First application of symbiotic organisms search algorithm to off-line optimization of PI parameters for DSP-based DC motor drives

Çelik, Emre; Öztürk, Nihat

doi:10.1007/s00521-017-3256-5

Cited by 25 publications

(8 citation statements)

References 33 publications

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“…However, some other possible criteria including integral‐based error functions combined by some time‐domain performance metrics like overshoot, settling time, rise time, etc., can be considered for future investigation. Some cost functions have been reviewed in Reference 97, and the function by a combination of ISE and Integral of Absolute Error (IAE) has been developed in the recent studies 23,40 In this study, we apply the direct speed of DC motor with uncertainty in physical load parameters as a stochastic closed‐loop control system.…”

Section: Discussionmentioning

confidence: 99%

“…Some cost functions have been reviewed in Reference 97, and the function by a combination of ISE and Integral of Absolute Error (IAE) has been developed in the recent studies. 23,40 • In this study, we apply the direct speed of DC motor with uncertainty in physical load parameters as a stochastic closed-loop control system. The proposed algorithm aims to perform a robust tuning of stochastic controllers with the least number of function evaluations.…”

Section: Limitations Of the Current Studymentioning

confidence: 99%

“…The optimal design of I‐PD controller (a new version of the PID) for DC motor speed control system by the cuckoo search metaheuristic optimization technique has been proposed in Reference 39. A new search algorithm based on the Symbiotic Organisms Search (SOS) algorithm for tuning proportional‐integral parameters has been propose by study in Reference 40, when it provides comparisons with SOS performance against those of PSO, Genetic Algorithm (GA), and Ziegler‐Nichols (Z‐N) tuning method. Stochastic Fractal Search (SFS) algorithm has been efficiently employed in Reference 23, for enhancing speed control of a DC servo system by tuning PI + DF controller in comparison with PSO.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

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This paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations Of the Current Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Parnianifard

Fakhfakh

Kotti

et al. 2020

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…The superior performance of SOS over well-known optimization techniques are affirmed in the original paper using some unconstrained mathematical problems and structural engineering design problems [20]. Besides, the SOS algorithm and its modified versions have been successfully employed in the optimization of various benchmarks and real life engineering problems, such as load frequency control of multi-area power system [21,22], load dispatch problem with valve-point effect [23], off-line optimization of PI parameters for DSP-based DC motor drives [24], optimization of pinjointed structures [25], economic dispatch problem [26], design of planar concentric circular antenna arrays [27], efficient PID based automatic voltage regulator design [28,29], static optimal power flow problem [30], and optimal placement and sizing of distributed generators [31]. Nonetheless, in the light of our literature review, SOS has not been yet applied to optimal design of multiple linear regression model in attempting to estimate synchronous motor excitation current for power factor correction task.…”

Section: Commensalist Relationmentioning

confidence: 96%

Estimation of Synchronous Motor Excitation Current Using Multiple Linear Regression Model Optimized by Symbiotic Organisms Search Algorithm

Çelik

2018

Mugla Journal of Science and Technology

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In this paper, an effective and simple means of estimating the excitation current of a synchronous motor (SM) is presented for power factor correction task. First, a multiple linear regression model with four predictor variables such as motor load current, actual power factor, power factor error and excitation current change is formed to estimate the SM excitation current. Then, recently introduced symbiotic organisms search (SOS) algorithm is benefitted in the hope of searching better values of regression coefficients in that model using the data collected from the prepared experimental setup. The supremacy of SOS over some recently published algorithms such as genetic algorithm, artificial bee colony and gravitational search algorithm is widely attested through comparative computer simulations for the similar compensation system. The results exhibited in this article show that the presented technique outperforms the other reported popular algorithms from the aspects of simplicity, robustness and accuracy. In view of this, the suggested tuning of regression coefficients of the multiple linear regression model yields a better estimating performance of SM excitation current than the earlier studies.

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“…The proportional-integral (P-I) controller is a closedloop controller which are extremely used due to its fast control, favorable cost and simplified structure with robust regulation. The proportional-integral (PI) controller is the most extensive controller in industrial control applications [45]. The error signal in the PI controller is calculated by the difference between the actual value desired values.…”

Section: B Power Hardware-in-loopmentioning

confidence: 99%

Efficient Real-Time Controller Design Test Bench for Power Converter Applications

et al. 2021

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In order to evaluate and validate the latest trends of power-hardware-in-loop (PHIL) test setup, the dc-dc buck converter is modelled within a real-time system where the simulation model of the converter is exported to FPGA NI PXIe with a time step of 250 ns. PHIL setup allows high flexibility, the benefit of graphical programming, and advanced investigation of the control system for a converter without any safety concern and with the possibility of testing against situations that rarely occur in the field. The LabVIEW-FPGA has been used as a prototyping environment for a digital controller with the help of OPAL-RT eHS software and NI hardware. Such collaboration enables other software such as MATLAB/Simulink, Multisim, PLECS, PSIM, and LabVIEW Co-simulation for accelerating innovative research and development. This research work presents a more efficient and effectual NI PXIE platform with at least ten times more FPGA capability. This paper highlights the hardware-software toolset's performance and the proposed methodology by addressing regulation issues in dc-dc converters. For more satisfactory and reliable operation real-time simulation study of a dc-dc buck converter is evaluated at different parametric variations under the closed-loop PI controller. Finally, the executed model's effectiveness for a closed loop buck converter with real DC loads is validated through the hardware-in-loop (HIL) laboratory setup.

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First application of symbiotic organisms search algorithm to off-line optimization of PI parameters for DSP-based DC motor drives

Cited by 25 publications

References 33 publications

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Robust tuning and sensitivity analysis of stochastic integer andfractional‐order PIDcontrol systems: application ofsurrogate‐basedrobustsimulation‐optimization

Estimation of Synchronous Motor Excitation Current Using Multiple Linear Regression Model Optimized by Symbiotic Organisms Search Algorithm

Efficient Real-Time Controller Design Test Bench for Power Converter Applications

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