Sensorless Permanent Magnet Synchronous Motor Research Articles

Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM

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.

Research on a Sensorless ADRC Vector Control Method for a Permanent Magnet Synchronous Motor Based on the Luenberger Observer

A sensorless vector active disturbance rejection control (ADRC) method for permanent magnet synchronous motors (PMSM) utilizing a Luenberger observer is presented. This method aims to address the challenges associated with weak active disturbances, substantial steady-state speed amplitude fluctuations, and difficulty in achieving a balance between overshoot and speed control in the sensorless PMSM control system. Mathematical models of the Luenberger observer and the ADRC were analyzed, leading to the proposal of a second-order ADRC control method based on the Luenberger observer. A mathematical model of the permanent PMSM has been introduced. Additionally, the necessary conditions for the convergence of the Luenberger observer were derived and examined. The allowable range of error feedback gain values was determined, and rotor position data were acquired using a phase-locked loop. The principle of the ADRC was analyzed, and the ADRC simulation results, along with the PI simulation results, were detailed. When the target speed is 1000 r/min, the steady-state error and load disturbance resistance of the ADRC control method outperform those of the PI control method. Finally, the control method was experimentally tested on an STM32F4 chip, demonstrating the advantages of small steady-state error and strong active disturbance ability.

Design and Implementation of Improved Gate Driver Circuit for Sensorless Permanent Magnet Synchronous Motor Control

Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control.

Multi-Parameter Fuzzy-Based Neural Network Sensorless PMSM Iterative Learning Control Algorithm for Vibration Suppression of Ship Rim-Driven Thruster

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.

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.

Current Ripple Sampling based Angle Error Compensation for High-speed Permanent Magnet Synchronous Motor Sensorless Control Considering Inductance Saturation

Current Ripple Sampling based Angle Error Compensation for High-speed Permanent Magnet Synchronous Motor Sensorless Control Considering Inductance Saturation

High-speed Permanent Magnet Synchronous Motor Sensorless Control Based on Stator Current Prediction

High-speed Permanent Magnet Synchronous Motor Sensorless Control Based on Stator Current Prediction

Improvement Performances of the SMO Observer for PMSM Sensorless Control Based on DTC Strategy Using Simulated Annealing and Reinforcement Learning TD3 Agent

From the elements that contribute to increasing the reliability of the control system of a Permanent Magnet Synchronous Motor (PMSM), could be enumerated the replacement of the speed transducers with software-implemented speed observers. On this basis, this paper focuses on the sensorless regulation of a PMSM using a Direct Torque Control (DTC) type strategy, in which a speed observer is used in combination with a Reinforcement Learning-Twin Delayed Deep Deterministic Policy Gradient (RL-TD3) type agent to increase the accuracy of the PMSM rotor speed estimate. Simulated Annealing (SA) is also used to achieve superior performance of the velocity observer for optimal tuning of the PI-type velocity controller parameters. The latter can, after the training phase, provide correction signals to the speed observer so that the estimated speed is as close as possible to the estimated speed. The control structures, control algorithms, and operating equations of the PMSM, the control strategy of the DTC type, and the speed observer are presented in this paper. Numerical simulations carried out in the Matlab/Simulink programming environment validate the superiority of the PMSM rotor speed estimation performance in the case of the use of an RL-TD3-type agent in combination with a speed observer, compared to the case of the use of the speed observer alone.

Research on a Permanent Magnet Synchronous Motor Sensorless Anti-Disturbance Control Strategy Based on an Improved Sliding Mode Observer

This paper designs an improved sliding mode observer (ISMO) compound control scheme combined with a disturbance observer to solve the chattering and anti-disturbance problems of the traditional sliding mode observer (SMO) for permanent magnet synchronous motor (PMSM) in a sensorless control system. First, the sign function is replaced by an exponential type input function, and the fuzzy control rules are developed to automatically regulate the boundary layer control coefficient of the exponential input function, thereby changing the convergence characteristics of the exponential input function and improving the system observation accuracy. Then, the integral sliding mode surface and the quadratic radical term function of the square of the state variable are introduced to reduce system chattering. The proposed ISMO is proved using Lyapunov’s law to guarantee the whole system is stable. Based on the exponential input function and the integral sliding surface, an improved sliding mode disturbance observer (ISMDO) is constructed as a feed-forward compensator, which can optimize the dynamic performance of the improved observation system and ensure the strong robustness of the system by compensating the q-axis current. Finally, MATLAB/Simulink simulation and experimental platform verification have been carried out, which confirms the feasibility of the proposed composite control scheme.

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

For the problem of the rotor position estimation and control accuracy of permanent magnet synchronous motor (PMSM), this paper proposes a PMSM sensorless based on radial basis function (RBF) neural network optimized Automatic disturbance rejection control (RBF-ADRC) and strong tracking filter (STF) improved square root generalized fifth-order cubature Kalman filter (SGHCKF-STF). The Automatic disturbance rejection control (ADRC) has strong robustness, but there are many parameters and difficult to adjust. Now we use RBF neural network to adjust the parameters in ADRC online so as to improve the robustness and anti-disturbance ability. In order to improve the estimation accuracy of rotor position and speed, the orthogonal triangle (QR) decomposition and STF are introduced on the basis of the generalized fifth-order cubature Kalman filter (GHCKF) to design the SGHCKF-STF algorithm that not only ensure the non-positive nature of the covariance matrix but also improve the ability to cope with sudden changes in state during the filtering process. Experimental results show that the combination of RBF-ADRC and SGHCKF-STF improve the sensorless control effect of the PMSM to some extent.

Design of sensorless permanent magnet synchronous motor controller based on improved sliding mode observer

Because PMSM needs to install a position sensor in practical application, and the buffeting issue in sensorless control stems from an old SMO (sliding mode observer), an improved SMO is proposed. Stemming from the improvement of the switching function and low-pass filter of the traditional SMO, the chattering problem of the conventional SMO is optimized. Considering the reality that the traditional PI control strategy unable to respond accurately and quickly in the case of current coupling, the current complete decoupling integrates the traditional PI control method is adopted. It can speed up system response and improve system performance. The experimental consequence demonstrates that the improved SMO can effectively restrain the chattering phenomenon and realize the fast and precise control of the PMSM.

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent

Starting from the fact that in sensorless control systems of the Permanent Magnet Synchronous Motor (PMSM), the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, thus increasing ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the PMSM sensorless control system based on the LADRC controller with an ESO-/DO-type observer are presented, as is its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. To optimize the gain value of the DO-type observer, a computational intelligence algorithm such as the Ant Colony Algorithm (ACO) is used. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. In addition, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

Three‐phase current reconstruction method for permanent magnet synchronous motor based on current transformer sensors

AbstractThe position sensorless permanent magnet synchronous motor (PMSM), with its simple structure, is applied in the aerospace field with high reliability requirements. To drive PMSM safely, the three‐phase current measured from the driver needs to be isolated for the closed‐loop control. And then, the accuracy current measurement is crucial for achieving optimal motor performance. In the paper, current transformer (CT) with centre taps is employed to isolate and measure the high‐frequency current of the inverter switch. Additionally, an algorithm is proposed to reconstruct the switch current into the low‐frequency phase current. Subsequently, the measuring method and the algorithm are applied to an inverter platform, which drives a 100W PMSM. Finally, the reconstructed phase current waveform is compared with the measured phase current waveform, and the experimental results show that the above method is effective and feasible.

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

Multi-dimensional vector control for six-phase interior permanent magnet synchronous motor sensorless drive system

In order to realize high-performance control of six-phase interior permanent magnet synchronous motor (IPMSM), a multi-dimensional sensorless vector control scheme is discussed in this paper. Firstly, a new adaptive sliding mode observer (SMO) is designed to estimate the speed and rotor position information online, avoiding the phase delay caused by the traditional SMO method using the low-pass filter, and the detailed design method of observer parameters is given. In addition, the designed multi-dimensional vector control strategy fully exploits the advantages of multi-degree of freedom control for multi-phase motor drive system, considers the influence of x-y harmonic subspace, and sets the reference value of the current loop in harmonic subspace to zero to solve the stator current unbalance problem. Finally, the simulation model of the proposed control algorithm is built based on Matlab/Simulink simulation software, and the simulation results verify that the employed control technology has good control quality.

Sequence-Separation Complex-Vector Observer in Sensorless PMSM Drives for BEMF Trajectory Optimization

The back-electromotive force (BEMF)-based position estimation is attractive for sensorless permanent magnet synchronous motor (PMSM) drives. However, it shows weak robustness against the low-frequency disturbance induced by the practical nonideal conditions. This results in the trajectory deviation of the estimated BEMF, causing apparent position estimation error. To address this problem, this paper proposed a sequence-separation complex vector observer to optimize the trajectory of the estimated BEMF. First, a sequence separation structure is designed to remove the response of the equivalent negative sequence disturbance at the operation frequency. On this basis, the speed-adaptive disturbance compensation law is added into the observer to force the center of the estimated BEMF converging to the original point. Compared with the conventional method, the studied BEMF observer can ensure accurate position estimation by optimizing the trajectory of estimated BEMF. Finally, the studied algorithm is experimentally verified on a 2.2kW ARM-based PMSM platform.

Sensorless PMSM control system based on luenberger observer

At present, sensorless becomes a research hit for its lower cost and smaller size, among the present sensorless methods, Luenberger Observer has outstanding advantages for its dynamic performance, parameter robustness and high system stability. In this paper, the model of Luenberger Observer which is based on the state equation of permanent magnet synchronous motor (PMSM), and the changes of residence and induce which may lead to the error of the system are also analysed, in order to increase the accuracy of the system, current compensator is also added. In addition, 2 conditions which PMSM runs at low speed and high speed are analysed under the MATLAB model, the results show that the Luenberger Observer has better performance at high speed, but the noisy data of the useful signal is more at low speed.

High Frequency Torque Pulsation Suppression Strategy for Dual Winding Permanent Magnet Synchronous Motor Sensorless Control Based on Dual High Frequency Square Wave Injection Method

High Frequency Torque Pulsation Suppression Strategy for Dual Winding Permanent Magnet Synchronous Motor Sensorless Control Based on Dual High Frequency Square Wave Injection Method

Multi-Optimization Objective Online Tracking-Based Parameter Self-Tuning Method for Sensorless PMSM Drives

It is important to optimize control parameters for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. In this article, a multi-optimization objective online tracking (MOOT)-based parameter self-tuning method is proposed to improve the sensorless control performance when the high-frequency signal injection (HFSI) method is used, which involves both offline and online stages. By the parameter self-learning and the model analysis, the injection signal, the position observer, the speed, and current regulators are preliminarily designed offline to ensure that the system can start smoothly. Based on the parameter characteristics analysis, an adaptive law with the speed ripple and torque ripple as the optimization index is designed to realize the online tracking of control and observation parameters. To solve the issues of tuning delay and mistuning, a moving window-based error detection strategy is investigated, which improves the parameter self-tuning speed and accuracy. The proposed method can improve the steady- and transient-state performance and realize the sensorless debug-free operation for HFSI-based method. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.

High-Frequency Injection Angle Self-Adjustment Based Online Position Error Suppression Method for Sensorless PMSM Drives

In the high-frequency (HF) signal injection based position sensorless permanent magnet synchronous motor (PMSM) drives, there is an estimated position error caused by the cross-saturation effect and other factors. In order to suppress the position error, an HF injection angle self-adjustment based online suppression method is proposed in this letter. By tracking the sign of the increment of the absolute back electromotive force in the estimated frame, the criterion of the HF injection angle self-adjustment can be determined online and the position error can be mitigated effectively. This method can avoid the offline measurement or the variation of the operating state, which has a satisfactory suppression effect for the position error under different operating conditions. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.