Model Reference Adaptive System Method Research Articles

Sensorless speed controller of an induction motor with MRAS-based model predictive control

Speed sensors play critical role in induction motor (IM) control systems. However, the use of these sensors cause several drawbacks such as increasing the overall cost, decreased reliability on control and increased noise problems in data acquisition. The sensorless control methods also have been proposed in control systems of IM to increase the overall performance. On the other hand, model predictive controllers (MPCs) which is one of the most recently used controller methods require the speed measurement to perform speed and torque control of an IM. In addition to control method, the topology of voltage source inverter (VSI) affects the overall efficiency of the IM drive because of its losses and total harmonic distortion (THD) ratios delivered by the topology. The main proposal of this study is to implement an IM drive built with a three-level active neutral point clamped (3L-ANPC) voltage source inverter (VSI) and to propose an MPC that eliminates sensor requirement in speed control of IM. The proposed IM drive takes benefits of multilevel inverter topology, which reduces the torque ripple, enables low switching frequency and provides decreased harmonic distortion. The switching pulses of ANPC VSI are generated by the proposed sensorless MPC method for controlling the torque of IM. The modelling and experimental verification of the ANPC based IM drive have been carried out and the sensorless MPC controller has been validated in this study. There two types of model reference adaptive system (MRAS) have been proposed to ensure precise estimation of IM speed that should be supplied to MPC controller. These MRAS methods are titled as the rotor flux based model reference adaptive system (MRASF), and the stator current based model reference adaptive system (MRASCC) according to estimation references. The proposed MPC-based IM drive has been analyzed under variable speed and torque circumstances to detect the efficiency and reliability of MRASF and MRASCC algorithms. The efficiency indicators such as flux and torque responses, and speed control of the suggested MPC IM drive are validated with experimental analysis. The results of revealed that the IM effectively tracks the reference speed and produces the required torque depending to the precise control of proposed MPC algorithm.

An optimization method of acceleration and deceleration time of feed system based on load inertia

The acceleration and deceleration time is usually a constant value in the process of computer numerical control (CNC) machine tool processing, which cannot adapt to the change of external load and greatly affects processing efficiency. This paper proposes an optimization method for the acceleration and deceleration time of the feed system based on load inertia, which provides the basis for the adaptive adjustment of the acceleration and deceleration time of the feed system. Firstly, by establishing the dynamic model of the servo system, the acceleration and deceleration method is used to identify the external load inertia under different working conditions. The prediction model of the current variance based on load inertia and acceleration and deceleration time parameters is established by using the response surface method, and then the multi-objective particle swarm optimization algorithm is used to build the acceleration and deceleration time optimization model based on the load inertia. At the same time, the inertia identification part is compared with the model reference adaptive system method and the empirical formula estimation method based on current and velocity, the simulation and cutting experiment results show that the inertia identification method based on acceleration and deceleration optimizes the other method. Finally, the machining experiments are carried out on three-axis and five-axis machine tools with the same machine tool type, and by adding different counterweight blocks to change the external load. The test proves that the acceleration and deceleration adaptive adjustment strategy based on load inertia can effectively improve processing efficiency and reduce the fluctuation of the processing load.

Modified Super-Twisting Algorithm-Based Model Reference Adaptive Observer for Sensorless Control of the Interior Permanent-Magnet Synchronous Motor in Electric Vehicles

In this paper, the model reference adaptive system (MRAS) method has been employed to observe speed in sensorless field-oriented control (FOC) with flux weakening (FW) and maximum torque per ampere (MTPA) operations for the interior permanent-magnet synchronous motor (IPMSM). This paper focuses on the modified MRAS observer, which is based on the sigmoid function as a switching function and also the adaptive sliding mode coefficient. The sliding mode strategies are employed for the adaptation mechanism instead of the PI controller. The conventional PI-MRAS causes oscillations in rotor speed. To solve this problem, the modified adaptive super-twisting algorithm (STA)-based MRAS method is proposed by utilizing the sigmoid function. The proposed modified MRAS is compared to conventional methods. Additionally, it is examined for performance against the fast terminal sliding mode (FTSM), which is applied to the MRAS as an adaptation mechanism in terms of sliding mode strategies. The modified STA-MRAS is explored under the ECE and EUDC (Extra Urban Driving Cycle) drive cycles for electric vehicle applications. Finally, the obtained results show the validity and capability of the proposed adaptive STA-MRAS in terms of speed tracking.

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.

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.

Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

A novel resonant frequency tracking control for linear compressor based on MRAS method

To optimize the efficiency of the linear compressor, its operating frequency must be controlled equal to the system resonant frequency. The traditional resonant frequency tracking control algorithm relies on the steady state characteristics of the system, which suffers from slow convergence speed, low accuracy and slow system response. In order to solve these problems, a novel resonant frequency tracking control for linear compressor based on model reference adaptive system (MRAS) is proposed in this paper, and the parameter adaptive rate is derived by the Popov's hyperstability theory, so that the system resonant frequency can be directly calculated through the parameter adaptive rate. Furthermore, the traditional algorithm needs to calculate the piston stroke signal by integrating the back-EMF, which has the problem of integral drift. The algorithm proposed in this paper only needs the velocity signal, and the accuracy of the velocity calculation can be ensured by utilizing the self-adaptive band-pass filter (SABPF), thereby greatly improving the accuracy of the resonance frequency calculation. Simulation results verify the effectiveness of the proposed algorithm.

Speed sensorless control employing adaptive sliding mode adjustable model MRAS for induction motors at low speed range

For the sensorless speed control of induction motors, at low speed range, there exist the common problems such as low speed estimation accuracy, weak loading capacity and poor dynamic performance. When the flux-based model reference adaptive system methods are adopted, the performance of the flux estimation will determine the accuracy of the speed estimation to a large extent. In this paper, an adaptive sliding mode observer was introduced to replace the adjustable model of the traditional model reference adaptive system. The adaptive gain is designed in order to estimate the flux more accurately, and a new speed adaptive law is proposed to improve the dynamic performance and the accuracy of the speed estimation at low speed range. Simulation results verify the correctness and effectiveness of the proposed method.

A new stator voltage error-based MRAS model for field-oriented controlled induction motor speed estimation without using voltage transducers

A sensorless speed estimation method for performing the indirect field-oriented control (IFOC) of induction motors (IMs) is presented in the current research. The method uses the stator voltage as state variable but eliminates the voltage transducers. Traditional MRAS methods use two sets of equations to estimate the motor speed. However, the proposed system utilizes the q-axis current regulator proportional–integral (PI) control output $$ \left( {V_{\text{sq}}^{*} } \right) $$ in the reference model as a new Model Reference Adaptive System (MRAS) technique and compares this value with the estimated voltage $$ \left( {\hat{V}_{\text{sq}} } \right) $$ in an adaptive model. The method calculates the steady-state q-axis stator voltage in adaptive model, so, the calculation burden in the estimation loop is significantly reduced. Choosing the q-axis stator voltage as state variable removes requirement of the rotor parameters in calculations, and therefore, the system becomes resistant to the alteration in rotor parameters. Furthermore, the mentioned special calculation ensures the elimination of the need for flux estimation, so the speed estimation technique becomes resistant to integral and derivative calculation-based problems. The MATLAB/Simulink simulation results and experiments under various operation conditions verifies the effectiveness of the studied technique.

Predictive Direct Voltage Control of Induction Motor With Mechanical Model Consideration for Sensorless Applications

Finite control set model predictive control is a simple solution for control of power electronics instruments. One of the deterrent problems with this method is the weighting factor in the cost function. Adjusting the weighting factor has a significant impact on achieving the desired response. However, the optimum value of the weighting factor can be changed by varying the operation point. Predictive direct voltage control uses a simplified cost function without weighting factor. In this method, the only term of the cost function is the last control variable, i.e., voltage of the inverter. The reference voltage is calculated by the deadbeat method, and the feasible voltages are examined in the cost functions. However, the weak robustness is the main drawback of the method because the reference is calculated on the basis of the deadbeat method with similar problem, especially in sensorless applications. In this paper, a robust predictive direct voltage control is proposed. The prediction model is improved by an model reference adaptive system method taking the mechanical model of the motor into consideration. The method is evaluated by simulations and experiments in a wide range of the operating points.

Real-Time Speed Control of an Electrically Powered Wheelchair by Using Fractional Order Model Reference Adaptive System Control

In this study, speed and direction angle control technique based on the fractional order model reference adaptive system (FO-MRAS) are proposed for controlling an electrically powered wheelchair (EPW) system for the first time in the literature. The model reference adaptive system (MRAS) technique has been used to achieve the estimated value of any system’s velocity information. In general, the control of the MRAS technique is usually performed by the PI based controller. However, the classical PI controllers are unable to meet the demands of high precise trajectory control during the motion. In this study, to increase the effectiveness of the PI based MRAS controller, this technique is combined with a fractional-order (FO) calculus to obtain an accurate and a robust trajectory tracking performance for an EPW. The proposed FO-MRAS controller has been compared experimentally with the conventional PI based MRAS method to show the effectiveness of using FO-MRAS method in terms of trajectory tracking accuracy and error levels. The experimental outcomes demonstrate that the proposed FO-MRAS controller gives a better trajectory tracking performance as well as having a smaller speed error.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21839

Modified Reference Model for Rotor Flux-Based MRAS Speed Observer Using Neural Network Controller

ABSTRACTThe poor low speed performance of rotor flux-based model reference adaptive system (MRAS) is due to the presence of integrator and parameter variation with temperature. To improve low speed performance, a novel rotor flux-based MRAS method is proposed and neural network controller (NNC) is used in place of PI controller in reference model and adaptation mechanism. In this method, a compensating voltage is added to the d–q axis rotor flux equations of induction motor (IM) by modifying voltage model to reduce DC drift and initial value problems of integrator. NNC is implemented in both modified reference model to obtain the drift voltage and in adaptation mechanism to accurately estimate the rotor speed. The proposed scheme is experimentally implemented using dSPACE ds-1104 R&D controller board with improved speed response compared to rotor flux-based MRAS.

MRAS identification of permanent magnet synchronous motor parameters

This article deals with the identification of synchronous motor parameters with permanent magnets using the model-reference adaptive system (MRAS). This method can effectively identify two parameters. These parameters are the stator resistance and the BEMF, under certain conditions as well as the direct inductance. MRAS method is based on two different models. One of these is in our case a real motor and the other is a simulated model based on the identified parameter. This identification was verified in simulations and then applied to a real motor.

Speed Sensorless Control of Six-Phase Asynchronous Motor Drive

Multi -phase ac motor drives are nowadays considered for various applications, due to many advantages that they offer when compared to three-phase motors. Cancellation of mechanical position or speed sensors at the motor shaft have the attractions for adjustable speed drives of induction motor to reduce the cost and increase the reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. This paper investigates speed estimation method using model reference adaptive system (MRAS) to improve the performance of a sensorless vector controller of six-phase induction motor (IM). In the proposed method, the stator current is used as the state variable to estimate the speed. Since the stator current error is represented as a function of the first degree for the error value in the speed estimation, the proposed method provides fast speed estimation and is also, more robust to variations in the stator resistance, compared with other MRAS methods. Consequently, this method can improve the performance of a sensorless vector controller in a low speed region and at zero-speed. The proposed method is verified by simulation using the Matlab/Simulink package. The performance of the proposed system is investigated at different operating conditions. The proposed controller is robust and suitable for high performance six-phase induction motor drives. Simulation results validate the proposed approaches.

Application of MRAS Algorithm to Replace the Speed Sensor in Induction Motor Drive System

Sophisticated control of induction motor drive requires knowledge of the rotor angular velocity. In order to obtain the speed information an optical sensor is usually utilized. However, it is not always advantageous because of the whole drive robustness decrease caused by the speed sensor inclusion. The paper presents some simulation and measurement results in case of the MRAS (Model Reference Adaptive System) method employing in attempt to eliminate the induction motor drive speed sensor. The operation principle is explained and a mathematical model of the drive system is created. The MRAS method influence on the drive behaviour is examined in simulation environment and experimentally. Finally, the experimental results and comparison of the drive behaviour with and without the speed sensor are discussed.

Speed Sensorless Control For Full-Scale Converter Of Wind Turbine With Induction Generator

This paper presents a speed sensorless control strategy for full-scale converter of wind turbine with the squirrel-cage induction generator (SCIG) based on a Model Reference Adaptive System (MRAS) method. The full-scale converter wind turbine has many advantages, such as simple structure, high reliability, low maintenance cost. However, there are still some drawbacks in its traditional control strategy, such as control stability, system performance, efficiency and reliability. The project implementation about generator speed estimation based on MRAS method is studied, and DC bias suppression is described completely based on the characteristics of SCIG. The Simulation experimental results show a consistency that is mainly in the correctness of flux observer design, the effectiveness of speed sensorless implementation strategy and good static and dynamic properties in the system.

Sensorless control application of PMSM with a novel adaptation mechanism

This paper proposes a novel adaptation design to replace the classical proportional-integral controller used in model reference adaptive system (MRAS) speed estimation. The proposed adaptation scheme is an association of two fuzzy units. The rules of each module were obtained by user experience and numerical data. In the traditional fuzzy logic controllers, the computational complication enhances with the attributes of the system variable quantities; the number of rules increases incrementally while the number of control variables increases. This negatively affects the response time of the system. This novel design was deduced to reduce the number of rules for a linear function of system variables. By this way, the response of the system became faster. Detailed simulation and experimental results were obtained for comparison of this novel method with traditional MRAS techniques. The results showed that the proposed method was faster in speed tracking and exhibited higher prediction accuracy and less oscillation than the traditional method. Thus, the proposed MRAS method was clearly seen to be applicable and reliable.

Improving Position Sensor Accuracy Through Spatial Harmonic Decoupling, and Sensor Scaling, Offset, and Orthogonality Correction Using Self-Commissioning MRAS Methods

This paper presents methods for improving the accuracy of vector-based position measurements through the decoupling of multiple nonideal sensor properties using model reference adaptive system (MRAS) techniques. The nonideal sensor properties considered are: signal scaling errors (amplitude imbalance on the vector components), signal offsets, imperfect orthogonality (quadrature error) between sensor vector components, and additional spatial harmonics superimposed on fundamental sensor outputs. Simulation and experimental results are provided to evaluate the proposed MRAS-based decoupling methods. The methods presented here can be applied to multiple forms of vector-based position measurement and estimation such as the use of magnetoresistive sensors, sine/cosine encoders, resolvers, or self-sensing (sensorless) methods. These methods can be implemented in real time and are well-suited to the self-commissioning of vector-based position sensors for enhanced sensor accuracy.

Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

In this paper, a novel rotor speed estimation method using model reference adaptive system (MRAS) is proposed to improve the performance of a sensorless vector control in the very low and zero speed regions. In the classical MRAS method, the rotor flux of the adaptive model is compared with that of the reference model. The rotor speed is estimated from the fluxes difference of the two models using adequate adaptive mechanism. However, the performance of this technique at low speed remains uncertain and the MRAS loses its efficiency, but in the new MRAS method, two differences are used at the same time. The first is between rotor fluxes and the second between electromagnetic torques. The adaptive mechanism used in this new structure contains two parallel loops having Proportional-integral controller and low-pass filter. The first and the second loops are used to adjust the rotor flux and electromagnetic torque. To ensure good performance, a robust vector control using sliding mode control is proposed. The controllers are designed using the Lyapunov approach. Simulation and experimental results show the effectiveness of the proposed speed estimation method at low and zero speed regions, and good robustness with respect to parameter variations, measurement errors, and noise is obtained.

Sensorless Rotor-Field Oriented Controlled Induction Motor Drive with Particle Swarm Optimization Algorithm Speed Controller Design Strategy

A sensorless rotor-field oriented control induction motor drive with particle swarm optimization algorithm speed controller design strategy is presented. First, the rotor-field oriented control scheme of induction motor is established. Then, the current-and-voltage serial-model rotor-flux estimator is developed to identify synchronous speed for coordinate transformation. Third, the rotor-shaft speed on-line estimation is established applying the model reference adaptive system method based on estimated rotor-flux. Fourth, the speed controller of sensorless induction motor drive is designed using particle swarm optimization algorithm. Simulation and experimental results confirm the effectiveness of the proposed approach.