Control Of Synchronous Motor Research Articles

Experimental comparison of the performance of PI and IP controllers for a field-oriented controlled permanent magnet synchronous motor drive

Experimental comparison of the performance of PI and IP controllers for a field-oriented controlled permanent magnet synchronous motor drive

Machine learning techniques for vector control of permanent magnet synchronous motor drives

Machine learning techniques for vector control of permanent magnet synchronous motor drives

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

Improvised direct torque control of a permanent magnet synchronous motor

Permanent magnet synchronous motor (PMSM) is widely used in different applications because of their different key operation features. But despite having important key features, PMSM suffers from torque ripple when simple control techniques like direct torque control (DTC) are employed. Several approaches have been raised by many researchers to lessen the ripple when DTC is used for controlling the PMSM drive. In this work, duty ratio-optimized DTC control, which has the same simplicity as conventional DTC (CDTC), and delay compensation for input signal prediction are proposed. In the proposed system, to determine the duty ratio, slope manipulation was made by considering the minimization of torque error and forcing the developed torque to be equal to the reference torque at the end of the span. An online slope determination approach was employed to compute the duty ratio. MATLAB 2021b is used for simulation purposes. The dynamics and steady-state performance of the proposed scheme were tested for both variable-load and variable-speed operations. In addition, the harmonic performance and ratio of harmonic power loss to total active power loss of the motor were evaluated. In general, the proposed scheme performs effectively in reducing steady-state ripple and harmonic.

Improved Finite Control Set Model Predictive Current Control for Permanent Magnet Synchronous Motor With Sliding Mode Observer

Improved Finite Control Set Model Predictive Current Control for Permanent Magnet Synchronous Motor With Sliding Mode Observer

Finite control set model-free predictive current control of permanent magnet synchronous motor

This thesis proposes finite control set model-free predictive current control (FCSMFPCC) algorithm of permanent magnet synchronous motor (PMSM). To address issue of degraded performance during motor operation due to parameter mismatch, a model-free predictive current control arithmetic is devised, which utilizes ultra-local model (ULM) of PMSM. This proposition takes sum of known and sealed disturbances of the system as an unknown quantity, and uses sliding mode observer (SMO) to take stock of sealed quantity. To address the issue of operation delay in predictive control, a two-step method is used to make up for the system. When using price function to choose the first-rank voltage vector, vector selection optimization link is added, which effectively simplifies the algorithm while optimizing the inverter switching action. The simulation outcomes highlight the advantages of the described control algorithm in terms of better dynamic response performance and stronger robustness.

Enhancement of Field Oriented Control for Permanent Magnetic Synchronous Motor using Ant Colony Optimization

Because of its frequent use in diverse systems, the PMSM drive must be controlled. Field-oriented control (FOC) based PMSM drive is modeled in the present work to optimize the torque and speed performance of the PMSM. The FOC is based on a dissociated speed and flux control approach, which controls the speed and flux of the PMSM independently. The standard Proportional Integrator Derivative (PID) controller regulates the speed in FOC, which is noted for its increased resilience in linear systems, however in nonlinear ones, the PID controller responds poorly to changes in the system’s variables. In this case, the best solutions are frequently based on optimization techniques that produce the controller’s gains in every period. Optimizing the PID’s behavior in response to the system’s nonlinear behavior. The novel proposed strategy for enhancing the gains of the PID controller by employing a cost function such as Integral Time Absolute Error (ITAE) is based on PID speed regulation and is optimized using the Ant Colony Optimization algorithm (ACO) for FOC. To confirm the strategy’s aims, the suggested method is implemented on Matlab/Simulink. The simulation results demonstrated the efficiency of the intelligent ACO-FOC control, which delivers good performance in terms of stability, rapidity, and torque fluctuations.

A High-Precision Position Estimation Method for Low Saliency Ratio Machine Based on Convolutional Neural Networks

Sensorless control of surface permanent magnet synchronous motor (SPMSM) faces great challenges in achieving high precision of rotor position estimation at low speed because the saliency of an SPMSM is only a minor secondary effect. In order to extract accurate position information for SPMSM with low and ultra-low saliency ratios, this paper presents a method that implements image recognition techniques with high-frequency (HF) rotating voltage vector injection. For building an image of the induced current vector, the information of the negative-sequence and the second harmonic of positive-sequence HF current signals in the stationary reference frame is extracted. Then, convolutional neural networks (CNN) are applied to the current vector images to establish the relationship between the vector image and the rotor position. Subsequently, the rotor position can be obtained from the result of image recognition from the neural network. Benefiting from CNN’s excellent performance in image classification, the proposed method is able to recognize subtle position signatures even under low saliency. This confirms the opportunity to increase position estimation accuracy for sensorless control of SPMSM. Finally, the experimental results have provided proof that the proposed method has significantly enhanced estimation accuracy.

Study on QEMF Model and Adaptive Full-Order Observer Design for Universal Sensorless Control of IPMSMs

Quadratic extended electromotive force (QEMF) model enabled the use of traditional high-speed adaptive estimation methods combined to high-frequency signal injection (HFSI) for full-range sensorless position control of interior permanent magnet synchronous motors (IPMSMs). However, the first QEMF model presented in literature only works with HFSI in the q-axis, due to the QEMF being a function of the q-axis current derivative. The q-axis HFSI is known to produce undesired torque ripple. Furthermore, q-axis signal injection can be insufficient for low-speed position estimation in IPMSMs with low salience. A recent study demonstrated that the QEMF concept can be modeled as a function of the d-axis current derivative. In this paper, the influence of d-axis HFSI on QEMF is investigated and compared to the q-axis HFSI method. Furthermore, the electromotive force based observers are usually designed for medium to high-speed operation. Here, the adaptive full-order observer is adapted in order to achieve universal sensorless control through QEMF based d-axis HFSI. The state observer and adaptive law are designed by a cascade methodology, which guarantees accurate EEMF estimation and robustness throughout the entire operating speed range. Experimental results are presented in order to validate the proposed method and analysis under full-range sensorless control.

Hardware-in-the-Loop Simulation of Synchronous Motion Control for Dual-Motor Driving Steer-by-Wire System Integrated With Optimal Finite Preview Path-Tracking Control

Hardware-in-the-Loop Simulation of Synchronous Motion Control for Dual-Motor Driving Steer-by-Wire System Integrated With Optimal Finite Preview Path-Tracking Control

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.

An improved sliding mode speed controller for PMSM based on new terminal reaching law with generalized proportional integral observer

A compound sliding mode controller for permanent magnet synchronous motor (PMSM) based on an adaptive terminal sliding mode reaching law with generalized proportional integral observer is proposed. First, an adaptive terminal reaching law is proposed to solve the defects of large chattering in commonly used sliding mode control, which consists of a piecewise function and a terminal term of state variables. The reaching law could adjust the gain according to the distance between the system state and the equilibrium point. On the premise of reducing chattering, a higher reaching speed is guaranteed. The convergence time has an upper bound and is not concerned with the initial value of the sliding mode variable. Based on the reaching law, an adaptive sliding mode controller is established. Second, to reduce the sliding mode gain and enhance the antidisturbance ability of the PMSM, a generalized proportional integral observer is established to evaluate the perturbations in PMSM and compensate them to the speed controller in real time. Simulations and experiments verify that the new compound control method has a faster-approaching speed, smaller chattering, and stronger antidisturbance ability than other methods.

Sensorless control of permanent magnet synchronous motor based on adaptive enhanced extended state observer

SummaryThis article proposes an enhanced adaptive disturbance rejection control strategy for sensorless control of permanent magnet synchronous motor. The traditional fixed bandwidth quadrature phase‐locked loop (Q‐PLL) is insufficient to meet system dynamics in the event of sudden speed changes or motor disturbances, resulting in a significant decrease in control performance. Meanwhile, conventional extended state observers usually rely on a fixed high gain for fast convergence, which raises concerns about their sensitivity to noise. To overcome these challenges, the proposed method introduces a third‐order adaptive cascaded linear extended state observer (ACLESO) in place of the conventional PLL to accurately estimate the speed, phase, and overall disturbances, ensuring timely and accurate estimation of the total disturbance. Then, a speed controller based on linear state error feedback control law (LSEFCL) was designed to enhance the dynamic performance of the system. Based on the stability analysis using the Lyapunov function, finally, extensive experimental tests were carried out to verify the validity of the proposed method.

Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss

The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed.

Research on Characteristic Model-based Adaptive Control of High-speed Permanent Magnet Synchronous Motor With Time Delay

Research on Characteristic Model-based Adaptive Control of High-speed Permanent Magnet Synchronous Motor With Time Delay

Displacement Self-Sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on Least Square Support Vector Machine Optimized by Improved NSGA-II

In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. Firstly, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived. Secondly, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out. The feasibility and reliability of the proposed displacement self-sensing method are verified.

Non-singular fast terminal sliding mode control of permanent magnet synchronous motor for automatic guided vehicle based on disturbance compensation

In the smart factory, the loading and unloading of goods by automatic guided vehicle in the driving state will cause speed fluctuations. To this problem, this article takes the permanent magnet synchronous motor of automatic guided vehicle as the research object and studies the motor speed control of automatic guided vehicle when the load changes. This article presents a non-singular fast terminal sliding mode control strategy for permanent magnet synchronous motor based on disturbance feed-forward compensation. In order to solve the problem of chattering and slow reaching the speed of sliding mode control, this article designs an adaptive exponential reaching law, which can not only suppress sliding mode chattering but also adjust the reaching speed adaptively according to the system state. In order to solve the problem of external load disturbance, this article designs a sliding mode disturbance observer, which suppresses the influence of load disturbance on speed by compensating the observed values feed-forward into the current loop. Compared with proportional–integral and sliding mode control, the control strategy adopted in this article reduces the speed fluctuation by 3.12% and 2.00%, and the response time is reduced by 67.9% and 33.3%, respectively—finally, the load mutation experiments of automatic guided vehicle with permanent magnet synchronous motor as the driving motor is carried out. The experimental results further verify the effectiveness of the control strategy. Simulation and experimental results show that the proposed control strategy has better anti-interference ability and faster response speed.

PMSM Field-Oriented Control with Independent Speed and Flux Controllers for Continuous Operation under Open-Circuit Fault at Light Load Conditions

Presented in this article is a permanent magnet synchronous motor (PMSM) control under open-circuit fault (OCF) operation using field-oriented control (FOC) with independent speed and flux controllers. The independent control allows the motor to operate efficiently under varying conditions. A simplified control approach is employed to control the PMSM under the OCF situation; the actual flux and torque of the PMSM are directly measured by the stator currents, eliminating the need for estimators or phase-locked-loop (PLL) systems. Matlab/Simulink is employed for the simulation, while hardware experiments are conducted using a dSPACE DS1104. The simulation and the hardware results demonstrate the control method’s effectiveness in maintaining continuous motor operation during OCF, its robustness against OCF conditions, and its ability to adapt under varying conditions, including speed, flux, and load torque change.

An enhanced direct torque control strategy with composite controller for permanent magnet synchronous motor

AbstractDirect torque control (DTC) based on sliding mode control (SMC) has been widely applied to permanent magnet synchronous motor (PMSM) drives for torque ripple reduction and flux tracking. However, the control performance may be degraded due to disturbances such as variations in motor parameters or uncertainties. In this paper, a DTC strategy with an improved composite SMC controller is proposed for the PMSM drive system to enhance its performance under various disturbances. The speed controller is first designed based on a proposed adaptive reaching law (ARL) to eliminate chattering that appears in the conventional SMC, accelerate convergence, improve tracking performance, and reduce torque ripples. Then, an enhanced extended sliding mode disturbance observer (E2SMDO) is developed to estimate the lumped disturbances of the PMSM drive system in real time and compensate for a feedforward to the speed controller. By combining the ARL and E2SMDO, a composite controller (denoted ARL + E2SMDO) is established, whose stability is proven through the Lyapunov theory. The effectiveness of the proposed method is validated by simulations and experiments. The results of both simulations and experiments demonstrate that the composite ARL + E2SMDO controller has a fast dynamic response, reduced torque ripples, strong robustness, and good anti‐disturbance compared to other conventional methods.

Online Inductance Identification of Permanent Magnet Synchronous Motors Independent of Rotor Position Information

Sensorless control of permanent magnet synchronous motors is preferable in some applications due to cost and mounting space concerns. The performance of most existing position estimation methods greatly depends on the accuracy of the motor inductance. As the estimated position should not be involved in the parameter identification process in a sensorless control system, an online inductance identification method independent of the rotor position information is developed in this paper. The proposed method utilizes the recursive least square algorithm and the particle swarm optimization algorithm to realize real-time identification of the inductance along the direct axis and the quadrature axis, respectively, based on the deduced parametric equations without position information. The proposed method is efficient enough to be implemented within 0.2 ms and does not introduce any additional signal injection. A test bench is built to validate the characteristics of the method, and the experimental results show that the identified inductance can converge to the actual value rapidly and is robust to changes in the initial values and stator current. With the proposed method, accurate estimation of the rotor position and speed can be obtained using traditional model-based position estimators, and the stability of the sensorless control system can be improved significantly.