Improved ANFIS based MRAC observer for sensorless control of PMSM

2021 ◽  
pp. 1-13
Author(s):  
Suryakant ◽  
Mini Sreejeth ◽  
Madhusudan Singh
Keyword(s):  
Fuzzy Inference ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Control ◽  
Control Algorithms ◽  
Motor Speed ◽  
Inference System ◽  
Rotor Position ◽  
Improved Performance ◽  
Incremental Encoder ◽  
Model Reference Adaptive

Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive.

Direct Torque Control of Induction Motor Using Enhanced Firefly Algorithm — ANFIS

2017 ◽  
Vol 26 (06) ◽  
pp. 1750092
Author(s):  
J. N. Chandra Sekhar ◽  
G. V. Marutheswar
Keyword(s):  
Induction Motor ◽  
Firefly Algorithm ◽  
Fuzzy Inference ◽  
Direct Torque Control ◽  
Torque Control ◽  
Support Vector ◽  
Hybrid Technique ◽  
Motor Speed ◽  
Inference System ◽  
Neuro Fuzzy

In this paper, the hybrid direct torque control (DTC) technique is proposed for controlling the speed of the induction motor (IM). The hybrid technique is the combination of an enhanced firefly algorithm (FA) and the adaptive neuro fuzzy inference system (ANFIS) technique. The performance of the FA is improved by updating the randomized parameter. Here, the genetic algorithm (GA) is utilized for updating the parameter and improved the performance of the FA. Initially, the actual torque and the change of toque are applied to the input of the enhanced FA and form the electromagnetic torque as a dataset. The output of the enhanced FA is given to the input of the ANFIS which is determined from the output of interference system. The dynamic behavior of the IM is analyzed in terms of the parameters such as the speed, torque, flux, etc. Based on the parameters, the motor speed is controlled by utilizing the proposed technique. Then the output of the ANFIS is translated into the stator voltage which is given to the input of the support vector machine (SVM). After that, the control signal is generated for controlling the speed of the IM. The proposed hybrid technique is implemented in the Matlab/Simulink platform. The performance analysis of the proposed method is demonstrated and contrasted with the existing techniques such as without controller, particle swarm optimization (PSO)-based ANFIS and FA-ANFIS controller.

scholarly journals Research on an Improved Sliding Mode Sensorless Six-Phase PMSM Control Strategy Based on ESO

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1292
Author(s):  
Hanying Gao ◽  
Guoqiang Zhang ◽  
Wenxue Wang ◽  
Xuechen Liu
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Sliding Mode ◽  
High Reliability ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Ripple ◽  
Estimation Accuracy ◽  
Switching Function ◽  
Motor Speed ◽  
Rotor Position

The six-phase motor control system has low torque ripple, low harmonic content, and high reliability; therefore, it is suitable for electric vehicles, aerospace, and other applications requiring high power output and reliability. This study presents a superior sensorless control system for a six-phase permanent magnet synchronous motor (PMSM). The mathematical model of a PMSM in a stationary coordinate system is presented. The information of motor speed and position is obtained by using a sliding mode observer (SMO). As torque ripple and harmonic components affect the back electromotive force (BEMF) estimated value through the traditional SMO, the function of the frequency-variable tracker of the stator current (FVTSC) is used instead of the traditional switching function. By improving the SMO method, the BEMF is estimated independently, and its precision is maintained under startup or variable-speed states. In order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. Finally, the effectiveness of the method is verified by simulation and experiment results. The proposed control strategy can effectively improve the dynamic and static performance of PMSM.

scholarly journals Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ahmed Tahour ◽  
Hamza Abid ◽  
Ghani Aissaoui
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Digital Simulation ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Inference System ◽  
Switched Reluctance ◽  
Neuro Fuzzy ◽  
Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

scholarly journals Research on sensorless control method of permanent magnet synchronous motor based on iterated cubature Kalman filter

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041989027
Author(s):  
Shi Peicheng ◽  
Wang Chen ◽  
Zhang Rongyun ◽  
Wang Suo
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Estimation Method ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Motor Speed ◽  
Rotor Position ◽  
Cubature Kalman Filter

Aiming at the problems of high cost, increased volume, low reliability, and environmental interference caused by sensor installation on permanent magnet synchronous motor, estimation method for motor speed and rotor position is proposed based on iterated cubature Kalman filter algorithm and applied to permanent magnet synchronous motor sensorless control. First, discrete mathematical model of permanent magnet synchronous motor in α-β coordinate system is established. Then, based on cubature Kalman filter and iterated cubature Kalman filter, simulation model of sensorless vector control system with dual closed-loop of permanent magnet synchronous motor speed and current is established. Also, simulation verification of two working conditions with given rotation speed and load is carried out. Finally, hardware experimental verification platform is built based on TMS320F28335 chip. Both simulation analysis and experimental results show that iterated cubature Kalman filter application to sensorless control of permanent magnet synchronous motor demonstrates good anti-load variation interference, stable motor operation, high motor speed and rotor position estimation accuracy, which suits the application with high requirement for precise motor control and mean important reference value and promotion significance.

DYNAMICS AND IMPROVED COMPUTED TORQUE CONTROL OF A NOVEL MEDICAL PARALLEL MANIPULATOR: APPLIED TO CHEST COMPRESSIONS TO ASSIST IN CARDIOPULMONARY RESUSCITATION

2015 ◽  
Vol 15 (04) ◽  
pp. 1550051 ◽  
Author(s):  
MOHSEN ASGARI ◽  
MAHDI A. ARDESTANI
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Fuzzy Inference ◽  
Virtual Work ◽  
Parallel Manipulators ◽  
Torque Control ◽  
Chest Compressions ◽  
Inverse Dynamic ◽  
Inference System ◽  
Novel Structure ◽  
Computed Torque

In cardiopulmonary resuscitation (CPR), in practice, the rescuer usually uses two hands to perform the action of chest compressions. During chest compressions action, the two arms of the rescuer actually constitute a parallel mechanism. Inspired by this performance, this paper presents a novel structure of parallel manipulators from Delta robot family for chest compressions in rescuing a patient. Also, two new control methodologies are applied to track the desired trajectory. Based on one supervisory approach and another one based upon adaptive neuro-fuzzy inference system (ANFIS) approach. Inverse dynamic modeling is performed based on principle of virtual work and the results are verified using MSC.Adams© software. The proportional derivative (PD) controllers of computed torque (C-T) method usually need manual retuning to make a successful task, particularly in the presence of disturbance. In the present paper, we study and compare the feasibility of applying supervisory controller and ANFIS instead of conventional controller used in C-T method to cope with the above mentioned problem. Several computer simulations imply that the proposed method is encouraging compared with C-T method implemented with conventional controller.

scholarly journals Iterative sliding mode observer for sensorless control of five-phase permanent magnet synchronous motor

2017 ◽  
Vol 65 (6) ◽  
pp. 845-857
Author(s):  
J. Yang ◽  
M. Dou ◽  
D. Zhao
Keyword(s):  
Permanent Magnet ◽  
Sliding Mode ◽  
Sensorless Control ◽  
Permanent Magnet Synchronous Motor ◽  
Sliding Mode Observer ◽  
Motor Speed ◽  
Third Harmonic ◽  
Rotor Position ◽  
The Third ◽  
Three Phase

AbstractDue to the star connection of the windings, the impact of the third harmonic which does not exist in three-phase permanent magnet synchronous motor (PMSM) cannot be ignored in five-phase PMSM. So the conventional sensorless control methods for three-phase PMSM cannot be applied for five-phase PMSM directly. To achieve the sensorless control for five-phase PMSM, an iterative sliding mode observer (ISMO) is proposed with the consideration of the third harmonic impact. First, a sliding mode observer (SMO) is designed based on the fivephase PMSM model with the third harmonic to reduce the chattering and obtain the equivalent signal of the back electromotive force (EMF). Then, an adaptive back EMF observer is built to estimate the motor speed and rotor position, which eliminates the low-pass filter and phase compensation module and improves the estimation accuracy. Meanwhile, by iteratively using the SMO in one current sampling period to adjust the sliding mode gains, the sliding mode chattering and estimation errors of motor speed and rotor position are further reduced. Besides, the stability of the SMO and the adaptive back EMF observer are demonstrated in detail by Lyapunov stability criteria. Experiment results verify the effectiveness of the proposed observer for sensorless control of five-phase PMSM.

The Study of Fuzzy/PID Compound Control Algorithms in Rubber Sulfuration Process Based on Sugeno Rules

2011 ◽  
Vol 221 ◽  
pp. 571-576
Author(s):  
Chun Tang Zhang ◽  
Zhen Zhu Yu
Keyword(s):  
Mathematical Model ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Control Algorithms ◽  
Fuzzy Pid ◽  
Inference System ◽  
Pid Algorithm ◽  
Compound Control ◽  
Simulation Results ◽  
Nonlinear Delay

Aiming at rubber sulfuration of nonlinear, delay and complexity, a Fuzzy/PID compound control algorithm is proposed. The algorithm combined fuzzy inference system and PID algorithm, it has solved well the problem which is difficult to establish a precise mathematical model because of the uncertainties and complexities of rubber sulfuration. The simulation results indicate that the control algorithm is viable and effective.

scholarly journals Four-Level Hysteresis-Based DTC for Torque Capability Improvement of IPMSM Fed by Three-Level NPC Inverter

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1558 ◽  
Author(s):  
Samer Saleh Hakami ◽  
Kyo-Beum Lee
Keyword(s):  
High Speed ◽  
Control Strategies ◽  
Permanent Magnet Synchronous Motor ◽  
Direct Torque Control ◽  
Simple Algorithm ◽  
Torque Control ◽  
Motor Speed ◽  
Average Value ◽  
Stator Current ◽  
Interior Permanent Magnet

Direct torque control (DTC) is considered one of the simplest and fastest control strategies used in motor drives. However, it produces large torque and flux ripples. Replacing the conventional two-level hysteresis torque controller (HTC) with a four-level HTC for a three-level neutral-point clamped (NPC) inverter can reduce the torque and flux ripples in interior permanent magnet synchronous motor (IPMSM) drives. However, the torque will not be controlled properly within the upper HTC bands when driving the IPMSM in the medium and high-speed regions. This problem causes the stator current to drop, resulting in poor torque control. To resolve this problem, a simple algorithm based on a torque error average calculation is proposed. Firstly, the proposed algorithm reads the information of the calculated torque and the corresponding torque reference to calculate the torque error. Secondly, the average value of torque error is calculated instantaneously as the reference torque changes. Finally, the average value of the torque error is used to indicate the operation of the proposed algorithm without the need for motor speed information. By using the proposed algorithm, the torque can be controlled well in all speed regions, and thus, a better stator current waveform can be obtained. Simulation and experimental results validate the effectiveness of the proposed method.

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