Research article

Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques


  • Received: 14 December 2023 Revised: 06 February 2024 Accepted: 20 February 2024 Published: 23 February 2024
  • Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories.

    Citation: Shaswat Chirantan, Bibhuti Bhusan Pati. Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques[J]. AIMS Electronics and Electrical Engineering, 2024, 8(1): 28-52. doi: 10.3934/electreng.2024002

    Related Papers:

    [1] P. Pirmohabbati, A. H. Refahi Sheikhani, H. Saberi Najafi, A. Abdolahzadeh Ziabari . Numerical solution of full fractional Duffing equations with Cubic-Quintic-Heptic nonlinearities. AIMS Mathematics, 2020, 5(2): 1621-1641. doi: 10.3934/math.2020110
    [2] SAIRA, Wenxiu Ma, Suliman Khan . An efficient numerical method for highly oscillatory logarithmic-algebraic singular integrals. AIMS Mathematics, 2025, 10(3): 4899-4914. doi: 10.3934/math.2025224
    [3] Kai Wang, Guicang Zhang . Curve construction based on quartic Bernstein-like basis. AIMS Mathematics, 2020, 5(5): 5344-5363. doi: 10.3934/math.2020343
    [4] Taher S. Hassan, Amir Abdel Menaem, Hasan Nihal Zaidi, Khalid Alenzi, Bassant M. El-Matary . Improved Kneser-type oscillation criterion for half-linear dynamic equations on time scales. AIMS Mathematics, 2024, 9(10): 29425-29438. doi: 10.3934/math.20241426
    [5] Dexin Meng . Wronskian-type determinant solutions of the nonlocal derivative nonlinear Schrödinger equation. AIMS Mathematics, 2025, 10(2): 2652-2667. doi: 10.3934/math.2025124
    [6] Samia BiBi, Md Yushalify Misro, Muhammad Abbas . Smooth path planning via cubic GHT-Bézier spiral curves based on shortest distance, bending energy and curvature variation energy. AIMS Mathematics, 2021, 6(8): 8625-8641. doi: 10.3934/math.2021501
    [7] Chunli Li, Wenchang Chu . Remarkable series concerning (3nn) and harmonic numbers in numerators. AIMS Mathematics, 2024, 9(7): 17234-17258. doi: 10.3934/math.2024837
    [8] Beatriz Campos, Alicia Cordero, Juan R. Torregrosa, Pura Vindel . Dynamical analysis of an iterative method with memory on a family of third-degree polynomials. AIMS Mathematics, 2022, 7(4): 6445-6466. doi: 10.3934/math.2022359
    [9] A. Palanisamy, J. Alzabut, V. Muthulakshmi, S. S. Santra, K. Nonlaopon . Oscillation results for a fractional partial differential system with damping and forcing terms. AIMS Mathematics, 2023, 8(2): 4261-4279. doi: 10.3934/math.2023212
    [10] Tongzhu Li, Ruiyang Lin . Classification of Möbius homogeneous curves in R4. AIMS Mathematics, 2024, 9(8): 23027-23046. doi: 10.3934/math.20241119
  • Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories.



    We consider the following family of nonlinear oscillators

    yzz+k(y)y3z+h(y)y2z+f(y)yz+g(y)=0, (1.1)

    where k, h, f0 and g0 are arbitrary sufficiently smooth functions. Particular members of (1.1) are used for the description of various processes in physics, mechanics and so on and they also appear as invariant reductions of nonlinear partial differential equations [1,2,3].

    Integrability of (1.1) was studied in a number of works [4,5,6,7,8,9,10,11,12,13,14,15,16]. In particular, in [15] linearization of (1.1) via the following generalized nonlocal transformations

    w=F(y),dζ=(G1(y)yz+G2(y))dz. (1.2)

    was considered. However, equivalence problems with respect to transformations (1.2) for (1.1) and its integrable nonlinear subcases have not been studied previously. Therefore, in this work we deal with the equivalence problem for (1.1) and its integrable subcase from the Painlevé-Gambier classification. Namely, we construct an equivalence criterion for (1.1) and a non-canonical form of Ince Ⅶ equation [17,18]. As a result, we obtain two new integrable subfamilies of (1.1). What is more, we demonstrate that for any equation from (1.1) that satisfy one of these equivalence criteria one can construct an autonomous first integral in the parametric form. Notice that we use Ince Ⅶ equation because it is one of the simplest integrable members of (1.1) with known general solution and known classification of invariant curves.

    Moreover, we show that transformations (1.2) preserve autonomous invariant curves for equations from (1.1). Since the considered non-canonical form of Ince Ⅶ equation admits two irreducible polynomial invariant curves, we obtain that any equation from (1.1), which is equivalent to it, also admits two invariant curves. These invariant curves can be used for constructing an integrating factor for equations from (1.1) that are equivalent to Ince Ⅶ equation. If this integrating factor is Darboux one, then the corresponding equation is Liouvillian integrable [19]. This demonstrates the connection between nonlocal equivalence approach and Darboux integrability theory and its generalizations, which has been recently discussed for a less general class of nonlocal transformations in [20,21,22].

    The rest of this work is organized as follows. In the next Section we present an equivalence criterion for (1.1) and a non-canonical form of the Ince Ⅶ equation. In addition, we show how to construct an autonomous first integral for an equation from (1.1) satisfying this equivalence criterion. We also demonstrate that transformations (1.2) preserve autonomous invariant curves for (1.1). In Section 3 we provide two examples of integrable equations from (1.1) and construct their parametric first integrals, invariant curves and integrating factors. In the last Section we briefly discuss and summarize our results.

    We begin with the equivalence criterion between (1.1) and a non-canonical form of the Ince Ⅶ equation, that is [17,18]

    wζζ+3wζ+ϵw3+2w=0. (2.1)

    Here ϵ0 is an arbitrary parameter, which can be set, without loss of generality, to be equal to ±1.

    The general solution of (1.1) is

    w=e(ζζ0)cn{ϵ(e(ζζ0)C1),12}. (2.2)

    Here ζ0 and C1 are arbitrary constants and cn is the Jacobian elliptic cosine. Expression (2.2) will be used below for constructing autonomous parametric first integrals for members of (1.1).

    The equivalence criterion between (1.1) and (2.1) can be formulated as follows:

    Theorem 2.1. Equation (1.1) is equivalent to (2.1) if and only if either

    (I)25515lgp2qy+2352980l10+(3430q6667920p3)l514580qp310q276545lgqppy=0, (2.3)

    or

    (II)343l5972p3=0, (2.4)

    holds. Here

    l=9(fgygfy+fgh3kg2)2f3,p=gly3lgy+l(f23gh),q=25515gylp25103lgppy+686l58505p2(f23gh)l+6561p3. (2.5)

    The expression for G2 in each case is either

    (I)G2=126l2qp2470596l10(1333584p3+1372q)l5+q2, (2.6)

    or

    (II)G22=49l3G2+9p2189pl. (2.7)

    In all cases the functions F and G1 are given by

    F2=l81ϵG32,G1=G2(f3G2)3g. (2.8)

    Proof. We begin with the necessary conditions. Substituting (1.2) into (2.1) we get

    yzz+k(y)y3z+h(y)y2z+f(y)yz+g(y)=0, (2.9)

    where

    k=FG31(ϵF2+2)+3G21Fy+G1FyyFyG1,yG2Fy,h=G2Fyy+(6G1G2G2,y)Fy+3FG2G21(ϵF2+2)G2Fy,f=3G2(Fy+FG1(ϵF2+2))Fy,g=FG22(ϵF2+2)Fy. (2.10)

    As a consequence, we obtain that (1.1) can be transformed into (2.1) if it is of the form (2.9) (or (1.1)).

    Conversely, if the functions F, G1 and G2 satisfy (2.10) for some values of k, h, f and g, then (1.1) can be mapped into (2.1) via (1.2). Thus, we see that the compatibility conditions for (2.10) as an overdertmined system of equations for F, G1 and G2 result in the necessary and sufficient conditions for (1.1) to be equivalent to (2.1) via (1.2).

    To obtain the compatibility conditions, we simplify system (2.10) as follows. Using the last two equations from (2.10) we find the expression for G1 given in (2.8). Then, with the help of this relation, from (2.10) we find that

    81ϵF2G32l=0, (2.11)

    and

    567lG32+(243lgh81lf281gly+243lgy)G27l2=0,243lgG2,y+324lG3281glyG2+2l2=0, (2.12)

    Here l is given by (2.5).

    As a result, we need to find compatibility conditions only for (2.12). In order to find the generic case of this compatibility conditions, we differentiate the first equation twice and find the expression for G22 and condition (2.3). Differentiating the first equation from (2.12) for the third time, we obtain (2.6). Further differentiation does not lead to any new compatibility conditions. Particular case (2.4) can be treated in the similar way.

    Finally, we remark that the cases of l=0, p=0 and q=0 result in the degeneration of transformations (1.2). This completes the proof.

    As an immediate corollary of Theorem 2.1 we get

    Corollary 2.1. If coefficients of an equation from (1.1) satisfy either (2.3) or (2.4), then an autonomous first integral of this equation can be presented in the parametric form as follows:

    y=F1(w),yz=G2wζFyG1wζ. (2.13)

    Here w is the general solution of (2.1) given by (2.2). Notice also that, formally, (2.13) contains two arbitrary constants, namely ζ0 and C1. However, without loss of generality, one of them can be set equal to zero.

    Now we demonstrate that transformations (1.2) preserve autonomous invariant curves for equations from (1.1).

    First, we need to introduce the definition of an invariant curve for (1.1). We recall that Eq (1.1) can be transformed into an equivalent dynamical system

    yz=P,uz=Q,P=u,Q=ku3hu2fug. (2.14)

    A smooth function H(y,u) is called an invariant curve of (2.14) (or, equivalently, of (1.1)), if it is a nontrivial solution of [19]

    PHy+QHu=λH, (2.15)

    for some value of the function λ, which is called the cofactor of H.

    Second, we need to introduce the equation that is equivalent to (1.1) via (1.2). Substituting (1.2) into (1.1) we get

    wζζ+˜kw3ζ+˜hw2ζ+˜fwζ+˜g=0, (2.16)

    where

    ˜k=kG32gG31+(G1,yhG1)G22+(fG1G2,y)G1G2F2yG22,˜h=(hFyFyy)G22(2fG1G2,y)G2Fy+3gG21FyF2yG22,˜f=fG23gG1G22,˜g=gFyG22. (2.17)

    An invariant curve for (2.16) can be defined in the same way as that for (1.1). Notice that, further, we will denote wζ as v.

    Theorem 2.2. Suppose that either (1.1) possess an invariant curve H(y,u) with the cofactor λ(y,u) or (2.16) possess an invariant curve ˜H(w,v) with the cofactor ˜λ(w,v). Then, the other equation also has an invariant curve and the corresponding invariant curves and cofactors are connected via

    H(y,u)=˜H(F,FyuG1u+G2),λ(y,u)=(G1u+G2)˜λ(F,FyuG1u+G2). (2.18)

    Proof. Suppose that ˜H(w,v) is an invariant curve for (2.16) with the cofactor ˜λ(w,v). Then it satisfies

    v˜Hw+(˜kv3˜hv2˜fv˜g)˜Hv=˜λ˜H. (2.19)

    Substituting (1.2) into (2.19) we get

    uHy+(ku3hu2fug)H=(G1u+G2)˜λ(F,FyuG1u+G2)H. (2.20)

    This completes the proof.

    As an immediate consequence of Theorem 2.2 we have that transformations (1.2) preserve autonomous first integrals admitted by members of (1.1), since they are invariant curves with zero cofactors.

    Another corollary of Theorem 2.2 is that any equation from (1.1) that is connected to (2.1) admits two invariant curves that correspond to irreducible polynomial invariant curves of (2.1). This invariant curves of (2.1) and the corresponding cofactors are the following (see, [23] formulas (3.18) and (3.19) taking into account scaling transformations)

    ˜H=±i2ϵ(v+w)+w2,˜λ=±2ϵw2. (2.21)

    Therefore, we have that the following statement holds:

    Corollary 2.2. If coefficients of an equation from (1.1) satisfy either (2.3) or (2.4), then is admits the following invariant curves with the corresponding cofactors

    H=±i2ϵ(FyuG1u+G2+F)+F2,λ=(G1u+G2)(±2ϵF2). (2.22)

    Let us remark that connections between (2.1) and non-autonomous variants of (1.1) can be considered via a non-autonomous generalization of transformations (1.2). However, one of two nonlocally related equations should be autonomous since otherwise nonlocal transformations do not map a differential equation into a differential equation [5].

    In this Section we have obtained the equivalence criterion between (1.1) and (2.1), that defines two new completely integrable subfamilies of (1.1). We have also demonstrated that members of these subfamilies posses an autonomous parametric first integral and two autonomous invariant curves.

    In this Section we provide two examples of integrable equations from (1.1) satisfying integrability conditions from Theorem 2.1.

    Example 1. One can show that the coefficients of the following cubic oscillator

    yzz12ϵμy(ϵμ2y4+2)2y3z6μyyz+2μ2y3(ϵμ2y4+2)=0, (3.1)

    satisfy condition (2.3) from Theorem 2.1. Consequently, Eq (3.1) is completely integrable and its general solution can be obtained from (2.2) by inverting transformations (1.2). However, it is more convenient to use Corollary 2.1 and present the autonomous first integral of (3.1) in the parametric form as follows:

    y=±wμ,yz=w(ϵw2+2)wζ2wζ+w(ϵw2+2), (3.2)

    where w is given by (2.2), ζ is considered as a parameter and ζ0, without loss of generality, can be set equal to zero. As a result, we see that (3.1) is integrable since it has an autonomous first integral.

    Moreover, using Corollary 2.2 one can find invariant curves admitted by (3.1)

    H1,2=y4[(2±ϵμy2)2(2ϵμy2)+2(ϵμy22ϵ)u]2μ2y2(ϵμ2y4+2)4u,λ1,2=±2(μy2(ϵμ2y4+2)2u)(2ϵμy22)y(ϵμ2y4+2) (3.3)

    With the help of the standard technique of the Darboux integrability theory [19], it is easy to find the corresponding Darboux integrating factor of (3.1)

    M=(ϵμ2y4+2)94(2ϵu2+(ϵμ2y4+2)2)34(μy2(ϵμ2y4+2)2u)32. (3.4)

    Consequently, equation is (3.1) Liouvillian integrable.

    Example 2. Consider the Liénard (1, 9) equation

    yzz+(biyi)yz+ajyj=0,i=0,4,j=0,,9. (3.5)

    Here summation over repeated indices is assumed. One can show that this equation is equivalent to (2.1) if it is of the form

    yzz9(y+μ)(y+3μ)3yz+2y(2y+3μ)(y+3μ)7=0, (3.6)

    where μ is an arbitrary constant.

    With the help of Corollary 2.1 one can present the first integral of (3.6) in the parametric form as follows:

    y=32ϵμw22ϵw,yz=77762ϵμ5wwζ(2ϵw2)5(2ϵwζ+(2ϵw+2ϵ)w), (3.7)

    where w is given by (2.2). Thus, one can see that (3.5) is completely integrable due to the existence of this parametric autonomous first integral.

    Using Corollary 2.2 we find two invariant curves of (3.6):

    H1=y2[(2y+3μ)(y+3μ)42u)](y+3μ)2[(y+3μ)4yu],λ1=6μ(uy(y+3μ)4)y(y+3μ), (3.8)

    and

    H2=y2(y+3μ)2y(y+3μ)4u,λ2=2(2y+3μ)(u2y(y+3μ)4)y(y+3μ). (3.9)

    The corresponding Darboux integrating factor is

    M=[y(y+3μ)4u]32[(2y+3μ)(y+3μ)42u]34. (3.10)

    As a consequence, we see that Eq (3.6) is Liouvillian integrable.

    Therefore, we see that equations considered in Examples 1 and 2 are completely integrable from two points of view. First, they possess autonomous parametric first integrals. Second, they have Darboux integrating factors.

    In this work we have considered the equivalence problem between family of Eqs (1.1) and its integrable member (2.1), with equivalence transformations given by generalized nonlocal transformations (1.2). We construct the corresponding equivalence criterion in the explicit form, which leads to two new integrable subfamilies of (1.1). We have demonstrated that one can explicitly construct a parametric autonomous first integral for each equation that is equivalent to (2.1) via (1.2). We have also shown that transformations (1.2) preserve autonomous invariant curves for (1.1). As a consequence, we have obtained that equations from the obtained integrable subfamilies posses two autonomous invariant curves, which corresponds to the irreducible polynomial invariant curves of (2.1). This fact demonstrate a connection between nonlocal equivalence approach and Darboux and Liouvillian integrability approach. We have illustrate our results by two examples of integrable equations from (1.1).

    The author was partially supported by Russian Science Foundation grant 19-71-10003.

    The author declares no conflict of interest in this paper.



    [1] Rodriguez J, Cortes P (2012) Predictive Control of Power Converters and Electrical Drives, John Wiley & Sons Ltd, United Kingdom. https://doi.org/10.1002/9781119941446
    [2] Wang L, Gan L (2014) Integral FCS predictive current control of induction motor drive. IFAC Proceedings Volumes 47: 11956-11961. https://doi.org/10.3182/20140824-6-ZA-1003.00753 doi: 10.3182/20140824-6-ZA-1003.00753
    [3] Wang L, Chai S, Yoo D, Gan L, Ng K (2015) PID and predictive control of electrical drives and power converters using MATLAB/Simulink, JohnWiley & Sons. https://doi.org/10.1002/9781118339459
    [4] Odhano S, Bojoi R, Formentini A, Zanchetta P, Tenconi A (2017) Direct flux and current vector control for induction motor drives using model predictive control theory. IET Electr Power Appl 11: 1483-1491. https://doi.org/10.1049/iet-epa.2016.0872 doi: 10.1049/iet-epa.2016.0872
    [5] Ahmed AA, Koh BK, Kim JS, Lee YI (2017) Finite control set-model predictive speed control for induction motors with optimal duration. IFAC Papers On Line 50: 7801-7806. https://doi.org/10.1016/j.ifacol.2017.08.1056 doi: 10.1016/j.ifacol.2017.08.1056
    [6] Wang J, Wang F (2020) Robust sensor less FCS-PCC control for inverter-based induction machine systems with high-order disturbance compensation. J Power Electron 20: 1222-1231. https://doi.org/10.1007/s43236-020-00113-8 doi: 10.1007/s43236-020-00113-8
    [7] Fereidooni A, Davari SA, Garcia C, Rodriguez J (2021) Simplified Predictive Stator Current Phase Angle Control of Induction Motor with a Reference Manipulation Technique. IEEE Access 9: 54173-54183. https://doi.org/10.1109/ACCESS.2021.3070790 doi: 10.1109/ACCESS.2021.3070790
    [8] Rodriguez J, Garcia C, Mora A, Flores-Bahamonde F, Acuna P, Novak M, et al. (2021) Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies. IEEE T Power Electr 37: 3927-3942. https://doi.org/10.1109/TPEL.2021.3121532 doi: 10.1109/TPEL.2021.3121532
    [9] Rodriguez J, Garcia C, Mora A, Davari SA, Rodas J, Valencia DF, et al. (2021) Latest advances of model predictive control in electrical drives—Part Ⅱ: Applications and bench marking with classical control methods. IEEE T Power Electr 37: 5047-5061. https://doi.org/10.1109/TPEL.2021.3121589 doi: 10.1109/TPEL.2021.3121589
    [10] Tang Y, Xu W, Dong D, Liu Y, Ismail MM (2022) Low-Complexity Multistep Sequential Model Predictive Current Control for Three-Level Inverter-Fed Linear Induction Machines. IEEE T Ind Electron 70: 5537-5548. https://doi.org/10.1109/TIE.2022.3192688 doi: 10.1109/TIE.2022.3192688
    [11] Yang X, Zhang L, Xie W, Zhang J (2019) Sequential and Iterative Distributed Model Predictive Control of Multi-Motor Driving Cutterhead System for TBM. IEEE Access 7: 46977-46989. https://doi.org/10.1109/ACCESS.2019.2908388 doi: 10.1109/ACCESS.2019.2908388
    [12] Wang T, Wang Y, Wang X, Han M, Rodríguez J, Zhang Z (2021) A Statistics-Based Dynamic Sequential Model Predictive Control for Induction Motor Drives. 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 513-518, https://doi.org/10.1109/PRECEDE51386.2021.9681001
    [13] Vodola V, Odhano S, Norambuena M, Garcia C, Vaschetto S, Zanchetta P, et al. (2019) Sequential MPC Strategy for High Performance Induction Motor Drives: a detailed analysis. 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 6595-6600. https://doi.org/10.1109/ECCE.2019.8912708
    [14] Vodola V, Odhano S, Garcia C, Norambuena M, Vaschetto S, Zanchetta P, et al. (2019) Modulated Model Predictive Control for Induction Motor Drives with Sequential Cost Function Evaluation. 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 4911-4917. https://doi.org/10.1109/ECCE.2019.8911870
    [15] Wang Y, Zhang Z, Huang W, Kennel R, Xie W, Wang F (2019) Encoderless Sequential Predictive Torque Control with SMO of 3L-NPC Converter-fed Induction Motor Drives for Electrical Car Applications. 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 1-6. https://doi.org/10.1109/PRECEDE.2019.8753238
    [16] Kerboua A, Kelaiaia R (2023) Fault Diagnosis in an Asynchronous Motor Using Three-Dimensional Convolutional Neural Network. Arab J Sci Eng, 1-19. https://doi.org/10.1007/s13369-023-08025-y
    [17] Yin Z, Du C, Liu J, Sun X, Zhong Y (2018) Research on Autodisturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm. IEEE T Ind Electron 65: 3077-3094. https://doi.org/10.1109/TIE.2017.2751008 doi: 10.1109/TIE.2017.2751008
    [18] Mahfoud S, Derouich A, Iqbal A, El Ouanjli N (2022) ANT-colony optimization-direct torque control for a doubly fed induction motor: An experimental validation. Energy Rep 8: 81-98. https://doi.org/10.1016/j.egyr.2021.11.239 doi: 10.1016/j.egyr.2021.11.239
    [19] Dhieb Y, Yaich M, Guermazi A, Ghariani M (2019) PID controller tuning using ant colony optimization for induction motor. J Electr Syst 15: 133-141.
    [20] Essa MS, Elhosseini MA, Gouda EA (2021) Induction Motor Drive Using Fractional-Order Proportional Integral Derivative (FOPID) Controller Based on Nelder-Mead and Grey Wolf Optimizers. MEJ-Mansoura Engineering Journal 46: 23-31. https://doi.org/10.21608/bfemu.2021.178058 doi: 10.21608/bfemu.2021.178058
    [21] Swetha KT, Reddy BV, Jain RK (2022) A Direct Search Nelder Mead MPPT based Induction Motor Drive for Solar PV Water Pumping Systems. 2022 22nd National Power Systems Conference (NPSC), 578-583. https://doi.org/10.1109/NPSC57038.2022.10069049
    [22] Habbi F, Gabour NE, Bounekhla M, Boudissa EG (2021) Output voltage control of synchronous generator using Nelder–Mead algorithm based PI controller. 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), 365-374. https://doi.org/10.1109/SSD52085.2021.9429387
    [23] Mishra DD, Padhi P, Tripathy AA, Patnaik S, Sahoo PK (2023) Optimal Tuning of Fractional Order PID controller using Nelder-Mead Algorithm for DC Motor Speed Control. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT), 373-378. https://doi.org/10.1109/APSIT58554.2023.10201735
    [24] Mallik S, Mallik K, Barman A, Maiti D, Biswas SK, Deb Nk, et al. (2017) Efficiency and Cost Optimized Design of an Induction Motor Using Genetic Algorithm. IEEE T Ind Electron 64: 9854-9863. https://doi.org/10.1109/TIE.2017.2703687 doi: 10.1109/TIE.2017.2703687
    [25] Keskin B, Eminoğlu I (2022) Optimally Tuned PI Controller Design for V/f Control of Induction Motor. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 1-5. https://doi.org/10.1109/HORA55278.2022.9800005
    [26] Houili R, Hammoudi MY, Betka A, Titaouine A (2023) Stochastic optimization algorithms for parameter identification of three phase induction motors with experimental verification. 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), 1-6. https://doi.org/10.1109/ICAECCS56710.2023.10104526
    [27] Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, et al. (2019) An evolutionary gravitational search-based feature selection. Inform Sciences 497: 219–239. https://doi.org/10.1016/j.ins.2019.05.038 doi: 10.1016/j.ins.2019.05.038
    [28] Jalil T, Boudour M, Tadjine M (2013) Optimal tuning of induction motor control using gravitational search algorithm. 2013 3rd International Conference on Systems and Control, 208-213.
    [29] El Mahfoud M, Bossoufi B, El Ouanjli N, Said M, Taoussi M (2021) Improved direct torque control of doubly fed induction motor using space vector modulation. Int J Intell Eng Syst 14: 177-188. https://doi.org/10.22266/ijies2021.0630.16 doi: 10.22266/ijies2021.0630.16
    [30] El Ouanjli N, Mahfoud S, Al-Sumaiti AS, El Daoudi S, Derouich A, El Mahfoud M, et al. (2023) Improved twelve sectors DTC strategy of induction motor drive using Backstepping speed controller and P-MRAS stator resistance identification-design and validation. Alex Eng J 80: 358-371. https://doi.org/10.1016/j.aej.2023.08.077 doi: 10.1016/j.aej.2023.08.077
    [31] El Idrissi A, Derouich A, Mahfoud S, El Ouanjli N, Chantoufi A, Al-Sumaiti AS, Mossa MA (2022) Bearing fault diagnosis for an induction motor controlled by an artificial neural network—Direct torque control using the Hilbert transform. Mathematics 10: 4258. https://doi.org/10.3390/math10224258 doi: 10.3390/math10224258
    [32] El Ouanjli N, Mahfoud S, Bhaskar MS, El Daoudi S, Derouich A, El Mahfoud M (2022) A new intelligent adaptation mechanism of MRAS based on a genetic algorithm applied to speed sensorless direct torque control for induction motor. International Journal of Dynamics and Control 10: 2095-2110. https://doi.org/10.1007/s40435-022-00947-z doi: 10.1007/s40435-022-00947-z
  • This article has been cited by:

    1. Dmitry I. Sinelshchikov, Linearizabiliy and Lax representations for cubic autonomous and non-autonomous nonlinear oscillators, 2023, 01672789, 133721, 10.1016/j.physd.2023.133721
    2. Jaume Giné, Xavier Santallusia, Integrability via algebraic changes of variables, 2024, 184, 09600779, 115026, 10.1016/j.chaos.2024.115026
    3. Meryem Belattar, Rachid Cheurfa, Ahmed Bendjeddou, Paulo Santana, A class of nonlinear oscillators with non-autonomous first integrals and algebraic limit cycles, 2023, 14173875, 1, 10.14232/ejqtde.2023.1.50
    4. Jaume Giné, Dmitry Sinelshchikov, Integrability of Oscillators and Transcendental Invariant Curves, 2025, 24, 1575-5460, 10.1007/s12346-024-01182-x
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1391) PDF downloads(146) Cited by(1)

Figures and Tables

Figures(14)  /  Tables(7)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog