Permanent Magnet Synchronous Motor Position Research Articles

Design and Performance Analysis of an Outer-Rotor PMaSynRM

Permanent magnet synchronous motors (PMSMs) are consciously used as traction motors in electric vehicles (EVs) and hybrid electric vehicles (HEVs). The rotor position (inner-rotor, outer-rotor) and topology of PMSMs significantly impact their torque profile, efficiency, and demagnetization characteristics. This article focuses on designing an outer-rotor permanent-magnet-assisted synchronous reluctance motor (PMaSynRM) under traction motor requirements and investigating its electromagnetic performance using the finite element method (FEM). This study's primary challenge is achieving optimal machine performance, considering high maximum torque and the risk of demagnetization at low levels. The design, derived from analytical calculations, was subjected to Finite Element Method (FEM) analyses. These analyses investigated motor performance in terms of efficiency, the ability to generate torque at different drive currents, and the risk of demagnetization. As a result of the study, the proposed PMaSynRM design was obtained that provides an adequate demagnetization performance even under 300% loading, has a maximum torque exceeding 35 Nm, and achieves an efficiency of 90% at rated conditions.

A Fractional-Order ADRC Architecture for a PMSM Position Servo System with Improved Disturbance Rejection

This paper proposes an active disturbance rejection control (ADRC) architecture for a permanent magnet synchronous motor (PMSM) position servo system. The presented method achieved enhanced tracking and disturbance rejection performance with a limited observer bandwidth. The model-aided extended state observer (MESO)-based ADRC was designed for the current, speed, and position loops of the PMSM position servo system. By integrating known plant information, the MESO improved disturbance estimation with a limited observer bandwidth without amplifying the noise. Additionally, a fractional-order proportional-derivative (FOPD) controller was designed as the feedback controller for the speed loop to further enhance the disturbance rejection. A simulation and experimental tests were conducted on a PMSM servo platform. The results demonstrate not only that the proposed method achieved superior tracking performance but also that the position error of the proposed strategy decreases to 2.25% when the constant disturbance was input, significantly improving the disturbance rejection performance.

Enhanced Position Estimation of PMSM Using the Luenberger Observer and PLL Algorithm: Design and Simulation Study

This study proposes an enhanced method for estimating the position of a permanent magnet synchronous motor (PMSM) using the Luenberger observer and phase-locked loop (PLL) algorithm. The main contribution to this research is the use of two low-pass filters (LPF) at the input of the PLL, which results in softer position reconstruction compared with conventional PLL. The proposed method is designed and simulated using the MATLAB/Simulink platform. The performance of the proposed method was evaluated and compared with conventional PLL and PLL with one LPF using several performance metrics such as estimation accuracy, convergence time, and stability. Simulation results show that the proposed method achieves better estimation accuracy and higher stability compared with the other methods. Additionally, the proposed method is robust to various disturbances such as load torque and parameter variations. Overall, the proposed method offers an effective and efficient solution for estimating the position of PMSM in various industrial applications.

Study of External Energy Gun Automata Firing Control Based on Permanent Magnet Synchronous Motor

In this paper, an external energy gun automata firing control method based on permanent magnet synchronous motor (PMSM) is presented. Firstly, on the basis of PMSM voltage equation and coordinate transform, current loop decoupling controllers are derived via the expected current closed loop time constant. The d-axis current and q-axis current can be tuned independently. Through the given mid-frequency interval criterion, the PMSM speed control method can be designed. Secondly, in order to implement the external energy gun automata firing control, the combined angle according to PMSM position and its over one circle count is solved first. The position controller is designed via Lyapunov stable theory. Last, an experiment is executed on a real external energy gun automaton and a PMSM position control system to verify the proposed method. The firing rate and firing shells length can be controlled. In order to decrease the firing rate without decrease PMSM speed, multiple single shells fired with given interval method can also be established and achieved. The experiment results verify the effectiveness of the proposed approach.

Research on Sensorless Control of Built-in Permanent Magnet Synchronous Motor Based on Extended Kalman Filter Algorithm

The permanent magnet synchronous motor (PMSM) is a system of complex interior structure coupling and nonlinear, variable parameters. In order to track and observe the position and speed of PMSM in real time, the extended Kalman filter (EKF) algorithm is used in this paper to track the speed and position of the built-in PMSM. The influence of the Q and R covariance matrix on the observation effect is studied by using the control variable method of trial and error. The reliability of the EKF on the observation function of velocity position is verified by MATLAB simulation.

A novel adaptive PD-type iterative learning control of the PMSM servo system with the friction uncertainty in low speeds.

High precision demands in a large number of emerging robotic applications strengthened the role of the modern control laws in the position control of the Permanent Magnet Synchronous Motor (PMSM) servo system. This paper proposes a learning-based adaptive control approach to improve the PMSM position tracking in the presence of the friction uncertainty. In contrast to most of the reported works considering the servos operating at high speeds, this paper focuses on low speeds in which the friction stemmed deteriorations become more obvious. In this paper firstly, a servo model involving the Stribeck friction dynamics is formulated, and the unknown friction parameters are identified by a genetic algorithm from the offline data. Then, a feedforward controller is designed to inject the friction information into the loop and eliminate it before causing performance degradations. Since the friction is a kind of disturbance and leads to uncertainties having time-varying characters, an Adaptive Proportional Derivative (APD) type Iterative Learning Controller (ILC) named as the APD-ILC is designed to mitigate the friction effects. Finally, the proposed control approach is simulated in MATLAB/Simulink environment and it is compared with the conventional Proportional Integral Derivative (PID) controller, Proportional ILC (P-ILC), and Proportional Derivative ILC (PD-ILC) algorithms. The results confirm that the proposed APD-ILC significantly lessens the effects of the friction and thus noticeably improves the control performance in the low speeds of the PMSM.

A Novel Position Speed Integrated Sliding Mode Variable Structure Controller for Position Control of PMSM

Improving the positioning response is one of the key goals for permanent magnet synchronous motor position servo systems. The sliding mode variable structure controller (SMVSC) is an effective method due to the advantages of strong robustness, fast response, and easy practical implementation. However, the existing SMVSCs have difficulties in optimizing the position tracking response speed and chattering performance at the same time. This article proposes an improved position speed integrated SMVSC (ISMVSC) with a new variable exponential reaching law based on position error and a saturation function, and a speed-limiting method using speed error and trajectory planning. The general design principle is given and various tests are carried out to verify the effectiveness of the proposed method. The experimental results show that compared with traditional methods, the ISMVSC can improve the positioning response speed and reduce chattering problems at the same time; it also exhibits a stronger transient speed-limiting ability. All these advantages make the proposed controller a superior method to achieve high-response positioning control of the servo system that performs point-to-point motion.

Estimated Position Error Compensation Method Considering Impact of Speed and Load in Permanent Magnet Synchronous Motor Position Sensorless Control Based on High-Frequency Voltage Injection

Estimated Position Error Compensation Method Considering Impact of Speed and Load in Permanent Magnet Synchronous Motor Position Sensorless Control Based on High-Frequency Voltage Injection

Experimental output regulation of permanent magnet synchronous motor position servo system: An internal model-based two-step control approach

It is well known that the position tracking control problem of permanent magnet synchronous motor (PMSM) is a challenging task when parameter uncertainties and time-varying load torque disturbances are taken into account. In this paper, a two-step controller design strategy composed of triple-loop control and internal model control is proposed to achieve a wide range of position tracking control of PMSM, where the reference position can be a relatively large value. In contrast, only local position tracking control problem has been solved by an internal model approach from output regulation theory in the recent work. In addition to the simulation results, the first experimental study is conducted to demonstrate the effectiveness of the proposed two-step control method. It is worth mentioning that our design can guarantee precise position tracking with a wide position range despite parameter uncertainties and time-varying load torque disturbances.

Design of PD-type second-order ILC law for PMSM servo position control

An iterative learning control method is better to control the high-order nonlinear strong coupling and external disturbances in the permanent magnet synchronous motor position servo system. This paper proposes a second-order P.D. type iterative learning control strategy, which can effectively achieve the optimal tracking control algorithm. By using the generalization of the Young inequality of convolution, the Lebesgue-p norm is obtained under the adequate condition that the tracking error converges monotonously. Furthermore, the convergence rate of second-order iterative learning control is compared, and it is proved by the mathematical knowledge that second-order iterative learning control is much better than a first-order iterative learning control under satisfactory conditions. Simulation results show that the effectiveness of the proposed algorithm is better than that of the traditional method, and the error of the iterative learning control strategy is smaller but with higher accuracy.

An adaptive PID-type sliding mode learning compensation of torque ripple in PMSM position servo systems towards energy efficiency

In this paper, for periodic motion tasks, combining adaptive PID-type sliding mode control (APIDSMC), model reference adaptive control (MRAC) and periodic adaptive learning control (PALC), a novel APIDSMC-PALC compensation approach towards energy efficiency is proposed to suppress the influence of torque ripple in permanent magnet synchronous motor (PMSM) servo systems. Using particle swarm optimization (PSO) algorithm, the equivalent control gain of sliding mode control is optimized to achieve energy efficiency during long-term operation. The objective of the proposed ripple compensation algorithm is to accurately approximate two dominant harmonic amplitudes in the torque ripple and generate an additional control effort for ripple compensation. Simulation and testbed experimental results demonstrate that with the proposed ripple compensation algorithm, the objective of excellent position tracking performance is ensured, and the energy efficiency is improved.

T-Source Inverter-Based Sensorless Speed Control for Permanent Magnet Synchronous Motor

Abstract Permanent magnet synchronous motors (PMSM) are used commonly in numerous industrial applications, for instance, in mechatronics, vacuum pumps, energy storage flywheels, automotive, centrifugal compressors, and robotics. Nowadays, the sensorless speed control of PMSM is getting more attention, and several studies are progressing because of its low cost and reliable features. Normally, the speed control methods in PMSM are achieved with the help of sensors for position or speed estimation and control. But, these sensors are easily prone to breakage. Also, the flexibility towards parameter variations is poor in the conventional speed control methods. So, a sensorless T-source inverter-based PMSM drive that integrates the Proportional Integral (PI) controller with an adaptive mechanism to cope with the time-varying system parameters is proposed in this article. A sensorless module, namely, a model reference adaptive system (MRAS), is employed to estimate the rotor position of PMSM based on its performance characteristics Simulation results are illustrated to investigate the performance of the proposed method with different speeds under no load and loaded conditions. Moreover, the proposed approach not only minimizes the cost and size of the motor but also maximizes the reliability and accuracy.

Research on Sensorless Control of Permanent Magnet Synchronous Motor

For the information of the rotor position of the permanent magnet synchronous motor position sensorless control system, the calculation method of different rotor positions of the motor under high speed and low speed is proposed. In the low speed state, the rotor information cannot be extracted because the back electromotive force on the stator is too small. The rotor position information can be obtained by the high-frequency voltage injection method. When the motor is in the medium-high speed operation state, the sliding mode observer method is introduced, and the rotor position information of the motor can be estimated. Finally, the rotor speed estimated by the sliding mode observer is obtained by simulation. The accuracy of the rotor position.

Position and Speed Estimation of PMSMs Using Gaussian Processes

In this paper, we present a novel low-cost technique to estimate both the position and the speed of a permanent magnet synchronous motor (PMSM) by sensing its stray magnetic field. At an optimal radial and axial distance, a low-cost magnetoresistive sensor is placed outside at the back of the PMSM. The magnetic field values are recorded for one complete rotor revolution at a resolution of less than a degree for different speeds of operation. Gaussian Processes (GPs) are employed to find a mapping function between the magnetic field values of the permanent magnet and the absolute angular positions. Then, by using the learned GP as a measurement function with an Extended Kalman Filter (EKF), both the angular position and speed of a PMSM can be estimated efficiently. Furthermore, we observe that the magnetic field depends not only on the position but also on the angular speed. To address this, we extend the GP to incorporate multivariate inputs. In order to take the periodicity of the data into account, we employ a periodic kernel for the GP. Additionally, a linear basis function model (LBFM) is introduced to incorporate more training points while maintaining the same computational cost. The GP and LBFM approaches are evaluated with data from a real PMSM experiment setup, and the accuracy of the position and speed state estimation is verified against a high-resolution optical encoder used as ground truth.

Research on PMSM Low-speed Sensorless Control Strategy based on Rotating High Frequency Injection Method

In this paper, by studying the Permanent Magnet Synchronous Motor (PMSM) position sensorless control strategy, a method for error compensation of current responsed by high frequency error signal is proposed. Since the high-frequency carrier current contained in the stator current causes the erroneous adjustment of the current regulator, the control system contains a high-frequency error signal, which affects the motor rotor position and speed estimation accuracy. The high-frequency error voltage signal is extracted in the stationaryα − β coordinate system, and the high-frequency error response current in the stator current is estimated, and the estimation system is compensated. The PMSM position sensorless control system based on the rotating high frequency voltage injection method and the Matlab-Simulink system model of the error compensation are established. The results show that the speed and position estimation are more accurate after error compensation, and the steady-state error is smaller, which improves the smoothness of the motor at low speed.

PSO-Based HMLP Network Controller for Speed Sensorless Control of PMSM

A sensorless control of permanent magnet synchronous motor (PMSM) by using model reference adaptive system (MRAS) and artificial neural network is presents in this paper. The objective for this study is to improve the speed and position estimation of the PMSM rotor which used the MRAS with the conventional PI controller. In this study, the multilayer perceptron (MLP) network and hybrid multilayer perceptron (HMLP) network from family of artificial neural network (ANN) were evaluated. Before the controller can be used, the value of weight between the network layers need to be optimised. To train the controller weight, particle swarm optimization (PSO) is used. Finally, the proposed method is evaluated by comparing with the proposed controller in controlling the speed and position of PMSM. Simulation results under various speed and load conditions indicated that the PSO-HMLP network controller achieved well results than the compared controller in terms transient response and overall system analysis.

A New Full‐Order Sliding Mode Observer Based Rotor Speed and Stator Resistance Estimation for Sensorless Vector Controlled Pmsm Drives

AbstractSensorless control of a permanent magnetsynchronous motor (PMSM) at low speed remains a challenging task. In this paper, a sensorless vector control of PMSM using a new structure of a sliding mode observer (SMO) is proposed. To remove the mechanical sensors, a full‐order (FO‐SMO) is built to estimate the rotor position and speed of PMSM drives. The FO‐SMO, which replaces a sign function by a sigmoid function, can reduce the chattering phenomenon. In order to overcome time delay, we cancel the low pass filter. This sensorless speed control shows great sensitivity to stator resistance and system noise. To improve the robustness of sensorless vector control, a full‐order SMO technique has been used for stator resistance estimation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. The validity of the proposed FO‐SMO with a 1.1 kw low‐speed PMSM sensorless vector control is demonstrated by experiments. In this paper, experimental results for FO‐SMO, back‐EMF SMO and MRAS techniques were obtained with fixed point DSP‐based (TMS320F240).

Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

A digital signal processor (DSP)-based intelligent sliding-mode control (SMC) is proposed for the position control of a six-phase permanent magnet synchronous motor (PMSM) drive system installed in an electric power steering (EPS) system in this study. First, the dynamic mathematical model of the EPS system is derived by the Lagrangian dynamics. Since the EPS system is a nonlinear and time-varying system, the control accuracy is very sensitive to the parameter variations and external disturbances. Therefore, a SMC is developed for the position control of the EPS system. However, the upper bound of the uncertainties is difficult to obtain in advance and the choice of switching control gain in SMC is vital but time-consuming and may cause undesired chattering phenomenon. Hence, an intelligent SMC with a novel recurrent wavelet fuzzy neural network (ISMC-RWFNN) is proposed, in which a recurrent wavelet fuzzy neural network (RWFNN) is adopted as an uncertainty estimator to overcome the aforementioned disadvantage of SMC. Moreover, a robust compensator is employed to reduce the estimation error. In addition, the adaptive learning algorithms for the online training of the RWFNN are derived using the Lyapunov theorem and Taylor series. Finally, the proposed ISMC-RWFNN to control the position of a six-phase PMSM drive system for the EPS system is implemented in a 32-bit floating-point DSP, and some experimental results are provided to verify its effectiveness.

Robust guaranteed cost ILC with dynamic feedforward and disturbance compensation for accurate PMSM position control

This contribution presents a robust ILC control design based on guaranteed costs. By combining this ILC design with dynamic feedforward control and an observer-based disturbance compensation, the initial tracking errors in an early learning stage can be reduced. The benefits of the proposed design approach are pointed out at the example of a robust position control of a Permanent Magnet Synchronous Motor (PMSM), which is subject to uncertain model parameters. The paper is concluded with convincing experimental results from a dedicated test rig. Moreover, a comparison with a classical observer-based tracking control is provided.

Adaptive Backstepping Control of Six‐Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

AbstractAn adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high‐performance six‐phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field‐oriented six‐phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six‐phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six‐phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32‐bit floating‐point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.