Improved double‐surface sliding mode observer for flux and speed estimation of induction motors

Abstract

This study studies a double-surface sliding-mode observer (DS-SMO) for estimating the flux and speed of induction motors (IMs). The SMO equations are based on an IM model in the stationary reference frame. The DS-SMO is developed based on the equations of a single-surface SMO (SS-SMO) of IM. In DS-SMO method, the observer is designed through combining sliding variables produced by combining estimated fluxes of currents error. The speed is easily determined based on the pass of switching signal through a low-pass filter. Also, an optimal DS-SMO (ODS-SMO) is proposed to improve the transient condition by optimally tuning the observer parameters. To optimise these parameters, the particle swarm optimisation method is adopted. Moreover, an improved DS-SMO (IDS-SMO) is proposed to improve both transient and steady-state conditions, torque ripple and total harmonic distortion. Moreover, the proposed IDS-SMO has a stable performance under sudden load change and the low-speed region. Finally, the accuracy of the proposed ODS-SMO and IDS-SMO methods is substantiated through simulation and experimental results.

Direct slip angle control for rotor side converter of standalone DFIG‐DC system

“…To make a precise evaluation, the root mean square error (RMSE) of the flux and torque is calculated in the following sections. These analyses are based on the subsequent equations [28, 29]: $$\begin{equation}{T_{{\rm{rip}}}} = \sqrt {\frac{1}{N}\sum\nolimits_{i = 1}^N {{{\left( {{T_e}\left( i \right) - {T_{{\rm{e - ave}}}}} \right)}^2}} } \end{equation}$$ $$\begin{equation}{\lambda _{{\rm{rip}}}} = \sqrt {\frac{1}{N}\sum\nolimits_{i = 1}^N {{{\left( {{\lambda _s}\left( i \right) - {\lambda _{{\rm{s - ave}}}}} \right)}^2}} } \end{equation}$$where N , $${{{\lambda}}_{{\rm{s}} - {\rm{ave}}}}$$, and $${T_{{\rm{e}} - {\rm{ave}}}}$$ are the sample number and the average value of the flux and torque, respectively.…”

“…To make a precise evaluation, the root mean square error (RMSE) of the flux and torque is calculated in the following sections. These analyses are based on the subsequent equations [28,29]:…”

Review of speed estimation algorithms for three- phase induction motor

“…Stator current, voltage, and back electromotive force (EMF) or flux are typical estimation parameters [ 9 , 10 ]. The first technique depends on machine model comprising the Model Reference Adaptive System (MRAS) [11] , [12] , [13] , [14] , Extended Kalman Filtering approaches (EKF) [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , Speed Estimators (SE) [25] , [26] , [27] , [28] , Sliding Mode Observer (SMO) [ 26 , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] ], reduced order nonlinear observer [ 23 , [36] , [37] , [38] ], Artificial Intelligence methods (AI) [39] , [40] , [41] , [42] , [43] , Direct calculation and Adaptive observers [44] , [45] , [46] , [47] , [48] . These techniques use motor mathematical models that makes use of stator current and voltage measurements and the motor model to estimate rotor speed.…”

Adaptive Non-Singular Terminal Sliding Mode Control Method for Electromagnetic Linear Actuator

“…It is difficult to meet the requirements of high response speed and high tracking accuracy optimal control. Therefore, scholars introduced the disturbance observer (DOB) to compensate for disturbances to improve the robustness of the system while suppressing jitter [27][28][29][30][31]. Wang designed an internal loop observation compensation controller based on discrete SMC to achieve the effective compensation of external disturbances on system performance [27].…”

“…Wang designed an internal loop observation compensation controller based on discrete SMC to achieve the effective compensation of external disturbances on system performance [27]. Mansouri optimized the two-sided sliding mode observer by a particle swarm algorithm to improve the robustness [28]. Wan proposed the SMC based on an extended state observer, which effectively suppresses the internal and external uncertainties and improves the tracking accuracy significantly [29].…”