2022 |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022b,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
Victor, Stéphane; Mayoufi, Abir; Malti, Rachid; Chetoui, Manel; Aoun, Mohamed System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). Résumé | Liens | BibTeX | Étiquettes: Continous time, Continuous time systems, Fractional model, Fractional systems, Instrumental variables, Intelligent systems, Monte Carlo methods, Multiple input single output systems, Multiple inputs single outputs, Optimization, Optimization algorithms, Order estimation, Order optimizations, Parameter estimation, Religious buildings, System-identification @article{Victor2022,This paper deals with continuous-time system identification of multiple-input single-output (MISO) fractional differentiation models. When differentiation orders are assumed to be known, coefficients are estimated using the simplified refined instrumental variable method for continuous-time fractional models extended to the MISO case. For unknown differentiation orders, a two-stage optimization algorithm is proposed with the developed instrumental variable for coefficient estimation and a gradient-based algorithm for differentiation order estimation. A new definition of structured-commensurability (or S-commensurability) is introduced to better cope with differentiation order estimation. Three variants of the algorithm are then proposed: (i) first, all differentiation orders are set as integer multiples of a global S-commensurate order, (ii) then, the differentiation orders are set as integer multiples of a local S-commensurate orders (one S-commensurate order for each subsystem), (iii) finally, all differentiation orders are estimated by releasing the S-commensurability constraint. The first variant has the smallest number of parameters and is used as a good initial hit for the second variant which in turn is used as a good initial hit for the third variant. Such a progressive increase of the number of parameters allows better performance of the optimization algorithm evaluated by Monte Carlo simulation analysis. © 2022 Elsevier Ltd |
2017 |
Guefrachi, Ayadi; Najar, Slaheddine; Amairi, Messaoud; Aoun, Mohamed Tuning of Fractional Complex Order PID Controller Conférence vol. 50, no. 1, 2017, (Cited by: 22; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Calculations, Complex order controllers, Controlled system robustness, Controllers, Delay control systems, Design, Electric control equipment, Fractional calculus, Frequency and time domains, Frequency domain analysis, Gain variations, Numeric optimization, Numerical methods, Numerical optimizations, Optimization, PID controllers, Proportional control systems, Robust control, Three term control systems, Time domain analysis @conference{Guefrachi201714563b,This paper deals with a new structure of Fractional Complex Order Controller (FCOC) with the form PIDx+iy, in which x and y are the real and imaginary parts of the derivative complex order, respectively. A tuning method for the Controller based on numerical optimization is presented to ensure the controlled system robustness toward gain variations and noise. This can be obtained by fulfilling five design requirements. The proposed design method is applied for the control of a Second Order Plus Time Delay resonant system. The effectiveness of the FCOC design method is checked through frequency and time domain analysis. © 2017 |
Yakoub, Z.; Amairi, M.; Chetoui, M.; Saidi, B.; Aoun, M. Model-free adaptive fractional order control of stable linear time-varying systems Article de journal Dans: ISA Transactions, vol. 67, p. 193 – 207, 2017, (Cited by: 22). Résumé | Liens | BibTeX | Étiquettes: Adaptive control systems, Calculations, Controllers, Fractional calculus, Fractional order control, Fractional pid controllers, Frequency characteristic, Frequency domain analysis, Linear time-varying systems, Model-free adaptive control, Numerical methods, Numerical optimizations, Optimization, Robustness (control systems), Selective filtering, Three term control systems, Time varying control systems @article{Yakoub2017193b,This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example. © 2017 ISA |
Yakoub, Z.; Chetoui, M.; Amairi, M.; Aoun, M. Model-based fractional order controller design Conférence vol. 50, no. 1, 2017, (Cited by: 3; All Open Access, Bronze Open Access). Résumé | Liens | BibTeX | Étiquettes: Bias elimination, Closed loops, Controllers, Fractional differentiation, Frequency domain analysis, Identification for control, Least squares approximations, Optimization, Process control, Recursive least square (RLS) @conference{Yakoub201710431b,This paper deals with model-based fractional order controller design. The objective is identification for controller design in order to achieve the desired closed-loop performances. Firstly, the fractional order closed-loop bias-eliminated least squares method is used to identify the process model. Then, based on the numerical optimization of a frequency-domain criterion, the fractional controller is designed. If the proposed algorithm detects any changes in the process parameters, the controller is updated to keep the same performances. A numerical example is presented to show the efficiency of the proposed scheme. © 2017 |
2015 |
Yakoub, Z.; Amairi, M.; Chetoui, M.; Aoun, M. On the Closed-Loop System Identification with Fractional Models Article de journal Dans: Circuits, Systems, and Signal Processing, vol. 34, no. 12, p. 3833 – 3860, 2015, (Cited by: 20). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Closed loop systems, Closed loop transfer function, Closed loops, Fractional model, Identification (control systems), Information criterion, Instrumental variable methods, Instrumental variables, Intelligent systems, Least Square, Monte Carlo methods, Non-linear optimization algorithms, Nonlinear programming, Optimization, Religious buildings @article{Yakoub20153833b,In this paper, the fractional closed-loop system identification problem is addressed. Using the indirect approach, which supposes the knowledge of the controller, both coefficients and fractional orders of the process are estimated. The optimal instrumental variable method combined with a nonlinear optimization algorithm is handled to identify the fractional closed-loop transfer function. Also, two techniques are used for model selection the Akaike’s information criterion and the $$R_T^2$$RT2 criterion. The performances of the proposed scheme are illustrated by a numerical example via Monte Carlo simulation and by real electronic system identification. © 2015, Springer Science+Business Media New York. |
Yakoub, Z.; Chetoui, M.; Amairi, M.; Aoun, M. 2015, (Cited by: 2). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Closed loop systems, Closed loops, Continuous time systems, Continuous-time, Direct approach, Fractional differentiation, Identification (control systems), Least Square, Least squares approximations, Nonlinear programming, Numerical methods, Optimization, Religious buildings, State-variable filters @conference{Yakoub2015e,The paper deals with the continuous-time fractional closed-loop system identification in a noisy output context. Both coefficients and fractional orders of the process are estimated using the direct approach. The proposed method is based on the least squares technique and the state variable filter. It is an extension of the bias eliminated least squares method to the fractional systems. It is combined to a nonlinear optimization algorithm in order to estimate both coefficients and fractional orders of the fractional process. A numerical example is presented to illustrate the performances of the proposed methods. © 2015 IEEE. |
Saidi, B.; Amairi, M.; Najar, S.; Aoun, M. Bode shaping-based design methods of a fractional order PID controller for uncertain systems Article de journal Dans: Nonlinear Dynamics, vol. 80, no. 4, p. 1817 – 1838, 2015, (Cited by: 66). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Carbon monoxide, Constrained optimization, Damping, Design, Electric control equipment, Fractional PID, Fractional-order PID controllers, Frequency bands, Frequency domain analysis, Frequency-domain design, Iso-damping property, Numerical methods, Numerical optimization algorithms, Numerical optimizations, Optimization, Proportional control systems, Robustness (control systems), Test benches, Three term control systems, Uncertain systems, Uncertainty @article{Saidi20151817b,This paper deals with robust fractional order PID controller design via numerical optimization. Three new frequency-domain design methods are proposed. They achieve good robustness to the variation of some parameters by maintaining the open-loop phase quasi-constant in a pre-specified frequency band, i.e., maintaining the iso-damping property of the controlled system. The two first methods are extensions of the well-known Monje-Vinagre et al. method for uncertain systems. They ameliorate the numerical optimization algorithm by imposing the open-loop phase to be flat in a frequency band not only around a single frequency. The third method is an interval-based design approach that simplifies the algorithm by reducing the constraints number and offers a more large frequency band with an iso-damping property. Several numerical examples are presented to show the efficiency of each proposed method and discuss the obtained results. Also, an application to the liquid carbon monoxide level control is presented. © 2014, Springer Science+Business Media Dordrecht. |
Yakoub, Z.; Chetoui, M.; Amairi, M.; Aoun, M. A bias correction method for fractional closed-loop system identification Article de journal Dans: Journal of Process Control, vol. 33, p. 25 – 36, 2015, (Cited by: 21). Résumé | Liens | BibTeX | Étiquettes: Active filters, Algorithms, Bias-correction methods, Bias-eliminated least squares methods, Closed loop systems, Commensurate-order, Continuous time systems, Electromagnetic wave attenuation, Fractional differentiation, Identification (control systems), Intelligent systems, Least Square, Least squares approximations, Least-squares estimator, Monte Carlo methods, Non-linear optimization algorithms, Nonlinear programming, Numerical methods, Optimization, Religious buildings, State-variable filters @article{Yakoub201525b,Abstract In this paper, the fractional closed-loop system identification using the indirect approach is presented. A bias correction method is developed to deal with the bias problem in the continuous-time fractional closed-loop system identification. This method is based on the least squares estimator combined with the state variable filter approach. The basic idea is to eliminate the estimation bias by adding a correction term in the least squares estimates. The proposed algorithm is extended, using a nonlinear optimization algorithm, to estimate both coefficients and commensurate-order of the process. Numerical example shows the performances of the fractional order bias eliminated least squares method via Monte Carlo simulations. © 2015 Elsevier Ltd. |
2014 |
Saidi, B.; Amairi, M.; Najar, S.; Aoun, M. Min-Max optimization-based design of fractional PID controller Conférence 2014, (Cited by: 3). Résumé | Liens | BibTeX | Étiquettes: Algorithms, Automation, Calculations, Constrained optimization, Convergence of numerical methods, Design method, Disturbance rejection, Electric control equipment, Fractional calculus, Fractional pid controllers, Load disturbance rejection capabilities, Min-max optimization, Numerical methods, Optimization, Proportional control systems, Robustness (control systems), Simulation example, Stability margins, Three term control systems @conference{Saidi2014468b,This paper deals with a new design method of a fractional PID controller. The proposed method is based on a numerical constrained Min-Max optimization algorithm. Its main objective is the improvement of the transient response, the stability margin, the robustness and the load disturbance rejection capability. All these performances are tested through a simulation example. © 2014 IEEE. |
2010 |
Amairi, Messaoud; Najar, Slaheddine; Aoun, Mohamed; Abdelkrim, M. N. Guaranteed output-error identification of fractional order model Conférence vol. 2, 2010, (Cited by: 13). Résumé | Liens | BibTeX | Étiquettes: Fractional differentiation, Fractional model, Fractional order, Fractional order models, Fractional-order systems, Global optimization, Global optimization techniques, Guaranteed convergence, Identification (control systems), Interval analysis, Optimization, System identifications @conference{Amairi2010246b,A global optimization technique for identifying an output-error fractional order model is proposed. The proposed technique use a modified version of Hansen algorithm. It is capable of estimating the fractional orders and the parameters, with guaranteed convergence. The technique is applied to identify a fractional order system in deterministic and stochastic context. © 2010 IEEE. |
Publications
2022 |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
System identification of MISO fractional systems: Parameter and differentiation order estimation Article de journal Dans: Automatica, vol. 141, 2022, (Cited by: 10). |
2017 |
Tuning of Fractional Complex Order PID Controller Conférence vol. 50, no. 1, 2017, (Cited by: 22; All Open Access, Bronze Open Access). |
Model-free adaptive fractional order control of stable linear time-varying systems Article de journal Dans: ISA Transactions, vol. 67, p. 193 – 207, 2017, (Cited by: 22). |
Model-based fractional order controller design Conférence vol. 50, no. 1, 2017, (Cited by: 3; All Open Access, Bronze Open Access). |
2015 |
On the Closed-Loop System Identification with Fractional Models Article de journal Dans: Circuits, Systems, and Signal Processing, vol. 34, no. 12, p. 3833 – 3860, 2015, (Cited by: 20). |
2015, (Cited by: 2). |
Bode shaping-based design methods of a fractional order PID controller for uncertain systems Article de journal Dans: Nonlinear Dynamics, vol. 80, no. 4, p. 1817 – 1838, 2015, (Cited by: 66). |
A bias correction method for fractional closed-loop system identification Article de journal Dans: Journal of Process Control, vol. 33, p. 25 – 36, 2015, (Cited by: 21). |
2014 |
Min-Max optimization-based design of fractional PID controller Conférence 2014, (Cited by: 3). |
2010 |
Guaranteed output-error identification of fractional order model Conférence vol. 2, 2010, (Cited by: 13). |