Second-Order Sliding-Mode Observer With Online Parameter Identification for Sensorless Induction Motor Drives

Zhao, Lihang; Huang, Jin; Liu, He; Li, Bingnan; Kong, Wubin

doi:10.1109/tie.2014.2301730

Cited by 241 publications

(31 citation statements)

References 26 publications

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“…As shown in Figure 2, only the weight w 2 is adjusted online by Equation (18). Consequently, the convergence of the estimated stator current to the measured stator current strongly depends on the convergence of the estimated speed.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…Various methods based on machine model-based schemes have been introduced. The broadly known methods are open-loop speed estimator, sliding-mode observer (SMO) [12,[16][17][18][19], adaptive full-order observer (AFO) [20][21][22][23][24], extended Kalman filter (EKF) [13,[25][26][27], model reference adaptive system (MRAS) [28][29][30][31] and artificial neural network (ANN) based technique [32,33].The open-loop speed estimation methods are simple to implement but they are sensitive to motor parameter variations, which lead to the estimation error. In [12,[16][17][18][19], various SMO methods are presented.…”

mentioning

confidence: 99%

“…The open-loop speed estimation methods are simple to implement but they are sensitive to motor parameter variations, which lead to the estimation error. In [12,[16][17][18][19], various SMO methods are presented. These methods are robust to the disturbances and easy to implement.…”

mentioning

confidence: 99%

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Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Pimkumwong

Wang

2018

Energies

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This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow-Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high-and low-speed regions have confirmed the validity of the proposed speed estimation method. switching table are replaced by the proportional-integral (PI) controllers and SVM technique when comparing with the traditional DTC. The coordinate transformation, which is used in DTC-SVM, is not required. As a result, the torque and flux ripples are reduced and the switching frequency is constant. However, the speed signal is required in this control algorithm. Speed signal is generally achieved in two ways: one is the direct measurement through speed sensors such as tachogenerators, optical rotary encoders and magnetic rotary encoders; the other is the software based state observers and estimators by other physical parameters known as speed sensorless.The speed sensorless control of IM drives has attracted attention due to its advantages of reduction of hardware complexity and cost, eliminating of sensor cables, higher mechanical robustness, increasing the system reliability, lower maintenance requirements and cost, and ability of working in hostile environments. Several speed estimation methods for the speed sensorless IM drives have been proposed and investigated. They can be classified into two categories: machine model-based schemes and signal injection-based schemes [8][9][10][11][12][13][14].Signal injection-based schemes use rotor slot harmonics, saturated, and leakage inductances for speed estimation by injecting high or low frequency signals. In [8][9][10], high frequency signal injection methods are introduced for speed estimation. A low frequency signal injection method for sensorless control of interior permanent magnet synchronous motor (IPMSM) drives is presented in [11]. These methods are insensitive to motor parameter variations and provide accurate speed estimation, especially at low speed [12]. However, they are very complicated to implement, strongly rely on motor designs and require special designs for specific motor drives [12][13][14]. Moreover, torque ripples and noises make them hard to be widely used in industry [14].Machine model-based schemes use the instantaneous values of machine stator voltages and currents to estimate the speed and flux linkage. These methods utilize the direct and quadrature (d-q) axis mathematical models to describe the machine behavior by ass...

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Pimkumwong

Wang

2018

Energies

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“…A PID sliding mode control using saturated function has been suggested by some researchers in literature [36][37][38]. The advantage of this method can be rapidly convergence, but the system stability has not been proved and chattering still exists.…”

Section: Remarkmentioning

confidence: 99%

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Trong

Duc

2017

Energies

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Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation.In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.

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“…The control in conventional SMC is discontinuous and requires the full state vector for its implementation. All these points mentioned here, are well known restrictions, concerns or problems in the field of sliding mode control, for which researchers have provided a variety of solutions [4][5][6].…”

Section: Introductionmentioning

confidence: 96%

A new state and perturbation observer based sliding mode controller for uncertain systems

Ginoya

Shendge

Patre

et al. 2014

Int. J. Dynam. Control

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This paper proposes a new continuous sliding mode controller and observer for perturbed systems. The controller, which does not need any knowledge of bounds of any perturbation, is estimated using the uncertainty and disturbance estimation technique and implemented with observer states. The stability of the overall observer-controller combination is proved. The controller-observer combination is validated by implementing the scheme for an actual hardware set-up of serial flexible joint in laboratory.

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Second-Order Sliding-Mode Observer With Online Parameter Identification for Sensorless Induction Motor Drives

Cited by 241 publications

References 26 publications

Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

A new state and perturbation observer based sliding mode controller for uncertain systems

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