Parameter Identification Of Motor Research Articles

Research on Parameter Identification of Induction Motor Based on Model Reference Adaptive System

Abstract This paper proposes a novel online parameter identification method based on model-reference adaptive systems to achieve more precise control of induction motors. This method efficiently and accurately obtains the parameters of induction motors during operation, facilitating effective control. Experimental validation is conducted on a hardware-in-the-loop test platform, demonstrating that the proposed online parameter identification method for induction motors achieves accurate parameter estimation with the advantages of high identification speed and precision. Control experiments on induction motors using the identified parameters further validate the feasibility of the proposed method for online parameter identification.

Parameter Identification of Permanent Magnet Synchronous Motor with Dynamic Forgetting Factor Based on H∞ Filtering Algorithm

To address system parameter changes during permanent magnet synchronous motor (PMSM) operation, an H∞ filtering algorithm with a dynamic forgetting factor is proposed for online identification of motor resistance and inductance. First, a standard linear discrete PMSM parameter identification model is established; then, the discrete H∞ filtering algorithm is derived using game theory reducing state and measurement noise influence. A cost function is defined, solving extremes values of different terms. A dynamic forgetting factor is introduced to the weighted combination of initial and current measurement noise covariance matrices, eliminating identification issues from different initial values. On this basis, a dynamic forgetting factor is added to weigh the combination of the initial measurement noise covariance matrix and the current measurement noise covariance matrix, which eliminates the influence of the discrimination error caused by the different initial values. Finally, the identification model is built in MATLAB/Simulink for simulation analysis to verify the feasibility of the proposed algorithm. The simulation results show the proposed H∞ filtering algorithm rapidly and accurately identifies resistance and inductance values with significantly improved robustness. The forgetting factor enables quick stable recognition even with poor initial values, enhancing PMSM control performance.

Parameter Identification of a Permanent Magnet Synchronous Motor Based on the Model Reference Adaptive System with Improved Active Disturbance Rejection Control Adaptive Law

Aiming at the problem that online parameter identification, based on the Model Reference Adaptive System (MRAS), is easily affected by the high-frequency noise of the sensor, an improved MRAS, based on variable bandwidth linear Active Disturbance Rejection Control (ADRC) adaptive law, is proposed. The proposed ADRC adaptive law, including an extended state observer, exhibits good anti-disturbance ability while effectively tracking the error signal through adjusting the bandwidth of the controller according to the observation error. When the observation error is large, the bandwidth of the controller is increased to enhance the tracking accuracy; in the opposite situation, the bandwidth is reduced to improve the anti-disturbance performance. To illustrate feasibility, a stability analysis of the proposed ADRC adaptive law is carried out and some comparison and validation experiments are designed to identify the inductance and flux linkage of the permanent magnet synchronous motor (PMSM). The experimental results show that the proposed method has better anti-disturbance performance than the MRAS based on traditional Proportional Integral (PI) adaptive law or switched PI adaptive law.

Position Sensorless Drive With Online Parameters Estimation for Magnetic Suspension Centrifugal Compressor

With the advantages of a high power-to-weight ratio and oil-free bearings, a magnetically Suspension centrifugal compressor (MSCC) equipped with active magnetic bearings (AMB) operates more efficiently than traditional mechanical compressors over a wide speed range by precisely controlling a high-speed permanent-magnet synchronous motor (PMSM). However, owing to the harsh working environment, the motor parameters, including the resistor, inductance and flux linkage, vary significantly. This degrades the rotor position observer performance, which is crucial for controlling a high-speed PMSM. Hence, this article proposes an improved sensorless control method combined with novel motor parameter identification. First, an improved position sliding mode observer using a novel super-twisting algorithm (STA) is designed to achieve higher precision and better transient performance. Second, the influence of parameter mismatch is analyzed. To overcome the problems of rank deficiency and system matrix uncertainties, a novel identification strategy based on recursive total least squares excitatory and inhibitory learning (RTLS EXIN) is developed to identify inductance and resistance in two-time scales. Meanwhile, the back electromotive force (EMF) in static coordinates is employed to estimate the flux linkage. Third, the results are adopted to update the control parameters immediately. Finally, the proposed method is tested on an MSCC prototype experimental platform, and the results verify its validity and feasibility.

Online parameter identification of permanent magnet synchronous motor based on recursive least squares algorithm

In order to solve the problem of variable parameters in the system when the PMSM works, this project intends to use the recursive algorithm in the steady state of alpha-beta. This project identifies the two parameters as the fixed values of two parameters at the same time, so as to reduce the number of parameters to be estimated and avoid the problem of under-classification. The proposed method is studied experimentally on the 2 kW PMSM experimental platform. Simulation results show that this method can quickly and accurately realize the online identification of parameters in the PMSM system.

Comparison of Differential Evolution and Nelder–Mead Algorithms for Identification of Line-Start Permanent Magnet Synchronous Motor Parameters

Line-start permanent magnet synchronous motors (LSPMSMs) are of great interest to researchers because of their high energy efficiency, due to the growing interest of manufacturers in energy-efficient units. However, LSPMSMs face some difficulties in starting and synchronization processes. The LSPMSM lumped parameter model is applicable to estimating the successfulness of starting and further synchronization. The parameters of such a model can be determined using computer-aided identification algorithms applied to real motor transient processes’ curves. This problem demands significant computational time. A comparison between two algorithms, differential evolution and Nelder–Mead, is presented in this article. The algorithms were used for 0.55 kW, 1500 rpm LSPMSM parameter identification. Moreover, to increase computational speed, it is proposed to stop and restart the algorithms’ procedures, changing their parameters after a certain number of iterations. A significant advantage of the Nelder–Mead algorithm is shown for the solving of the considered problem.

Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved MRAS Algorithm

During the operation of a permanent magnet synchronous motor, the electrical parameters of the motor will be affected by the temperature and the saturation of the magnetic chain, which will lead to a decrease in the effectiveness of the control algorithm for motor control. In this paper, we address the problem that the traditional MRAS algorithm has under-ranked equations when identifying motor parameters, and the three parameter identification values are coupled with each other and affect each other, which leads to scattered identification results and slow identification speed. In this paper, we propose a segmentation identification algorithm that combines the traditional MRAS algorithm with the recursive least squares method. In this paper, the inductance value is first identified by the recursive least squares method, and then the inductance value is substituted into the MRAS to identify the stator resistance and rotor magnetic chain, which effectively solves the problems of equation under-ranking and slow identification speed in the traditional MRAS, thus improving the stability of the permanent magnet synchronous motor operation.

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization

Accurate estimation of PMSM parameters is beneficial to the high performance operation of PMSM. In order to prevent PSO from falling into the local optimal solution in the PMSM parameter identification process, so as to improve the accuracy of identification results, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which changes the speed of each particle from only affected by the optimal particle to affected by the surrounding particles, This improvement guarantees the identification accuracy of the algorithm, and introduce the convergence factor to ensure that the MDFPSO can converge. Simulation results show that the MDFPSO algorithm is effective in PMSM parameter identification.

A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi-strategy self-optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full-rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition-based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self-learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine-exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.

Study of rotor position estimation algorithm based on back-EMF voltage for dual-winding fault-tolerant permanent magnet motor

An improved position estimation of the sensorless control system with online parameter identification based on back-Electromotive Force (EMF) voltage is presented for the dual-winding fault-tolerant permanent magnet motor (FTPMM). In this control system, the rotor position is estimated by the flux linkage and the back-EMF which are generated by each phase winding. By introducing phase-locked loop technology to compensate the steady-state error of the system and online identification of motor parameters which is using the least-square method with forgetting factor, more accurate position estimation can be obtained. The current vector fault-tolerant control strategy improves the fault tolerance of the system and makes it strong robust stability. The simulation results have shown that the accurate position data can be acquired both under healthy condition and single-phase fault condition. Then, the hardware experimental results show the feasibility and validity of the proposed algorithm.

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.

ROS-based Toolbox for Motor Parameter Identification of Robotic Manipulators

For many applications, a precise knowledge of the model of the robot is necessary for accurate and stable control. However, it is not always feasible or desirable to perform from scratch an in-depth study of the robot model, especially if it is not an element of concern for the respective application. In this article, we present a methodology for identifying motor parameters of a robotic manipulator. We discuss the mathematical model and introduce an extensible toolbox with velocity-control based methodology for a fast identification of individual motor parameters. The results show that we can identify individual parameters even for joints that are commercialised as of the same type.

Parameter identification of permanent magnet synchronous AC motor based on spectral method

This paper establishes a mathematical model with random terms for a permanent magnet synchronous AC motor, which is a nonlinear system using d - axis current, q - axis current and the rotor electric angular speed as state variables. The numerical solution obtained by the Euler-Maruyama method is used as the measurement data. Aiming at the parameter identification of the system, a step-by-step identification method based on the spectral method discretize the system first and then using the least squares is proposed. This method is used to identify multiple parameters of the motor in the same model. In step-by-step identification, firstly by fixing the motor speed, the system is transformed into a linear system, which is used to estimate the resistance, inductance and flux linkage. After that, the speed is not fixed, for the electrical parameters are known, we can identify damping and inertia by using mechanical equations. Finally, the experimental results show that the relative errors of the parameters identified by the proposed method are smaller, which shows the effectiveness of this method for multi-parameter identification.

Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

In this article, the parameter identification of a brushless DC motor (BLDC) is presented. The approach here presented is based on a direct identification considering a three-phase line-to-line voltage electromagnetic torque as function of the electric currents and rotor speed. The estimation is divided into two stages. First, the electrical parameters are estimated by well-known no-load and DC tests. Consequently, estimation of mechanical parameters is performed using a recursive Least Square Algorithm. The proposed approach is validated by comparing model responses to motor real time responses. Additionally, the design, digital simulation and real time implementation of a PI rotor speed controller, based on the estimated model, validate the identification proposal presented here.

Algorithm for identification of DC motor parameters by method of dynamic expansion of regressor and mixing

Subject of Research. The paper considers the problem of identifying the parameters of various robotic objects. A DC motor is used as an example. Existing methods for identification of parameters require either a large amount of time for accurate determination of the required values or give an estimate with a large error. We propose to expand the application area of the identification algorithm by the method of Dynamic Regressor Extension and Mixing for control problems of robotic objects with a DC motor. Method. The first stage of Dynamic Regressor Extension and Mixing method generates to this procedure. Main Results. A new algorithm is proposed for identifying the parameters of DC motor models. It is shown that, when using the new approach, the fluctuations in parameter estimates are significantly lower, while the response time is much shorter. When using the gradient method, the transient time to estimate the signal parameters is 350 seconds, while for the Dynamic Regressor Extension and Mixing method this time does not exceed six seconds. Besides, Dynamic Regressor Extension and Mixing method has not got overshoot. Practical Relevance. The results of the work can be applied to the design of automatic control systems in control problems of electromechanical objects, including DC motors.new regression forms by applying a dynamic operator to the original regression data. Then, the required combination of new data is selected to obtain the final desired regression form. Standard parameter estimation methods are applied.

PMSM parameter identification based on improved PSO

The results of the standard PSO algorithm are easy to fluctuate and the time-varying error is too large. By introducing the strategy of Gaussian decline and Gaussian disturbance, an improved PSO motor parameter identification method is proposed. When the motor parameters change, the improved PSO method can be used to identify the motor parameters faster, more accurate and more stable. The simulation results show that the improved PSO overcomes the recognition results of the standard PSO and improves its recognition accuracy.

Application of recent nature‐inspired meta‐heuristic optimisation techniques to small permanent magnet DC motor parameters identification problem

In this study, an attempt is made to find an effective solution on the small direct current (DC) motor's parameters identification problem by employing two recently introduced stochastic nature-inspired techniques. The first one is called ‘salp swarm algorithm’ (SSA) while the second one is called ‘ant-lion optimiser’ (ALO). These algorithms have been inspired by the interaction strategy between the ocean salps and the ants and ant-lions, respectively, in nature. To appraise the effectiveness of these algorithms, a DC motor model has been appropriately implemented and its performance is evaluated by using speed step responses, while all model's parameters are considered unknown and therefore search variables. Integral of the square of the error (ISE), integral absolute error and integral in time of absolute error have been adopted as objective functions for the algorithms’ evaluation. In order to judge the acceptability of these algorithms, the simulation results are compared with those of another one similar technique, namely the ‘grey wolf optimiser’ (GWO). The obtained results reveal very satisfactory performance and confirm that the examined SSA, ALO and GWO algorithms can identify accurately the DC motor parameters and can be applied effectively to the specific problem. Another finding is that SSA combined with ISE criterion seems to be the most appropriate technique among the three algorithms.

The Ideal Electric Drive

Reliable and easy identification of motor parameters with a power analyzer helps motor control engineers ensure optimal motor efficiency performance

An Impedance Model-Based Multiparameter Identification Method of PMSM for Both Offline and Online Conditions

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq -axes. This method identifies the resistance and the dq -axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions.