An Efficient Antidisturbance Sliding-Mode Speed Control Method for PMSM Drive Systems

Xu, Wei; Junejo, Abdul Khalique; Liu, Yi; Hussien, Mohamed G.; Zhu, Jun

doi:10.1109/tpel.2020.3039474

Cited by 59 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Determined by the sliding mode variable structure control principle, high frequency chattering is unavoidable in the SMO. The traditional method is to use a low-pass filter to suppress chattering in the SMO [17], [18]. However, the use of low-pass filters will produce phase lag.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

Ding

Zou

2022

IEEE Access

View full text Add to dashboard Cite

In this paper, a sensorless control strategy of permanent magnet synchronous motor (PMSM) based on fuzzy sliding mode observer (FSMO) is proposed. On the premise of satisfying the Lyapunov stability condition, a sliding mode observer (SMO) is constructed. The sigmoid function is used instead of the sign function as the switching function. The parameters of sigmoid function are adjusted in real time by establishing fuzzy rules to change the convergence characteristics of sigmoid function, so as to improve the observation performance. The back EMF signal extracted by the SMO can be made smoother by using the back EMF adaptive law, which reduces the chattering of the system and the observation error. In order to solve the problem that the traditional phase locked loop (PLL) can not be used when the positive and negative speed of the motor is switched, a tangent function PLL is proposed in this paper. Through the use of tangent function, the value and symbol of back EMF are avoided from entering the system, so as to realize the accurate estimation of rotor speed and rotor position under the condition of positive and negative speed switching. The designed Fuzzy Sliding Mode Observer is simulated in Matlab/Simulink, and the experimental verification is carried out on the 2kW surface mounted PMSM vector control platform with TMS320F28335 as the main control chip. Simulation and experimental results show that this method can effectively track the changes of rotor speed and position when the motor is switched between positive and negative speed, and has the characteristics of fast convergence, small chattering and good robustness.INDEX TERMS Permanent magnet synchronous motor, sensorless control, fuzzy sliding mode observer, back EMF adaptive law, tangent function phase locked loop.

show abstract

Section: Introductionmentioning

confidence: 99%

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

Ding

Zou

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When EDLSSs are used for the servo system load test, the loading motor is dragged by the bearing system to follow its synchronous motion and simulate the load and inertia torques. During this period, the loading motor is affected by the motion state of the bearing system to produce load torque fluctuation [1][2][3]. Therefore, suppressing the strong-coupling torque disturbance of the servo system to the EDLSS is crucial.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Load Simulation Algorithm Based on an Inertia Simulation Predictive Model

Lin

Wang

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

In this study, an electric dynamic load simulation system (EDLSS) algorithm was proposed based on an inertia simulation predictive model to mitigate strong-coupling torque disturbance and load torque fluctuation caused by the change in the motion state of a bearing system. First, the inertia simulation model was proposed by combining the dynamic equations of both the EDLSS and the target system. The aforementioned inertia simulation model converted the conventional realisation method of the inertia simulation into the tracking of the motion characteristics for the target system under the same working conditions. Next, based on the aforementioned inertia simulation model while considering the strong-coupling torque effect and motor braking state disturbance as two influential factors, an inertia simulation predictive model and a load simulation algorithm were proposed. The predicted speed calculated by the predictive model was consistent with the dynamic characteristics of the target system under the same working conditions and input into the control loop. Based on the analysis of the braking state and power model of the permanent magnet synchronous motor, an energy feedback control method was proposed to improve EDLSS stability caused by the braking state of the loading motor. Finally, the experimental data revealed that the maximum speed fluctuation range of the loading motor was approximately 7.5, which was 84% lower than the range before the application of the aforementioned algorithm, which was about 46.8. Furthermore, the maximum range of the torque ripple was close to 1.5, which was 75% lower than before, which was roughly 6. All experimental data were consistent with simulation data.

show abstract

“…After summarizing the problems of the above methods, the sliding mode variable structure control is introduced to identify the load torque, which is simple in implementation, strong in robustness, and fast in response [26][27][28]. In [29], an improved SMO load torque adaptive identification method is proposed, which is applied to the motor running under variable operating conditions, and the load has been fluctuating.…”

Section: Introductionmentioning

confidence: 99%

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Effective and accurate parameter identification, especially the identification of load torque, is one of the key factors to improve the control performance of the robot servo system. Sliding mode observer (SMO) has always been a common method for identifying load torque due to its advantages of simple implementation, strong robustness, and fast response. However, due to the discontinuity of the SMO switching function, the system will generate high-frequency chattering, which will reduce the accuracy of load torque identification and affect system performance. In this paper, an adaptive parameter identification method based on an improved sliding mode observer is proposed. A continuous deformation mode of saturation function based on boundary variation is proposed as the switching function to alleviate the chattering phenomenon. Meanwhile, the relationship between the sliding mode gain and the feedback gain of proposed SMO is defined so that it can be selected properly to improve the accuracy of identification, and the radial basis function neural network (RBFNN) is used to adaptively tune the boundary layer gain according to the speed change. Moreover, considering that the identification result of the load torque is related to the moment of inertia and the mismatch of the inertia will cause identification errors, the variable period integration method is proposed to identify the inertia and redefine the calculation period of the load torque and inertia. The effectiveness and superiority of the proposed method are verified by simulation experiments. Experimental results demonstrate that the improved SMO combines observer gain coefficient tuning and inertia matching can smoothly and accurately estimate the value of load torque, which is an adaptive identification method worthy of reference for robot servo system.

show abstract

An Efficient Antidisturbance Sliding-Mode Speed Control Method for PMSM Drive Systems

Cited by 59 publications

References 32 publications

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

A Dynamic Load Simulation Algorithm Based on an Inertia Simulation Predictive Model

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer

Contact Info

Product

Resources

About