Search

In the field oriented control (FOC) permanent magnet synchronous motor (PMSM) servo system, torque ripple impairs motor performance. Harmonic injection methods can suppress torque ripple by injecting additional voltage harmonic to offset the existing speed harmonic. However, since FOC is a closed-loop motion control strategy, the transfer path from injected voltage harmonic to speed harmonic is affected by FOC, especially in very low frequency. Due to the influence of FOC on the transfer function, the relation between the injected voltage harmonic and speed harmonic varies with operating conditions and motor control parameters. It causes the existing harmonic injection methods ineffective to suppress low order speed harmonics. To solve the problem, in this paper, the closed-loop transfer mechanism of injected voltage harmonics is analyzed and the closed-loop transfer function is derived. Based on the derived closed-loop transfer function, a low order speed harmonic suppression method is proposed. With the derived transfer function, the proposed method can adapt to the variations of the relation between low order injected voltage harmonic and speed harmonic under different operation conditions. Finally, the accuracy of the derived closed-loop transfer function and the effectiveness of the proposed low order speed harmonic suppression method are evaluated by experiment. [ABSTRACT FROM AUTHOR]

This paper reviews the application of Hall position sensors in control and position estimation of permanent magnet synchronous motors (PMSMs). Accurate rotor position and motor speed data are essential for the high-efficiency control of PMSMs in modern industry. Rotor position and motor speed can be measured by mechanical position sensors, which are costly and less reliable, or by rotor position observers, which are sensitive to system models and changes in motor parameters. This paper examines the benefits, limitations, challenges, and uses of low-resolution Hall position sensors in PMSM drives, presenting them as a cost-effective solution for achieving a balance between performance and expense. In addition, the paper discusses recent solutions to issues related to misplaced Hall position sensors and fault-tolerant control algorithms, and gives an outlook to future developments in this area. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) has been widely used in industry due to the advantages of high efficiency, high power density and high reliability. In this paper, a finite-control-set model-free predictive current control (FCS-MFPCC) based on extended control set is proposed for permanent magnet synchronous motor drives. At first, the mathematical model of PMSM is analyzed and the principle of finite-control-set model predictive current control (FCS-MPCC) is elaborated. Then, the traditional FCS-MFPCC based on linear extended state observer (LESO) is introduced. In order to tackle the issue of dissatisfactory steady-state performance of the traditional FCS-MFPCC, a control-set extension method based on the discrete space vector modulation (DSVM) is utilized, by which, the number of voltage vectors can be increased to 25. Following this, a fast optimization strategy is proposed to solve the problem of high computation burden caused by the extended control set. The principle and procedure of implementation of the strategy is explained in detail. Finally, the effectiveness and superiority of the proposed control strategy are verified by comparing the traditional and the proposed FCS-MFPCC methods based on a 500 W PMSM experimental test rig. The results show that the proposed control strategy can significantly enhance the steady-state performance of the system. The total harmonic distortion of stator current can be reduced from 10.07% to 6.48%.

Permanent magnet synchronous motors (PMSMs) are widely utilized in the field of manufacturing and production due to their notable advantages such as simple structure, stable operation, high efficiency, and diverse configurations. Operating under conditions with variable speeds and load fluctuations, PMSMs inevitably experience various faults, including bearing faults, eccentricity faults, demagnetization faults, and interturn short-circuit faults. An overview of common fault types in PMSMs is provided in this paper and the existing researches on signal-based condition monitoring methods in the field of automation for the detection and diagnosis of electrical and mechanical faults are summarized. Subsequently, diagnostic methods are summarized, and their strengths and limitations are analyzed, discussing the pros and cons of different signal processing approaches in application. Finally, considering the current state of research, the challenges and future directions in the monitoring and diagnosis of PMSM faults are discussed.

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor's operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system's dynamic response and steady-state performance. [ABSTRACT FROM AUTHOR]

For the variable speed variable displacement power unit (VSVDPU), achieving power matching between the permanent magnet synchronous motor (PMSM) and the variable displacement plunger pump (VDPP) is the key to reducing system energy consumption. The control method of adjusting the speed of the PMSM and the displacement of the VDPP is the mainstay of current research and application of the VSVDPU. However, the dynamic properties of the PMSM and VDPP have not been balanced, which affects the control effect of the VSVDPU. This paper proposes a control method of variable speed and variable displacement with low energy consumption and high dynamics. The main idea is based on the efficiency model and dynamic response model of the PMSM and VDPP, and the factors that affect the efficiency and dynamic characteristics of the VSVDPU are analyzed. Guided by the multi-objective optimization algorithm, the optimal combination of speed and displacement under specific working conditions is derived. Simulation and experiment results show that the proposed control method is feasible to improve the efficiency and dynamic characteristics of the VSVDPU. [ABSTRACT FROM AUTHOR]

Traditional industrial robots come with prime movers, i.e. electric motors (EMs), which range from a few hundred to just a few kilo watts of power ratings. However, for autonomous robotic navigation systems, we require motors which are lightweight with the aspect of high torque and power density. This aspect is very critical when the EMs in robotic navigations are subjected to harsh high temperature survival conditions, where the sustainability of the performance metrics of the electromagnetic system of the EMs degrades with the prevailing high temperature conditions. Hence, this research work addresses and formulates the design methodology to develop a 630 W high temperature PMSM (HTPMSM) in the aspect of high torque and power density, which can be used for the autonomous robotic navigation systems under high temperature survival conditions of 200°C. Two types of rotor configurations i.e. the surface permanent magnet type (SPM) and the interior permanent magnet type (IPM) of HTPMSM are examined for its optimal electromagnetic metrics under the temperature conditions of 200°C. The 630 W HTPMSM is designed to deliver the rated torque of 2 Nm within the volumetric & diametric constraints of DxL, which comes at 80 × 70 mm at the rated speed of 3 000 rpm with the survival temperature of 200°C and target efficiency greater than 90%. The FEM based results are validated through the hardware prototypes for both SPM and IPM types, and the results confirm the effectiveness of the proposed design methodology of HTPMSM for sustainable autonomous robotic navigation applications. [ABSTRACT FROM AUTHOR]

To address the internal and external disturbances in Permanent Magnet Synchronous Motor (PMSM) such as modeling errors and sudden load variation, a load estimation-based composite Active Disturbance Rejection Control (ADRC) approach is proposed for PMSM speed regulation. ADRC is adopted to replace the PI controller in the speed loop to improve the performance of control system and solve the contradiction between system rapidity and overshooting. A load torque observer is designed to correct the slow response of ESO to sudden load variation by directly estimating and compensating the load torque in real time via speed and current signals. Additionally, a semi-physical experimental platform of composite ADRC for speed regulation is constructed in Matlab/Simulink environment, and the proposed composite ADRC is compared with traditional PI control and linear ADRC. The results illustrate that the proposed approach outperforms conventional controller by reducing the speed variation by more than 30% under abrupt load variation, and has superior disturbance rejection ability and speed regulation performance. [ABSTRACT FROM AUTHOR]

AbstractDue to the high power density and wide speed range, a permanent magnet synchronous motor (PMSM) is commonly used in industry. Efficient production requires optimized motion control of the mechanism driven by the PMSM. Unfortunately, designing the controller is usually based on a model of the mechanism without including parameters originating from the motor. Cogging stiffness originates from the magnetic forces between rotor magnets and stator teeth and is one of these forgotten parameters. The performance is sub-optimal when this stiffness is not considered during control design. The stiffness also changes over time due to changing load conditions such as temperature. Aiming for optimized motion control of high-speed mechanisms, this article proposes to expand the classic motion controller with an on-line stiffness tracker. The tracker is based on the sliding discrete Fourier transform (SDFT). The tracking technique is conceptually analyzed and experimentally validated on a PMSM-driven rod. The classic Welch technique having an update time of at least 100 s is used as a benchmark. With similar accuracy and a much faster update time of 1.25 ms, the stiffness is tracked on-line using SDFT. The developed stiffness tracker is implemented on the provided commercial motion controller, proving its computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we applied 1D convolutional neural networks (1D-CNNs) to permanent magnet synchronous motor (PMSM) fault diagnosis on 12 common PMSM fault types, namely, normal motor (Class A), poor dynamic balance of rotor (Class B), bent shaft (Class C), magnet demagnetization (Class D), uneven air gap (Class E), rotor misalignment (Class F), stator coil three-phase imbalance (Class G), stator coil layer short circuit (Class H), poor lubrication of bearing (Class I), damaged inner ring of bearing (Class J), damaged bearing ball (Class K), and poor assembly (Class L). First, a vibration spectrum analyzer was used to measure and capture the vibration signals of a faulty motor. Then, the 1D-CNN was utilized to analyze and diagnose the captured data. The results showed that the proposed 1D-CNN method can identify 11 motor fault types with an accuracy of up to 99.7%, higher than the 96.1% accuracy of 2D convolutional neural networks (2D-CNNs). In addition, the fault diagnosis system developed in this study can perform a rapid motor fault diagnosis with a small amount of training data, significantly reducing the detection cost for PMSM fault diagnosis. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) can exhibit chaotic behaviors detrimental to their regular operation in practical applications. To accurately predict the chaotic state of PMSM, this paper proposes a C-MRESN method based on the combination of convolutional neural network (CNN) and multi-reservoir echo state network (MRESN). The significant advantage of C-MRESN is that it combines the advantages of the two models, which can capture the complex temporal and spatial information from nonlinear time series and retain these features for prediction. In addition, this work uses the L-BFGS-B optimization algorithm to optimize the training process of C-MRESN and significantly improve the prediction accuracy of C-MRESN. By comparing the prediction experimental results with six other machine learning models, C-MRESN shows the minor prediction error and the most extended accurate prediction range. The root mean square error (MSE) of the 2000-step prediction results of C-MRESN for the three PMSM variables, id,iq and ω can reach 1.190×10−9, 8.599×10−10 and 1.626×10−9, respectively. The experimental results substantiate that the C-MRESN is an effective and advanced method for the chaos prediction of PMSM. [ABSTRACT FROM AUTHOR]

In permanent magnet synchronous motor (PMSM) sensorless drive systems, the motor inductance is a crucial parameter for rotor position estimation. Variations in the motor current induce changes in the inductance, leading to core magnetic saturation and degradation in the accuracy of rotor position estimation. In systems with constant load torque, the saturated inductance remains constant. This inductance error causes a consistent error in rotor position estimation and some performance degradation, but it does not result in speed estimation errors. However, in systems with periodic load torque, the error in the saturated inductance varies, consequently causing fluctuations in both the estimated position and speed errors. Periodic speed errors complicate speed control and degrade the torque compensation performance. In this paper, we propose a wavelet denoising-group method of data handling (GMDH) based method for accurate inductance estimation in PMSM sensorless control systems with periodic load torque compensation. We present a method to analyze and filter the collected three-phase current signals of the PMSM using wavelet transformation and utilize the filtered results as inputs to GMDH for training. Additionally, a method for magnetic saturation compensation using the inductance parameter estimator is proposed to minimize periodic speed fluctuations and improve control accuracy. To replicate the load conditions and parameter variations equivalent to the actual system, experiments were conducted to measure the speed ripples, inductance variations, and torque component of the current. Finally, software simulation was performed to confirm the inductance estimation results and verify the proposed method by simulating load conditions equivalent to the experimental results. [ABSTRACT FROM AUTHOR]

This paper, evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward (PDFF) controller-based direct torque controller (DTC) for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller (HCC). The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed, electromagnetic torque, and stator current. Two case studies, one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation, has been validated to show the effectiveness of the proposed control strategy. The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Permanent magnet synchronous motor (PMSM) speed control systems with conventional linear active disturbance rejection control (CLADRC) strategy encounter issues regarding the coupling between dynamic response and disturbance suppression and have poor performance in suppressing complex nonlinear disturbances. In order to address these issues, this paper proposes an improved two-degree-of-freedom LADRC (TDOF-LADRC) strategy, which can enhance the disturbance rejection performance of the system while decoupling entirely the system's dynamic and anti-disturbance performance to boost the system robustness and simplify controller parameter tuning. PMSM models that consider total disturbances are developed to design the TDOF-LADRC speed controller accurately. Moreover, to evaluate the control performance of the TDOF-LADRC strategy, its stability is proven, and the influence of each controller parameter on the system control performance is analyzed. Based on it, a comparison is made between the disturbance observation ability and anti-disturbance performance of TDOF-LADRC and CLADRC to prove the superiority of TDOF-LADRC in rejecting disturbances. Finally, experiments are performed on a 750 W PMSM experimental platform, and the results demonstrate that the proposed TDOF-LADRC exhibits the properties of two degrees of freedom and improves the disturbance rejection performance of the PMSM system.

AbstractIn the conventional vector control technique for motor drive, Proportional-Integral (PI) controllers are being used, which are sensitive to parameter variations of the drive system. This article presents Machine Learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. In this work, ML-based regression algorithms such as linear regression, support vector machine regression and feedforward neural network are investigated for speed control application. The entire vector control scheme implementing the ML-based control algorithms is investigated theoretically and simulated under various dynamic operating conditions. Simulation results and performance metrics are compared with those of the conventional PI controller, and they validate the effectiveness of the proposed control algorithms for speed control applications. The proposed ML-based controllers have the ability to meet the speed tracking requirements in the closed-loop system, with performance metrics superior to those of the PI controller, by an average value of 20% for different test scenarios. The transient levels of the motor drive reduce by 0.02% while using the proposed controllers.

In this paper, a comparative analysis is obtained to analyse the performances of two-level conventional and three-level diode clamped voltage source inverter-based drives. Carrier-based space vector modulation technique is employed in both three-level & two-level voltage source inverter (2L-VSI)-based drives. Comparative study for 2L-VSI, three-level neutral-point-clamped (3L-NPC) and three-level T-type neutral-point-clamped (3L-TNPC) inverters are performed in view of conduction & switching losses and harmonic profile. Also presented in this work, the dynamic response of field-oriented controlled (FOC) permanent magnet synchronous motor (PMSM) drive under varying load conditions towards EV applications. Rapid changes in speed and loading in Electric Vehicle (EV) traction drives lead to sharp fluctuations in modulation depth and power factor (PF). With variation of modulation index and power factor, power losses of all the three inverters are analysed. Also, in the present work, control logics are simulated in MATLAB to observe various speed-torque combinations of 5.4 kW PMSM drive. Additionally, power losses of inverters are developed in PLECS environment. By using a scaled-down prototype of 5 hp PMSM drive, the results are carried out for hardware validation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method to estimate the electrical angular ve- locity, the electrical angle, and the currents of the permanent magnet synchronous motor. A recurrent neural network learning algorithm is first developed to estimate the states of the permanent magnet synchronous motor. Then, an event-triggered state observer is designed for the recurrent neural network. This state observer robustly estimates state variables of the permanent magnet synchronous motor. A sufficient condition in terms of a convex optimization problem for the existence of the event-triggered state observer is established. In contrast with the abundance of state estimation methods based on time-triggered state observers where the measurements are always continuously available, the ones in this paper are updated when an event-triggered condition holds. Therefore, it lessens the stress on communication resources while can still maintain an estimation performance. Simulation results are provided to demonstrate the merit of the proposed method. [ABSTRACT FROM AUTHOR]

Barrier-function-based adaptive fast-terminal sliding-mode control approaches have been devised to enhance the precision of speed regulation of permanent magnet synchronous motors (PMSMs). Firstly, the speed loop utilizes fast-terminal sliding-mode control, which contributes to a faster convergence rate and enhances the robustness of the system. By adopting this control technique, the system can quickly reach the desired speed setpoint and effectively handle disturbances. Secondly, an adaptive law based on the barrier function is employed to adjust the control gain adaptively. The proposed adaptive law considers the magnitude of the disturbance and effectively mitigates chattering resulting from excessive switching gain. Unlike conventional control methods, the design of the adaptive fast-terminal sliding-mode control does not require attaining the upper limit of the lumped disturbances. Experimental results are presented to validate the proposed approach. These results demonstrate that the proposed method outperforms the conventional terminal sliding mode control technique in terms of handling both external and internal disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we describe a position sensorless vector control system for a permanent magnet synchronous motor (PMSM) for a lawnmower in order to solve the problems of inferior dynamic performance and insufficient load resistance in the control process of lawnmower motors. A speed–current double-closed-loop vector control strategy was adopted to control the motor speed; an extended Kalman filter (EKF) was constructed to track the motor rotor position. STM32F407 was selected as the main control chip to establish the hardware experimental platform, and the performance of the control system was evaluated. The experimental results demonstrate that the control system accurately regulates motor speed, has good dynamic response characteristics, and can maintain stability under various loads; therefore, it meets the performance requirements of lawnmower motors in practical operation. [ABSTRACT FROM AUTHOR]

This paper focuses on the speed control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. Conventional sliding mode control (CSMC) can only converge asymptotically in the infinite domain and will cause unacceptable sliding mode chattering. To improve the performance of the PMSM speed loop in terms of response speed, tracking accuracy, and robustness, a hybrid control strategy for a fixed-time-convergent sliding mode controller (FSMC) with a fixed-time-convergent sliding mode observer (FSMO) is proposed for PMSM speed regulation using the fixed-time control theory. Firstly, the FSMC is proposed to improve the convergence speed and robustness of the speed loop, which can converge to the origin within a fixed time independent of the initial conditions. Then, the FSMO is used as a compensator to further enhance the robustness of the speed loop and attenuate sliding mode chattering. Finally, simulation and experimental results show that the proposed method can effectively improve the dynamic performance and robustness of the PMSM speed control system. [ABSTRACT FROM AUTHOR]

To enhance the anti-inertia disturbance ability of permanent magnet synchronous motor (PMSM) speed system, an adaptive sliding mode control with inertia identification is proposed. A novel sliding mode control (NSMC) based on a new reaching law coupled with model reference adaptive system (MRAS) inertia identification is realized the adaptive control, named MRAS+NSMC. In the NSMC construct process, an integral sliding mode surface and a variable speed reaching law are introduced to avoid speed differentiation and improve dynamics, respectively. And the new reaching law imported a successive sigmoid(s) to replace the traditional sign(s) to suppress chattering phenomena. For the problem that the performance deteriorated by rotational inertia variation caused by load changes, the inertia is estimated in real time according to the MRAS theory, and the identification value is updated to the NSMC controller to realize adaptive MRAS+NSMC speed control. Experimental results show that the proposed adaptive MRAS+NSMC control has a faster speed response, and the speed response time is reduced from 85 to 49 ms compared with conventional SMC control. In addition, it has strong robustness to inertia disturbances and high speed tracking accuracy. Compared with conventional SMC, the speed tracking accuracy of proposed MRAS+NSMC is increased from 12% to 4%. This makes the proposed MRAS+NSMC control has great potential practical significance for speed control of PMSM. [ABSTRACT FROM AUTHOR]

Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To realize advanced velocity regulation for permanent magnet synchronous motors (PMSM) in servo control and enhance the capability to resist disturbances of load and inertia, a disturbance-resistant adaptive fast super-twisting algorithm (FSTA) speed control method is proposed. To address the challenge of of chattering caused by the conventional exponential reaching law in sliding mode control (SMC), a FSTA reaching law is proposed. Introducing an integral sliding surface simultaneously to avoid the requirement of PMSM acceleration in the control input, decreasing the static error of the system. Furthermore, replacing the sign function sgn(s) in the FSTA reaching law with the continuous function sigmoid(s) to further enhance the system’s responsiveness and stability in varying conditions. To address the issue of system instability caused by inertia and load variations, an extended sliding mode observer (ESMO) and model reference adaptive system (MRAS) method are for the purpose of achieving real-time observation of load torque and rotational inertia, respectively. The observed torque values and identified inertia values are then updated in the FSTA control, enabling adaptive FSTA speed control. The outcomes of the experiment show that, compared to the traditional adaptive SMC and STA speed control method, the proposed adaptive FSTA control method in this paper exhibits superior dynamic performance and stronger disturbance rejection capability against load and inertia.

Control techniques for Permanent Magnet Synchronous Motor (PMSM) drives rely significantly on digital systems, and control system discretization is essential for stability and robustness. While continuous-time analyses are widely employed for stability analysis, generally, they fall short of describing phenomena such as the waterbed effect and understanding system dynamics, especially in servo-drive applications. Consequently, discrete-time systems play a crucial role. The use of discrete-time analysis in digital motion systems of control allows for a more accurate study of system stability while addressing the abovementioned difficulties. Despite the vital role of discrete-time analysis in maximizing stability, robustness, and performance in digital PMSM implementations, there is a significant research gap in the complete analysis and investigation of discrete-time control systems. This research focuses on improving digital disturbance observer (DOB)-based speed control via discrete-time analysis by investigating various discretization techniques. Analyzing widespread discrete-time approaches that rely on analog-to-digital time conversion demonstrates that some strategies outperform others. The paper indicates that constructing a digital DOB-based control utilizing the implicit Adams approach and applying it to PMSMs enhances performance by increasing control accuracy and significantly reducing undershoot when compared to popular discrete-time methods such as Euler’s, Tustin, Al-Alaoui, and others. The experimental results show that this chosen discrete-time approach has a significant impact on the efficiency of the PMSM control system. The outcomes of this research highlight the critical relevance of selecting an appropriate discrete-time conversion in improving the performance of a digital DOB motion control system with an application to PMSM drives.

Permanent Magnet Synchronous Motors (PMSMs) are used in a variety of industries, including aerospace, industrial control, and electric vehicles (EVs). Extensive research has been dedicated to the control and drive of PMSMs, with field-oriented control (FOC) gaining prominence in today’s industry. Despite its popularity, the nonlinear nature of the PMSM drive system impacts the dynamic performance when employing the traditional Proportional-Integral (PI)-based FOC method, particularly under varying operating points. Over the last decade, flatness-based control has emerged as a viable alternative to PI-based FOC in response to system nonlinearity. As a traditional approach, a cascaded flatness-based control structure has been proposed in the literature. This method, however, has lower dynamic performance due to its cascaded structure. The flatness-based control with only one control loop is suggested for improving the dynamic performance of the flatness-based control, especially for high-speed and low-inertia systems. This method, however, lacks current protection. This paper introduces a novel trajectory planning approach for the one-loop flatness-based control of PMSM drives. The focus is on addressing controller limits, an aspect that has received limited attention thus far. The proposed approach ensures control stability by considering the constraints of the maximum motor current (determined by the electrical specifications of the motor and driver) and the switching voltages, which depend on the DC-bus voltage. The method’s effectiveness is validated through simulation studies and experimental tests.

In machine learning, the extraction of features is necessary for intelligent motor fault diagnosis. In industrial applications, it is necessary to identify the optimal number of features to differentiate various types of fault characteristics with less computational complexity and cost. However, motor fault diagnosis for real-time applications has challenges in capturing characteristics due to variations in speed, load, force, run-to-failure state as well as the type of the motor and its parts. The deep learning techniques that automatically extract features and perform classification have algorithmic complexity. In this work, the authors address these challenges by: 1) selecting and ensembling optimal time-domain features that are capable of identifying motor faults using current signals of the permanent magnet synchronous motor (PMSM) in bearing; and 2) investigating the feature ensemble constituting optimal features for robust fault diagnosis in the PMSM bearing as well as the stator and bearing of squirrel cage induction motor (SCIM) for various conditions. The optimal features mean absolute value, simple sign integral, and waveform length yields 99.8% and 100% for bearing fault and stator fault diagnosis, respectively, in PMSM. These features show 100% accuracy for identification of fault in SCIM and 98.2% accuracy in the run-to-failure state.

With the increasingly widespread application of permanent magnet synchronous motors (PMSMs), it is required that PMSMs can still maintain high efficiency and high reliability in complex environments. Due to the nonlinearity of PMSMs and the influence of external disturbances, its precise control is still a challenge. As a modern control scheme, sliding mode control can improve the control PMSM drive system. It has been successfully applied in the control of PMSMs and has become a hot spot in the motor drive system. This paper provides an overview of the current research status of sliding mode control strategies based on PMSMs and introduces the design of sliding mode controllers, as well as the development and application of high-order sliding mode control. The method of suppressing the chattering problem is summarized, and the deficiencies of it are put forward. Finally, the development trend of sliding mode control technology is discussed, and the future development direction of SMC for PMSM is prospected.

Permanent magnet synchronous motor (PMSM) is a vital component of modern industry which is widely used in transport, aerospace and intelligent machinery. In the light of its relatively high frequency of inter-turn short circuit (ITSC) fault, it is valuable to detect the fault accurately and efficiently. Due to the convenience of motor vibration signal measurement, more attention has been paid to fault detection methods based on vibration signal. As a nonstationary, nonlinear signal, vibration signal is highly susceptible to external noise interference. Variational mode decomposition (VMD) has a wide range of applications in the field of nonlinear signal analysis, but the processing ability to the signal is affected by the parameter setting. Hence, a Bayesian optimized adaptive parameter selection variational mode decomposition (BOAPS-VMD) signal processing method is proposed and applied to detect the ITSC in PMSM. Firstly, the motor vibration signal is decomposed through BOAPS-VMD, and the intrinsic mode functions (IMFs) are obtained. Secondly, the cumulative variance contribution rate (C-VCR) is applied to identify the IMFs that contain fault signature information. Finally, using Hilbert transform (HT) to further analyze the IMFs that are identified. The results are output in 3D time-frequency diagrams to enhance the representation of fault signatures. The effectiveness and accuracy of the BOAPS-VMD were validated through the finite element simulation and experiment. The study shows that the BOAPS-VMD effectively improves the modal mixing phenomenon, has better noise robustness, improves the accuracy of fault detection, and has better engineering applicability.

For the speed-tracking control of permanent magnet synchronous motor (PMSM) systems, fast dynamic response and high reference speed require a large transient current to provide sufficient electromagnetic torque. However, the excessive transient current may damage the drive circuit and threaten the system safety. Besides, it is also important to pay attention to the influence of multiple disturbances including system parameter uncertainties and unknown load torque variation. To solve these problems, we propose a nonlinear speed-tracking control approach for uncertain PMSM systems. More specifically, based on the cascade control structure and by introducing auxiliary smooth function, we first develop a nonlinear current-constrained controller (CCC) to guarantee the speed tracking, disturbance rejection, and overcurrent protection simultaneously. In order to improve the robustness of the desired performances, we further implement extended state observer (ESO) techniques to propose an ESO-based CCC (ESO-CCC). Finally, comparative experiments are implemented in a 5.5 kW PMSM platform to verify the performance of the proposed two controllers. [ABSTRACT FROM AUTHOR]