Speed Estimation Of Motor Research Articles

FPGA-Based High-Frequency Voltage Injection Sensorless Control with Novel Rotor Position Estimation Extraction for Permanent Magnet Synchronous Motor

This study developed a realization of sensorless control for a permanent magnet synchronous motor (PMSM) using a field-programmable gate array (FPGA). Both position and speed were estimated using a high-frequency (HF) injection scheme. Accurate estimation is essential to ensure the proper functioning of sensorless motor control. To improve the estimation accuracy of the rotor position and reduce the motor speed ripple found in conventional methods, a new extraction strategy for estimating the rotor position and motor speed is proposed. First, signal modulation compensation was applied to expand the information of the error function in order to provide more accurate data to the tracking loop system for rotor position extraction. Second, to minimize the motor speed ripple caused by the HF injection, motor speed estimation was performed after obtaining the rotor position information using a differential equation with a low-pass filter (LPF) to avoid the direct effect of the injected signal. Verified experimentally, the results showed that the rotor position error did not exceed 10 el.deg, so these methods effectively reduce the rotor position estimation error by about 30%, along with the motor speed ripple. Therefore, better performance in sensorless PMSM control can be achieved in motor control applications.

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage-Current Sensors.

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets (using voltage-current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally.

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.

Speed Estimation of Induction Motor at Low and Zero Speed using High Frequency Signal Injection for Rotor Slot Harmonics Detection

Rotor position estimation based on high frequency signal injection method represents the interesting way to provide the high performances of the AC drives sensorless controlled at low or near zero speed range. So, this method requires the existence of saliencies inside the IM particularly the presence of the slots where their design constitutes the main advantageous to exploit its effect for rotor speed monitoring. First, the present paper exposes a model of the induction machine including slotting effects which it can be exploited in order to extract the harmonic saliency created by rotor slotting. Second, the high frequency signal injection method is used in order to allow tracking the characteristic harmonic saliency due to rotor slotting from which the rotor position and speed may be estimated. A High frequency voltage signal injection creates a high frequency current modulated by the saliencies presented inside the machine. To extract information about rotor position, the resulting stator current treads to heterodyning process to eliminate the disturbance components and subsequently the rotor position may be extracted by a closed-loop observer. Simulation results are shown in order to verify the validity of the proposal and to confirm the effectiveness of estimation method at zero frequency.

Robust speed estimation of an induction motor under the conditions of rotor time constant variability due to the rotor deep-bar effect

Robust speed estimation of an induction motor under the conditions of rotor time constant variability due to the rotor deep-bar effect

Super-Twisting MRAS Observer-Based Non-linear Direct Flux and Torque Control for Induction Motor Drives

Abstract This research paper proposes a novel design of an efficient combined sliding mode observer (SMO) for induction motor flux and speed estimation. The suggested sensorless technique employs the sliding mode’s second-order approach using a model reference adaptive system (MRAS). The second-order super-twisting control method is free-chattering, which lowers the chattering effect while preserving the same good features as the first-order sliding mode control (SMC). In addition, the conjunction with the MRAS as a separated speed estimator can raise the accuracy and make the observer immune to speed fluctuations, particularly for low-speed applications. Furthermore, in order to achieve effective decoupled flux–torque control, the super-twisting algorithm (STA) was combined with a non-linear feedback linearisation controller for the inner control loop construction. This strategy can boost the control system’s stability and robustness against external disturbances and modelling uncertainty. The performance analysis of the suggested methods has been carried out via simulation and experimental validation utilizing MATLAB/Simulink with the dSpace 1104 real-time interface.

Improving Regenerating Mode Operation of MRAS-Based Induction Motor Speed Estimation Using the Multilayer Technique

Improving Regenerating Mode Operation of MRAS-Based Induction Motor Speed Estimation Using the Multilayer Technique

Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO

Here, for controlling a high-speed flywheel permanent magnet synchronous motor (HSPMSM), a position sensorless control method for estimation of motor rotor position and speed is proposed to address the problems faced by mechanical position sensors of high cost, large size, and poor interference immunity. The extended Kalman filter (EKF) has difficulty obtaining the optimal covariance matrix when performing state estimation. Therefore, the particle swarm algorithm (PSO) with an immune mechanism is used to optimize the covariance matrix of the EKF. However, the EKF algorithm makes the system less robust due to its delay effect. Based on the traditional sliding mode control rate, the exponential convergence law is improved, and the continuous function sat(s) is used instead of the symbolic function sgn(s). This improves the convergence law and proves the asymptotic stability of the designed sliding mode variable structure controller based on Lyapunov’s stability theorem. Then, the novel control law is applied to the sliding mode surface (SMS). An ordinary sliding mode controller (OSMC) using a linear sliding mode controller (LSMC), a global sliding mode controller (GSMC) using a global sliding mode surface (GSMS), and an integral sliding mode controller (ISMC) using an integral sliding mode surface (ISMS) are designed for improving control. Joint simulation in MATLAB and Simulink verifies that the optimized EKF based on the immune PSO can improve precision and accuracy for controlling the electronic rotor position and speed. Comparing the new sliding mode controller with a traditional PI controller reveals that the proposed system has stronger resistance to load disturbance and better robustness.

Sensorless model predictive control based on I‐f integrated sliding mode observer for surface permanent magnet synchronous motor

SummaryDue to the limited fast dynamic response of the dual proportional‐integral (PI) control strategy in sensorless speed control, a novel composite control strategy is proposed for sensorless control of the full speed range of the surface‐mounted permanent magnet synchronous motor. This strategy combines dual closed‐loop model predictive control (MPC) with a sliding mode observer (SMO) and a constant current‐frequency ratio (I‐f). The I‐f control scheme is employed in the low‐speed range, while the SMO is utilized for motor speed and rotor position estimation in the medium and high‐speed ranges, enabling the dual‐loop MPC. Furthermore, a new smooth switching method is proposed for transitioning between the two control methods. In addition, the MPC outer loop parameters were thoroughly analyzed and optimized for better dynamic performance of sensorless control. The steady‐state performance of this control strategy is compared to PI control and improved the dynamic responsiveness of the system. Experiments have verified the feasibility of this strategy. Under the control of the MPC outer loop, the steady‐state speed fluctuation of the motor is within 10 r/min. Compared to PI control, the motor settling time was reduced by 17% and 60% during the start‐up phase and after a sudden speed change, respectively.

Multiparameter Estimation-Based Sensorless Adaptive Direct Voltage MTPA Control for IPMSM Using Fuzzy Logic MRAS

This paper introduces a parameter-estimation-based sensorless adaptive direct voltage maximum torque per ampere (MTPA) control strategy for interior permanent magnet synchronous machines (IPMSMs). In direct voltage control, the motor’s electrical parameters, speed, and rotor position are of great significance. Thus, any mismatch in these parameters or failure to acquire accurate speed or position information leads to a significant deviation in the MTPA trajectory, causing high current consumption and hence affecting the performance of the entire control system. In view of this problem, a fuzzy logic control-based cascaded model reference adaptive system (FLC-MRAS) is introduced to mitigate the effect of parameter variation on the tracking of the MTPA trajectory and to provide precise information about the rotor speed and position. The cascaded scheme consists of two parallel FLC-MRAS for speed and multiparameter estimation. The first MRAS is utilized to estimate motor speed and rotor position to achieve robust sensorless control. However, the speed estimator is highly dependent on time-varying motor parameters. Therefore, the second MRAS is designed to identify the quadratic inductance and permanent magnet flux and continuously update both the speed estimator and control scheme with the identified values to ensure accurate speed estimation and real-time MTPA trajectory tracking. Unlike conventional MRAS, which uses linear proportional-integral controllers (PI-MRAS), an FLC is adopted to replace the PI controllers, ensuring high estimation accuracy and enhancing the robustness of the control system against sudden changes in working conditions. The effectiveness of the proposed scheme is evaluated under different speed and torque conditions. Furthermore, a comparison against the conventional PI-MRAS is extensively investigated to highlight the superiority of the proposed scheme. The evaluation results and our quantitative assessment show the ability of the designed strategy to achieve high estimation accuracy, less oscillation, and a faster convergence rate under different working conditions. The quantitative assessment reveals that the FLC-MRAS can improve the estimation accuracy of speed, permanent magnet flux, and quadratic inductance by 19%, 55.8% and 44.55%, respectively.

Instantaneous speed estimation of induction motor by time-varying sinusoidal mode extraction from stator current

When an induction motor operates at variable speed, the fundamental wave and harmonics in the stator current are presented in the form of nonstationary sinusoidal components with time-varying parameters. To estimate the instantaneous speed of the motor utilizing the stator current signal, a time-varying sinusoidal mode extraction (TVSME) method is firstly proposed. In TVSME, a variational model, which compacts the frequency bandwidth of the desired time-varying sinusoidal mode (TVSM) to the narrowest bandwidth and excludes other harmonics as much as possible, is constructed to optimally extract the desired TVSM and to estimate its instantaneous frequency (IF). Then, the fundamental wave and the principal slot harmonics (PSH) are extracted by TVSME, and their IFs are used to calculate the instantaneous rotational speed of the motor. As a result, a highly accurate and smooth rotational speed curve, which is valuable for performance evaluation and fault diagnosis of induction motors, can be obtained. The proposed method is evaluated by both an artificial signal and two experimental signals collected from actual motors. Results show that the instantaneous speed of induction motor can be accurately estimated under the condition of speed variation and multi-harmonic interference.

Speed Estimation of Multiphase Induction Motor Using Rotor Slot Harmonics With Limited SNR and Dynamic Load Conditions

Accurate and reliable speed feedback is vital for closed-loop control of multiphase induction motors (IMs), especially in harsh operating scenes like ship propulsion. High performance speed estimation is effective in getting rid of usual sensor failures, through the rotor slot harmonic (RSH) based method that featured for parameter mismatch robustness. However, the accurate and real-time low speed estimation through RSHs under limited signal noise ratio (SNR) and dynamic load conditions is still a challenge, mainly due to the extremely weak and nonstationary amplitude and closely distributed frequency properties of RSHs. In this paper, we investigate the performance limitations of low speed estimation using primary rotor slot harmonics (PSH) of multiphase IMs, whose detectability is ensured through an improved interior detection coil, under limited SNR and dynamic load conditions. A frequency shifting and an amplitude limiting zero-crossing detection algorithm are proposed for efficient extraction and processing of PSHs in full speed range, which effectively improve the estimation accuracy with reduced complexity. Finally, the proposed method is investigated on a nine-phase IM experimental platform within the whole speed range, limiting the steady and transient error respectively under 4r/min and 20r/min for closed-loop control.

Sensorless optimal control of electronic wedge brake based on dynamic model and Kalman filter state multiple-estimation

An electronic wedge brake is a brake-by-wire system developed by some major companies in the domain of the automotive industry. The inherent self-reinforcing feature of the electronic wedge brake is interpreted as reduced actuation force and hence lower actuation energy. Controlling the electronic wedge brake requires measuring the clamping force using a force sensor, which requires special techniques to be installed onto the system and maintained safe from extreme conditions. This way the system design will be more complex, and the cost is increased due to the sensor expense. In this paper, a new sensorless optimal control (Servo-Linear Quadrature Regulator) approach is proposed using system dynamic-based estimation and Kalman filter-based estimation for clamp force control of electronic wedge brake. The suggested control scheme eliminates the need for clamp force and motor speed sensors by state multiple-estimation using only the current measurement of the motor. First, the motor current is measured and used to estimate motor speed relying on system dynamic equations. Brake wedge speed and position are then approximately derived in terms of the motor state estimates. The resulting current-based state estimates are eventually introduced to the Kalman filter as state measurements and used with the system control input calculated by the optimal controller to correct and update state prediction in the Kalman filter.

Improved ROGI-FLL-Based Sensorless Model Predictive Current Control With MRAS Parameter Identification for SPMSM Drives

This paper presents a sensorless finite control set model predictive current control (FCS-MPCC) for surface-mounted permanent magnet synchronous motor drives based on an improved reduced-order generalized integrator-frequency-locked loop (IROGI-FLL). To simplify the structure of the sensorless FCS-MPCC and avoid control performance degradation caused by the rotor position estimation error, an instantaneous power theory-based given current calculation method is designed. Accordingly, the whole algorithm can be realized without using rotor position. IROGI-FLL is adopted for filtering the estimated back electromotive force and estimating motor speed. To enhance its dynamic speed tracking performance, a speed tracking deviation compensation term is designed for FLL. With the feedforward compensation term, speed static tracking deviation in acceleration and deceleration states can be eliminated. Moreover, to avoid the adverse effect of parameter mismatch on control performance, an incremental adjustable model-based model reference adaptive system (MRAS) is introduced to identify the resistance and inductance parameters. The incremental adjustable model can avoid identification error caused by the rank deficiency issue. Finally, the effectiveness of the proposed method is investigated by experiments.

Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

ABSTRACT Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders.

A new approach of robust speed-sensorless control of doubly fed induction motor fed by photovoltaic solar panel

<span lang="EN-US">In this work, we presented the modelling and simulation of the electrical operation of a photovoltaic (PV) system adapted by an MPPT control, the latter is applied to the robust observer for the control of the speed without sensor of a double fed induction motor (DFIM).Our machine is powered by the PV system, where the chosen control is the direct field oriented control with sliding mode speed regulator is used for the control of this machine which is powered by two pulse-width modulation (PWM) voltage inverters, finally the speed estimation of induction motor with observer based dual feed in sliding mode is presented. The simulation results show the efficiency of the proposed method.</span>

Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter

Speed and rotor position estimation for sensorless brushless DC motor drive based on particle filter

Multi-sliding mode current disturbance suppression scheme based model reference adaptive system for sensorless control of permanent magnet synchronous motor

In order to improve the accuracy of the speed and angle estimation of the permanent magnet synchronous motor (PMSM) in the sensorless mode, this paper firstly proposes a multi-sliding mode scheme based on model reference adaptive system (MRAS) with proportional integral (PI) control. The current disturbance of the sensorless method is suppressed by the multi-sliding mode based on MRAS method, thus reducing the disturbance of the current to the PI and indirectly improving the current anti-interference of the PI. Meanwhile, the quantitative analysis of the current disturbance by the MRAS based on multi-sliding mode scheme is presented in detail. Then, the suppression capability of different sliding-mode schemes is discussed. Finally, various experiments are designed to verify the feasibility of the scheme and the correctness of the theory.

A New SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Low Speed

AbstractA novel sliding mode observer for speed estimation in sensorless induction motor drives at zero and low speed is proposed in this paper. First, aiming at the poor stability when the induction motor runs at low speed, analyze the reasons of the poor stability and a new feedback gains design method is proposed. Second, for the problem of ignored flux error in speed estimation which cause speed error at low speed, a new compensation method is put forward, which can improve the accuracy of speed estimation in the dynamic and steady state of the system. Meanwhile, a continuous function is given which can replace sign, to restrain the chattering problem of sliding mode observer. Detailed simulation results are given showing the operation at zero and low speed, the results prove the effectiveness of the proposed sliding mode observer. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.