Comparison of Two Novel MRAS Based Strategies for Identifying Parameters in Permanent Magnet Synchronous Motors

Two model reference adaptive system （MRAS） estimators are developed for identifying the parameters of permanent magnet synchronous motors （PMSM） based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.

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.

Two New Control Signal Approaches for Obtaining the MRAS-CDM and a Real-time Application

The coefficient diagram method （CDM） is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems （MRAS） are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.

Analysis of the Mass Appraisal Model by Using Artificial Neural Network in Kaohsiung City

An accurate assessment of the property value is very important to make a deal, property tax, and mortgage for loan. The mass appraisal system has been developed in some foreign countries, especially in American for a long time. In Taiwan, we still have few experiences in using computer-assisted mass appraisal system, especially using artificial neural network （ANN）. This article has two objectives： （1） to illustrate application of ANN to the Kaohsiung property market by the method of back-propagation. The study is based on the properties data of sales price, we also use multiple regressions in the same data; （2） to evaluate the performance of two models by using the mean absolute percentage error （MAPE） and hit ratio （HR）. This paper finds that using artificial neural network （ANN） is able to overcome multiple regressions＇ methodological problems and also get better performance than multiple regression model （MRA）. These results are useful in helping local government to assess their assessment value.

Speed sensorless FCS-MPTC based on FOSM-MRAS five-phase permanent magnet synchronous motor

For the two-level five-phase permanent magnet synchronous motor(FP-PMSM)drive system,an improved finite-control-set model predictive torque control(MPTC)strategy is adopted to reduce torque ripple and improve the control performance of the system.The mathematical model of model reference adaptive system(MRAS)of FP-PMSM is derived and a method based on fractional order sliding mode(FOSM)is proposed to construct the model reference adaptive system(FOSMMRAS)to improve the motor speed estimation accuracy and eliminate the sliding mode integral saturation effect.The simulation results show that the FP-PMSM speed sensorless FCS-MPTC system based on FOSM-MRAS has strong robustness,good dynamic performance and static performance,and high reliability.