A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

Yeroğlu, Celaleddin; Ateş, Abdullah

doi:10.1016/j.jfranklin.2013.12.006

Cited by 49 publications

(41 citation statements)

References 26 publications

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“…SMDO algoritması [23]'de detaylı olarak açıklanmıştır. [24] ve [25]'te ise bu algoritma ile yapılan uygulamalar yer almaktadır.…”

Section: Smdo Algoritması Ile 2dof Fopid Parametrelerinin Elde Edilmesiunclassified

“…Örneğin, genetik algoritma [19], istenen faz ve kazanç payını sağlayan tasarım yöntemi [20], tipik bir ikinci derece sistem için kesir dereceli PD kontrolör parametrelerinin ayarlanması [21] ve FOPID tasarımı için tabu search algoritmasının kullanılması [22], bu yöntemler arasında sayılabilir. Literatürdeki yeni çalışmalar arasında önerdiğimiz olasılıksal çok parametreli sapma optimizasyonu "stochastic multi-parameter divergence optimization (SMDO)" algoritması [23] FOPID kontrolör parametrelerinin ayarlanması için de etkin bir çözüm sunmaktadır [24], [25]. Dolayısı ile SMDO algoritması FOPID kontrolörlü 2DOF kontrol çevriminin performansını artırabilir.…”

Section: Introductionunclassified

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Two degrees of freedom FOPID control loop design via SMDO algorithm

Ateş¹,

Yeroğlu²

2016

Pamukkale J Eng Sci

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“…SMDO algoritması [23]'de detaylı olarak açıklanmıştır. [24] ve [25]'te ise bu algoritma ile yapılan uygulamalar yer almaktadır.…”

Section: Smdo Algoritması Ile 2dof Fopid Parametrelerinin Elde Edilmesiunclassified

Section: Introductionunclassified

Two degrees of freedom FOPID control loop design via SMDO algorithm

Ateş¹,

Yeroğlu²

2016

Pamukkale J Eng Sci

Self Cite

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“…A desired time response specifications of the reference system such as maximum overshoot, peak time, rise time, settling time can be configured by using the appropriate values of the parameters γ and c w of the closed loop transfer function of the reference system [13][14][15][16]. The parameter γ increases the overshoot and c w decreases settling time of step response of BICL.…”

Section: B Teoritical Backgroundmentioning

confidence: 99%

“…This paper presents a reference model optimization approach for the auto-tuning of FOPIDs using SMDO method. The SMDO performs optimization in a parameter space where the controller parameters are represented by a parameter vector in a parameter search space [13][14]. Essentially, SMDO method diverges stochastically parameter vectors to new parameter vectors according to a predefined objective function.…”

Section: Introductionmentioning

confidence: 99%

Tuning of fractional order PID with master-slave stochastic multi-parameter divergence optimization method

Ateş

Alagöz

Yeroğlu

et al. 2014

ICFDA'14 International Conference on Fractional Differentiation and Its Applications 2014

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This paper presents master-slave stochastic multiparameters divergence optimization (SMDO) method for tuning of fractional order proportional-integral-derivative (FOPID) controllers. Bode's ideal control loop (BICL) is used as a reference model that guides the optimization process. Masterslave SMDO performs optimization of FOPID and BICL parameters together. In the first stage called as locking, step responses of FOPID and BICL approximate each other. In the second stage called as drifting, BICL guides tuning of the FOPID towards a desired step response. One of the advantages of presented optimization approach is that both optimization of the reference model (BICL) and controller (FOPID) cooperates and the tuning of FOPID is governed by BICL in the drifting stage. Operation and performance of the proposed algorithm are demonstrated by simulation examples.

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“…The work [13] considers the design of decentralized (multivariable) PID controllers with probabilistic robustness via Monte Carlo (MC) simulations and genetic algorithms (GAs). Finally, other applications of stochastic methods to the design of PID controllers, not necessarily for process models with probabilistic parameterization, have been reported, for example, in [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Robust PID design by chance-constrained optimization

Mercader

Soltesz

Baños

2017

Journal of the Franklin Institute

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A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented. The proposed method constitutes a stochastic extension to the well-studied maximization of integral gain optimization (MIGO) approach, i.e., maximization of integral gain under constraints on the H∞-norm of relevant closed-loop transfer functions. The underlying chance-constrained optimization problem is solved using a gradient-based algorithm once it has been approximated by a deterministic optimization problem. The approximate solution is then probabilistically verified using randomized algorithms (RAs). The proposed method is demonstrated through several realistic synthesis examples.

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A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

Cited by 49 publications

References 26 publications

Two degrees of freedom FOPID control loop design via SMDO algorithm

Two degrees of freedom FOPID control loop design via SMDO algorithm

Tuning of fractional order PID with master-slave stochastic multi-parameter divergence optimization method

Robust PID design by chance-constrained optimization

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