Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives With a Modified Reference Frame

Chaoui, Hicham; Khayamy, Mehdy; Aljarboua, Abdullah Abdulaziz

doi:10.1109/tie.2017.2650858

Cited by 112 publications

(46 citation statements)

References 33 publications

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“…Fourth‐order Runge‐Kutta method with fixed step size of 0.1 ms was implemented in numerical assessments. The machine is characterized by the set of system parameters described in Table . Motion planning was specified to smoothly transfer the motor from an initial velocity

{\overset{ω}{true}}_{i n}

to a desired operating velocity

{\overset{ω}{true}}_{f}

into the time interval [ t 1 , t 2 ], following Bézier reference curves described as follows:

y^{⋆} = ({, \begin{matrix} array {\overset{true‾}{ω}}_{i n}, & array for 0 \leq t < t_{1} \\ array {\overset{true‾}{ω}}_{i n} + ({\overset{true‾}{ω}}_{f} - {\overset{true‾}{ω}}_{i n}) φ, & array for t_{1} \leq t \leq t_{2} \\ array {\overset{true‾}{ω}}_{f}, & array for t > t_{2} \end{matrix})

with

φ = true \sum_{k = 1}^{3} r_{k} {((), \frac{t - t_{1}}{t_{2} - t_{1}})}^{2 + k}

where

{\overset{ω}{true}}_{i n} = 0

{\overset{ω}{true}}_{f} = 188.496

rad/s (1800 rpm), r 1 =10, r 2 =−15, r 3 =6, t 1 =0 second, t 2 =5 seconds.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The machine is characterized by the set of system parameters described in Table 1. 4 Motion planning was specified to smoothly transfer the motor from an initial velocity in to a desired operating velocity into the time interval [t 1 , t 2 ], following Bézier reference curves described as follows:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Therefore, remarkable relevance of high‐performance motion control of the synchronous motor can be immediately inferred. Synchronous motors have been driven by different conventional control techniques . It has been a practice to assume constant mechanical load quantities in classical control design.…”

Section: Introductionmentioning

confidence: 99%

“…Synchronous motors have been driven by different conventional control techniques. [4][5][6] It has been a practice to assume constant mechanical load quantities in classical control design. Nevertheless, high-efficiency model-based conventional LIST OF SYMBOLS AND ABBREVIATIONS: , mechanical angular velocity; i d , direct-axis electric current signal; i q , quadrature-axis electric current signal; u d , direct-axis stator voltage; u q , quadrature-axis stator voltage; L , load torque; t 0 , initial time; x, state vector of the synchronous motor; u, control input vector; f, vector function; J, inertia moment; b, viscous damping; R s , stator resistance; L d , direct-axis inductance; L q , quadrature-axis inductance; m , permanent-magnet magnetic flux; n p , number of pole pairs; 1 , output velocity signal; 2 , output electric current signal; ⋆ 1 , desired velocity reference trajectory; ⋆ 2 , desired electric current trajectory; e q , velocity tracking error; e d , electric current tracking error; z, state vector of tracking error dynamics; z 1 , z 2 , z 3 , components of the state vector z; V(z), Lyapunov function candidate; u ⋆ d u ⋆ q , reference trajectories of control inputs; y q , y d , control input gains;̃q,̃d, approximate values of control input gains; r, order of Taylor polynomial; q , d , uncertainty signals; P q,C , P d,C , closed-loop characteristic polynomials; i,j , control gains; in , initial velocity; , desired operating velocity; [t 1 , t 2 ], time interval of the Bézier motion planning; ⋆ , Bézier reference trajectory for angular velocity; A, B, Jacobian matrices of the linear mathematical model; x, equilibrium point in the state space; u, equilibrium control input vector; i d , i q , , ; x , u , state and control vectors of the linear model; control strategies commonly depend on accurate information of motor parameters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic output feedback control for desired motion tracking on synchronous motors

Beltrán-Carbajal

Favela‐Contreras

Hernández-Ávila

et al. 2020

Int Trans Electr Energ Syst

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Summary A new dynamic output feedback control scheme for planned motion reference tracking on nonlinear permanent‐magnet synchronous motors in presence of uncertainty is proposed. Parametric uncertainty, unmodeled dynamics, and variable mechanical load torque are considered as time‐varying unknown signals to be suppressed actively by control voltage inputs. Dynamic feedback of output signals to be controlled are only required to perform entire planned motion tracking. The control scheme can be performed on both classes of interior‐ and surface‐permanent‐magnet synchronous motors. Analytical and numerical results prove the effectiveness of the dynamic output feedback control method on multi‐input multi‐output nonlinear synchronous motors.

show abstract

{\overset{ω}{true}}_{i n}

to a desired operating velocity

{\overset{ω}{true}}_{f}

into the time interval [ t 1 , t 2 ], following Bézier reference curves described as follows:

y^{⋆} = ({, \begin{matrix} array {\overset{true‾}{ω}}_{i n}, & array for 0 \leq t < t_{1} \\ array {\overset{true‾}{ω}}_{i n} + ({\overset{true‾}{ω}}_{f} - {\overset{true‾}{ω}}_{i n}) φ, & array for t_{1} \leq t \leq t_{2} \\ array {\overset{true‾}{ω}}_{f}, & array for t > t_{2} \end{matrix})

with

φ = true \sum_{k = 1}^{3} r_{k} {((), \frac{t - t_{1}}{t_{2} - t_{1}})}^{2 + k}

where

{\overset{ω}{true}}_{i n} = 0

{\overset{ω}{true}}_{f} = 188.496

rad/s (1800 rpm), r 1 =10, r 2 =−15, r 3 =6, t 1 =0 second, t 2 =5 seconds.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic output feedback control for desired motion tracking on synchronous motors

Beltrán-Carbajal

Favela‐Contreras

Hernández-Ávila

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…T2FS and T1FS have been addressed in numerous studies in the fields of engineering and control (Chaoui, Khayamy, & Aljarboua, ; Coteli, Acikgoz, Ucar, & Dandil, ; Mendez, Hernández, Cavazos, & Mata‐Jiménez, ), identification (Almaraashi, John, Coupland, & Hopgood, ; Dahal, Almejalli, Hossain, & Chen, ; Togun & Baysec, ), prediction (Mendez et al, ; Teshnehlab, Shoorehdeli, & Sedigh, ), and modelling (Huang & Chen, ; Liu, Leng, & Fang, ; Meher, Behera, Rene, & Park, ). Identification of dynamic systems using ANFIS demands powerful algorithms to optimize the antecedent and the consequent parameters.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

2019

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In recent years, control research has strongly highlighted the issue of training stability in the identification of non‐linear systems. This paper investigates the stability analysis of an interval type‐2 adaptive neuro‐fuzzy inference system (IT2ANFIS) as an identifier through a novel Lyapunov function. In so doing, stability analysis is initially conducted on the IT2ANFIS identifier, while performing the online training of both the antecedent and the consequent parameters by the gradient descent (GD) algorithm. In addition, the same stability analysis is carried out when the antecedent and the consequent parameters are trained by GD and forgetting factor recursive least square (FRLS) algorithms, respectively (GD + FRLS). A novel Lyapunov function is proposed in this study in order for the identifier stability to attain the required conditions. These conditions determine the permissible boundaries for the covariance matrix and the learning rates at every iteration of the identification procedure. Stability analysis reveals that wide range of learning rates is obtained. Furthermore, simulation results indicate that when the permissible boundaries are selected according to the proposed stability analysis, a stable identification process with appropriate performance is achieved.

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Nonlinear sensorless speed tracking control for PMSM with unmodeled electromotive forces

Yang

Cheng

Zhu

et al. 2021

Asian Journal of Control

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In this paper, a sensorless speed tracking control scheme is proposed for Permanent Magnet Synchronous Motor (PMSM) with unmodeled electromotive forces. Specifically, one observer is developed to estimate the angular velocity of PMSM and the estimated value is injected into a novel nonlinear feedback controller. The unmodeled electromotive forces are not estimated. The influences of the unmodeled electromotive forces on the speed tracking performance are overcome by constructing corresponding nonlinear damping terms in the controller. Under certain assumptions, the rigorous proof of the stability of the closed-loop system is completed with Lyapunov theory. All the dynamic signals are bounded. Furthermore, the estimation error and the tracking error can converge to an arbitrarily small value by choosing the control parameters appropriately large. Finally, the simulation results of constant and time-vary speed tracking illustrated the effectiveness of the proposed controller.

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Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives With a Modified Reference Frame

Cited by 112 publications

References 33 publications

Dynamic output feedback control for desired motion tracking on synchronous motors

Dynamic output feedback control for desired motion tracking on synchronous motors

Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

Nonlinear sensorless speed tracking control for PMSM with unmodeled electromotive forces

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