Permanent Magnet Synchronous Motor Speed Research Articles

Research on linear/nonlinear active disturbance rejection switching control of PMSM speed servo system

An improved linear/nonlinear active disturbance rejection switching control strategy is proposed to enhance the dynamic response and disturbance suppression capabilities of the permanent magnet synchronous motor (PMSM) speed servo system. Firstly, facilitated by a tracking differentiator, rapid setpoint tracking is achieved without speed overshoot, effectively addressing the inherent conflict between fast system response and overshooting. Secondly, weight factors are introduced into the control law, and a novel switching criterion based on the step function is designed, enabling the system to smoothly transition between operating modes in the presence of disturbances with varying amplitudes. This facilitates accurate estimation and swift compensation for disturbances, thereby enhancing control precision and robustness. Additionally, a frequency domain analysis of the extended state observer is conducted, leading to the formulation of a parameter tuning scheme for the controller. Lastly, simulation tests validate the feasibility and effectiveness of this strategy as a PMSM speed controller. The system exhibits superior dynamic tracking and disturbance suppression performance over a wide speed range.

A Novel reaching law sliding mode control method of PMSM considering iron loss

The loss of motor is ignored in the traditional speed control, while the loss minimization control (LMC) takes the high efficiency of the motor and does not consider the speed performance. Therefore, with the iron loss torque as the bond between efficiency and dynamic performance, this paper presents a sliding mode control method under the loss minimization target to achieve the high efficiency and high dynamic performance of permanent magnet synchronous motor (PMSM). Firstly, a d-axis current compensation method based on Taylor series expansion of virtual power loss (VPL) is proposed to minimize the loss of PMSM. Secondly, to eliminate the influence of iron loss on the speed performance, a PMSM speed tracking model including iron loss is established. Then, an adaptive sliding mode reaching law with disturbance estimation (ASMRLDE) is proposed to avoid energy consumption and the sliding mode chattering of the system caused by excessive sliding mode switch gain by adjusting the speed at which the system state reaches the sliding surface. Finally, the superiority of the proposed sliding mode control method under the loss minimization target in terms of loss minimization and speed control of the motor is proved through multi-condition experiments.

Fixed-Time-Convergent Sliding Mode Control with Sliding Mode Observer for PMSM Speed Regulation.

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.

Comparative vector control study on speed of PMSM drive using sensorless and machine learning techniques: review

The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers.

Finite time fault estimation and fault‐tolerant control for a class of uncertain nonlinear systems based on cascaded observers

AbstractIn this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application.

Permanent magnet synchronous motor inter-turn short circuit diagnosis based on physical-data dual model under oil-drilling environment

Inter-turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motor (PMSM) have gained significant attention due to the growing demand for enhanced reliability and safety in actuation systems. This paper presents a adaptive fault diagnosis approach specifically designed for ITSC faults in PMSM used in oil-drilling applications, where sparse actual data and varying speed conditions pose considerable sparse training data and ITSC feature extraction challenges respectively. To address these challenges, we first construct a physical fault model of PMSM-ITSC and establish a simulation experiment platform to replicate downhole environments with high temperatures and rapidly changing PMSM speeds, ensuring a reliable data source for analysis. Subsequently, we propose a novel adaptive peak-to-peak self-finding method (APPS) that leverages frequency-domain prior knowledge to adaptively extract ITSC fault characteristics, even amidst drastic changes in PMSM speed. Furthermore, we introduce the time-sequence efficient moving window self-attention network (EMWSAN) data model for inferring the stator phase winding state, incorporating Half-sandwich and Cascaded windows group attention operations. This approach significantly reduces computational complexity and network parameters compared to traditional self-attention mechanisms. To expedite the fitting process, we apply transfer learning (TL) theory, transferring knowledge from the physical knowledge to the data model, enabling EMWSAN to be trained more efficiently. As a result, our proposed ITSC fault diagnosis scheme achieves an impressive 96.72% classification accuracy, achieving existing state-of-the-art methods.

Research on magnetic-fluid-thermal-stress multi-field bidirectional coupling of high speed permanent magnet synchronous motors

Due to the fact that high speed permanent magnet synchronous motors (HSPMSM) are direct drives instead of using gears, they have technical and economical advantages in many applications. In this paper, a 150 kW, 30,000 r/min HSPMSM was used as research object, for which a magnetic-fluid-thermal-stress multi-field coupling model was developed and solved. Motor losses were obtained from electromagnetic analysis and were applied as heat source. Subsequently, the temperature and fluid distribution were solved according to given heat source characteristics and cooling structure. Based on this, the rotor stress distribution influenced by the temperature load was further obtained. On the other hand, the effect of temperature change on conductor resistivity, permanent magnet remanence, cooling medium thermophysical properties, and loss characteristics, which in turn affect temperature and stress distribution, was considered. The bidirectional coupling of multi-fields was achieved by iterative calculations in the "forward" and "reverse" cycles. The results showed that the hotspot temperature increased by 14.19 % and the tangential stress in permanent magnet increased by 5.28 % compared to the unidirectional coupling. Finally, an experiment platform was built, and tests were conducted. The results showed that the temperature obtained by bidirectional coupling was closer to the experimental result.

Design of PMSM Dual-Loop Control Systems Integrating LADRC and PI Controllers via an Improved PSO Algorithm

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods.

Research on speed sensorless control strategy of permanent magnet synchronous motor based on fuzzy super-twisting sliding mode observer.

Aiming at the problems of poor adjustment effect, weak anti-disturbance ability, and low robustness of the traditional sliding mode algorithm for permanent magnet synchronous motor speed sensorless, a permanent magnet synchronous motor speed observation method combining super-twisting sliding mode control algorithm and fuzzy control is proposed to accelerate the convergence speed of the system and improve the anti-disturbance ability. Fuzzy rules are used to solve the problem of obtaining the upper bound of the boundary function in the super-twisting algorithm. Moreover, the fuzzy algorithm is used to output the variable sliding mode gain instead of the fixed sliding mode coefficient to improve the system robustness and suppress the jitter. The simulation results show that the overshoot of the control system is 4%, the lag time is not more than 0.003 ms, the speed error is not more than 1%, and the response and adjustment time is not more than 0.02 s. The proposed control strategy improves the tracking accuracy and response speed of the system, suppresses the sliding mode chattering, and enhances the anti-interference ability of the system.

Permanent magnet synchronous motor speed control system based on fractional order integral sliding mode control

Permanent magnet synchronous motor speed control system based on fractional order integral sliding mode control

Response Analysis and Stator Optimization of Ultrahigh-Speed PMSM for Fuel Cell Electric Air Compressor

For the requirement of the complex dynamic performance for the fuel cell vehicle (FCV), the wide operation range and rapid dynamic response ability are essential to the fuel cell electric air compressor, which are limited by the output torque of the permanent magnet synchronous motor (PMSM) commonly used in FCV. The purpose of this study is to improve the output torque of the stators and its rapid dynamic response capacity of PMSM using the mathematical model, multi-objective optimization and experimental validation. Firstly, a mathematical model of speed regulation response of ultra-high speed PMSM is established, which provides a basis for taguchi legal sub-optimization in this study. Secondly, stator parameters were selected as optimization variables based on Taguchi method, and the optimal combination of optimization variables was selected with the maximum electromagnetic torque, minimum cogging torque, minimum stator copper loss and minimum stator iron loss as optimization objectives. Finally, the effectiveness of the optimization method is verified by simulation analysis and bench test. At the target speed of 110652rpm, 124364rpm and 146876rpm, the overall response times before optimization are 1.9386s, 2.3902s, and 2.7844s. Respectively, the overall response times after optimization are 1.8604s, 2.2856s, and 2.6132s. The speed response time after optimization is 0.0782s, 0.1046s and 0.1712s less than before optimization, respectively.

Disturbances rejection optimization based on improved two-degree-of-freedom LADRC for permanent magnet synchronous motor systems

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.

Direct torque control of electric vehicle drives using hybrid techniques

Permanent magnet synchronous motors (PMSM) have the capability of delivering a high torque-to-current ratio, better efficiency and low noise. Because of the above-mentioned factors, PMSMs are commonly employed in variable speed drives, especially in electric vehicle (EV) applications. Without the usage of electromechanical devices, the conventional direct torque control (DTC) can control the speed and torque of PMSM. DTC is highly efficient, fast-tracking and provides smooth torque while limiting its ripple during transient periods. There are many benefits to using a DTC-controlled PMSM drive, including quick and reliable torque reaction, high-performance control speed, and enhanced performance. This research examines the use of the DTC approach to enhance the speed and torque behavior of PMSM. The jellyfish search optimizer (JSO) is used to adjust the DTC's responsiveness and tailor the controller's best gains. In order to train the adaptive neuro-fuzzy inference system (ANFIS) controller, JSO data are utilized. The simulation outcomes demonstrate that the proposed JSO-ANFIS controller achieves a minimal torque ripple of 0.26 Nm and preserves the speed with a harmonic error of 1.21% while contrasted to existing methods.

Performance analysis of FOC space vector modulation DCMLI driven PMSM drive

The effectiveness of a permanent magnet synchronous motor (PMSM) drive managed by an automatic voltage regulator (AVR) microcontroller using field oriented control (FOC) with space vector modulation (SVM) and a diode clamped multilevel inverter (DCMLI) is investigated. Due to its efficacy, FOC would be widely implemented for PMSM speed regulation. The primary drawbacks of a 3-phase classic bridge inverter appear to be reduced dv/dt stresses, lesser electromagnetic interference, and a relatively small rating, especially when compared to inverters. PMSMs have a better chance of being adopted in the automotive industry because of their compact size, high efficiency, and durability. The SVM idea states that an inverter's three driving signals are created simultaneously. Using MATLAB simulations, researchers looked into incorporating a DCMLI with a resistive load on an AVR microcontroller. Torque, current, and harmonic analysis were evaluated between the SVM and the inverter-driven PMSM drive in this research. In comparison to the prior art, the proposed PMSM drive has better speed and torque management, less output distortion, and less harmonic distortion.

Model-free fast integral terminal sliding-mode control method based on improved fast terminal sliding-mode observer for PMSM with unknown disturbances

This paper presents a novel model-free fast integral terminal sliding-mode control (MFFITSMC) method based on an improved fast terminal sliding-mode observer (IFTSMO) for permanent magnet synchronous motor (PMSM) drive system, which can effectively eliminate the impact caused by unknown disturbances, such as parameter perturbations and external disturbances. The PMSM mathematical model with unknown disturbances is first established, and the ultra-local model (ULM) of the PMSM speed loop is constructed. Next, the model-free fast integral terminal sliding-mode controller is designed in the speed loop based on the ULM. Then, the IFTSMO is designed to precisely estimate the unknown term of the ULM, and the estimated unknown term is fed back to the MFFITSMC controller to perform compensation for unknown disturbances in real time. Finally, compared with the proportional–integral (PI) control method and the conventional model-free sliding-mode control (MFSMC) method, the results of simulations and experiments demonstrate that the presented MFFITSMC method reduces the dependence on the precise model and achieves the purpose of anti-disturbance control of the PMSM drive system.

A novel sliding mode control with MRAS inertia identification for permanent magnet synchronous motors

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.

An Improved Integral Sliding Mode Control for PMSM Drives Based on New Variable Rate Reaching Law With Adaptive Reduced-Order PI Observer

This paper focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC+AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.

Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives

To eliminate the influence of the parameter mismatches and obtain high model quality, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed in this paper and applied to the permanent magnet synchronous motor (PMSM) speed control system. Since the ARMA model group which is a family of mathematical models containing AR, MA and ARMA structures considers operating states within several sampling periods to achieve better model accuracy, the plant is online designed as this type, and its coefficients are estimated according to the sampled data by the normalized least-mean-square (NLMS) algorithm with adaptive normalized step length to achieve improved model quality with reduced calculation burden. Compared with the ultra-local MF-PCC strategy, advantages of better stator current quality and robustness are demonstrated by the experimental results, as well as the reduced calculation burden compared with the recursive least square (RLS) algorithm used to estimate the coefficients.

Reinforcement Learning and Optimal Control of PMSM Speed Servo System

This article proposes a novel model-free optimal speed tracking control scheme for permanent magnet synchronous motors (PMSMs) through reinforcement learning (RL). To achieve the speed servo control, we formulate the linear quadratic regulator associated with the reduced-order model in the outer-loop controller design. Such a model is obtained in terms of singular perturbation theory, which enables the separation of slow and fast time-scale dynamics. Moreover, we develop an off-policy RL algorithm to iteratively approximate the ideal value of solution to the linear quadratic regulator without requiring any knowledge of model parameters of the PMSM and the measurement of the load torque. Both simulation and experimental tests are carried out to justify that the proposed control scheme realizes precision speed tracking performance and shape transient response in the presence of unknown model parameters.