system uncertainties or if a change in theconguration could be expected.

1 - 14 of 14
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