Pareto-Based Multi-objective Optimization for Fractional Order $$\hbox {PI}^{\lambda }$$ PI λ Speed Control of Induction Motor by Using Elman Neural Network

Demirtaş, Metin; İlten, Erdem; Çalgan, Haris

doi:10.1007/s13369-018-3364-2

Cited by 26 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where [. ] and h are the integer part and step size, respectively (Demirtas et al, 2019). The derivative order of 4D memristive system is dealt here as fractional-order, where 0 < q i ≤ 1 (i = 1,2,3,4).…”

Section: Fractional-order 4d System Based On Memristormentioning

confidence: 99%

“…The fractional-order operator can be determined via Grünwald–Letnikov (GL) definition as follows (Ilten, 2022):where [.] and h are the integer part and step size, respectively (Demirtas et al, 2019). The derivative order of 4D memristive system is dealt here as fractional-order, where 0 < q i ≤ 1 ( i = 1,2,3,4).…”

Section: Fractional-order 4d System Based On Memristormentioning

confidence: 99%

See 1 more Smart Citation

Fractional-Order sliding mode control of a 4D memristive chaotic system

Gokyildirim

Çalgan

Demirtaş

2023

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

Chaotic systems depict complex dynamics, thanks to their nonlinear behaviors. With recent studies on fractional-order nonlinear systems, it is deduced that fractional-order analysis of a chaotic system enriches its dynamic behavior. Therefore, the investigation of the chaotic behavior of a 4D memristive Chen system is aimed in this study by taking the order of the system as fractional. The nonlinear behavior of the system is observed numerically by comparing the fractional-order bifurcation diagrams and Lyapunov Exponents Spectra with 2D phase portraits. Based on these analyses, two different fractional orders (i.e., q = 0.948 and q = 0.97) are determined where the 4D memristive system shows chaotic behavior. Furthermore, a single state fractional-order sliding mode controller (FOSMC) is designed to maintain the states of the fractional-order memristive chaotic system on the equilibrium points. Then, control method results are obtained by both numerical simulations and different illustrative experiments of microcontroller-based realization. As expected, voltage outputs of the microcontroller-based realization are in good agreement with the time series of numerical simulations.

show abstract

Section: Fractional-order 4d System Based On Memristormentioning

confidence: 99%

Section: Fractional-order 4d System Based On Memristormentioning

confidence: 99%

Fractional-Order sliding mode control of a 4D memristive chaotic system

Gokyildirim

Çalgan

Demirtaş

2023

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…(6) Eşitlik ( 6)'da gösterilen optimizasyon problemi birden fazla amaç fonksiyonu içerdiği için 'Çok amaçlı optimizasyon problemi' olarak tanımlanmaktadır ve bu amaçlar genellikle birbiriyle çelişmektedirler [26]. Bu tip problemlerde hangi çözümün diğer olası çözümlerden daha önce olduğunu belirtmek veya bu çözümleri bir sıralamaya koyma işlemi Pareto Optimal çözümlerini karşımıza çıkarmaktadır [27][28]. Çalışmadaki Pareto eğrisinin elde edilmesine kadar uygulanan adımlar Şekil 10'daki blok diyagramında görülmektedir.…”

Section: Yanıt Yüzey Yöntemi Ile Kontrolör Katsayıların Optimizasyonuunclassified

Pareto-based multi-objective optimization of PI coefficients for speed control of brushless DC motor

Çalgan¹,

Yaman²,

İlten³

et al. 2018

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

Self Cite

View full text Add to dashboard Cite

This paper present comparison for finding optimum values of PI controller coefficients (Proportional gain K p and Integral gain K i) by using Elman Neural Network (ENN) model with Pareto based multi-objective optimization method and Response Surface Method (RSM). Objective functions are chosen as settling time (T s) and maximum overshoot (M o). The aim of the study is to optimize tuning parameters of PI controller K p and K i. Firstly, experimental design has been carried out by using Minitab program. Then, RSM and ENN model have been obtained separately to construct the mathematical model of the brushless DC (BLDC) motor speed control system. Finally, optimization process has been carried out with both methods. Optimum values of coefficients and Pareto front have been obtained after optimization process. The real time outputs of the BLDC motor are obtained by using the obtained optimal values of the coefficients inside of the controller in the system. When compared the results, the better performance is provided by the Pareto based-ENN method.

show abstract

“…After the system has been modeled properly, it needs to be optimized. In terms of optimization, methods such as genetic algorithm (Mallik et al , 2017), response surface method (Ilten and Demirtas, 2019), artificial neural networks (Demirtas et al , 2019) and particle swarm optimization (PSO) (Demenko et al , 2015) are frequently preferred. PSO algorithm is used in this study to optimize the parameters of the proposed controller.…”

Section: Introductionmentioning

confidence: 99%

Conformable fractional order controller design and optimization for sensorless control of induction motor

İlten

2022

COMPEL

Self Cite

View full text Add to dashboard Cite

Purpose In recent years, use of sensorless control methods for electrical motor-based variable speed drive systems has been increasing rapidly to compensate the increasing costs in industrial systems. Also, use of induction motors is popular for a long time to decrease the cost of these industrial systems. This study aims to design an effective controller to improve the sensorless speed control performance of induction motor. To achieve this, a conformable fractional order proportional integral (CFOPI) controller is designed. Design/methodology/approach The system is modeled based on small signal analysis by using the input–output data, experimentally. To do this, system identification toolbox of Matlab is used. The proposed controller is established on conformable fractional integral approach proposed by Khalil et al. (2014). CFOPI controller coefficients are optimized using particle swarm optimization method on the created small signal-based simulation model of the system to minimize the integral time absolute error. To prove the success of the proposed method, a traditional fractional order proportional integral (TFOPI) controller is tested under the same experimental system with the CFOPI controller. Findings TFOPI and CFOPI controllers are tested with the optimum parameters. Reference and actual speed trends are obtained for both methods. In induction motor start-up test, settling-times are measured as 8.73 and 8.44 s and steady-state oscillations are 2.66% and 0% (almost) for TFOPI and CFOPI controllers, respectively. In variable referenced speed tracking test, CFOPI performs well at all speed levels, while TFOPI fails to reach the reference speed at most speed levels. Practical implications Proposed CFOPI control method can be easily implemented in industrial systems, thanks to its simple algorithm. digital signal peripheral interface controller (dsPIC) based driver circuit with designed CFOPI controller used in this study can be applied directly to industrial systems such as elevators, conveyors, cranes and drills. Moreover, it can improve the performance of induction motor-based variable speed drive systems. Originality/value The proposed method provides robust performance for induction motor used in control systems. Additionally, it does this by using less complex algorithm written on the processors according to the traditional fractional order controllers.

show abstract

Pareto-Based Multi-objective Optimization for Fractional Order $$\hbox {PI}^{\lambda }$$ PI λ Speed Control of Induction Motor by Using Elman Neural Network

Cited by 26 publications

References 21 publications

Fractional-Order sliding mode control of a 4D memristive chaotic system

Fractional-Order sliding mode control of a 4D memristive chaotic system

Pareto-based multi-objective optimization of PI coefficients for speed control of brushless DC motor

Conformable fractional order controller design and optimization for sensorless control of induction motor

Contact Info

Product

Resources

About