Flux Model Reference Adaptive System Research Articles

An MRAS Speed Observer Based on dq-axis Power Winding Flux for Sensorless Control of Standalone BDFIGs

This paper addresses a new mechanical rotor speed observer for sensorless control of standalone Brushless Doubly-Fed Induction Generators (BDFIGs) based on the dq axis power winding flux Model Reference Adaptive System (MRAS) observer. The observer is integrated in the control method for control the terminal frequency and voltage magnitude under various work conditions. The efficiency of the proposed observer and control strategy is proved by overall simulation results and confirmed by experiments. As illustrated without using physical rotor speed sensors, the sensorless control strategy integrating the proposed speed observer can keep effectively the frequency and amplitude of power winding voltage fixed at various rotor speeds and under various conditions of machine parameter and load changes.

Second-order sliding mode-direct torque control and load torque estimation for sensorless model reference adaptive system–based induction machine

In this article, an improved sensorless direct flux and torque control is presented for high-performance induction motor drive. This algorithm integrates the super twisting control approach with direct torque control and model reference adaptive system. The super twisting algorithm is a second-order sliding mode approach that uses a continuous control law in order solves the problems of chattering and enhances control robustness against various uncertainties. Besides, a load torque observer design bed on super twisting–based design is presented to improve the speed regulation loop and to increase its rejection ability of load disturbances. Furthermore, the stator flux model reference adaptive system is used as a sensorless algorithm for rotor speed reconstruction. The association of dual estimators can increase the control system’s reliability and reduce its cost. The effectiveness of the proposed control algorithm has been verified through simulation and experimental work using MATLAB/Simulink software and dSpace 1104 implementation board, respectively.

An MRAS Speed Observer Based on Control Winding Flux for Sensorless Control of Stand-Alone BDFIGs

This article presents a new rotor speed observer for the sensorless control of stand-alone brushless doubly fed induction generators based on the control winding flux model reference adaptive system structure. The observer is incorporated in the control strategy for regulating the terminal voltage magnitude and frequency under the different operating conditions. The capability of the proposed observer and control method is demonstrated by comprehensive simulation results and verified by experiments. As shown, without using physical speed sensors, the sensorless control algorithm incorporating the proposed speed observer can effectively maintain the amplitude and frequency of power winding voltage constant at different rotor speeds and under different conditions of load and machine parameter, e.g., inductance and/or resistance, variations.

Rotor Resistance Estimation of Vector Controlled Induction Motor Drive using GA/PSO t uned Fuzzy Controller

Induction motor with indirect field oriented control is preferred for high performance applications due to its excellent dynamic behavior. However, it is sensitive to variations in rotor time constant, especially variation in rotor resistance which needs to be estimated online. Conventionally the model reference adaptive system with fuzzy logic controllers as adaptation is used, which works satisfactorily for one particular operating condition and fails under variable operating condition. Therefore the need arises for a fuzzy controller whose parameters are tuned using evolutionary algorithm. In this paper, the input/output gain and the membership function parameters of the fuzzy system are optimized using genetic algorithm and particle swarm optimization to obtain an optimal designed fuzzy controller for rotor resistance estimation. The system is investigated in MATLAB/Simulink environment. Results shows that the steady state error in estimation of rotor resistance by the proposed controller under stringent operating condition is better with the proposed controller as compared to the conventional trial and error based fuzzy controller. Index Terms: Induction motor (IM), Indirect Rotor flux Field Oriented Vector Control (IRFOC), Rotor Flux Model Reference Adaptive system (RF-MRAS), Proportional Integral (PI) controller, Mamdani fuzzy controller, Genetic Algorithm (GA), Particle Swarm Optimization (PSO).

Encoderless Finite-State Predictive Torque Control for Induction Machine With a Compensated MRAS

An encoderless predictive torque control (PTC) is proposed in this paper. By using a rotor flux model reference adaptive system (MRAS) estimation method to PTC, the system has the virtue of low cost due to the absence of PWM and speed measurement components. PTC requires not only estimated speed but also estimated stator and rotor flux. In implementation, a compensated MRAS is considered for obtaining good flux estimations. The experimental results confirm that this control strategy has very fast dynamics, can adapt a very wide speed range, and shows good performance both at transient and steady states.

하이브리드형 압전 변압기의 고주파 승압 초퍼를 이용한 적응제어기법 유도전동기 속도 센서리스 벡터제어에 관한 연구

In this paper, recently, it is described to the piezoelectric transformer technology develops, because it was have to favorable characteristics such as electromagnetic-noise free, compact size, higher efficiency, and superior power density, flux linkage, noiseless, etc. its resonance frequency was used to output waveform of a sine wave. A rotor speed identification method of induction motor based on the theory of flux model reference adaptive system(FMRAS). The estimator execute the rotor speed identification so that the vector control of the induction motor may be achieved. The improved auxiliary variable of the model are introduced to perform accurate rotor speed estimation. The control system is composed of the PI controller for speed control and the current controller using space voltage vector PWM techniuqe and DC-DC converter. High speed calculation and processing for vector control is carried out by digital signal one chip microprocessor. Validity of the proposed control method is verified through simulation and experimental results.

Rotor flux-based MRAS for three level inverter fed induction motor drive using fuzzy logic controller

This paper proposes a sensorless vector control scheme for induction motor to operate at low speed. Low speed operation of rotor flux model reference adaptive system (MRAS) have the problems of dc drift and dead time due to pure integrator present in the reference model. A control scheme is proposed with non-linear integrator in the reference model of the rotor flux-based MRAS speed observer to obtain the compensating voltage. Adaptive model tuning is done with fuzzy logic controller (FLC) in place of PI controller. Multilevel inverter with space vector pulse width modulation (SVPWM) technique is used to improve the performance of drive. A SIMULINK model is developed to compare performance of conventional rotor flux MRAS and proposed control scheme with PI controller and FLC.

Sensorless Control of Induction Motor Drives at Very Low and Zero Speeds Using Neural Network Flux Observers

A new method is described which considerably improves the performance of rotor flux model reference adaptive system (MRAS)-based sensorless drives in the critical low and zero speed regions of operation. It is applied to a vector-controlled induction motor drive and is experimentally verified. The new technique uses an artificial neural network (NN) as a rotor flux observer to replace the conventional voltage model. This makes the reference model free of pure integration and less sensitive to stator resistance variations. This is a radically different way of applying NNs to MRAS schemes. The data for training the NN are obtained from experimental measurements based on the current model avoiding voltage and flux sensors. This has the advantage of considering all drive nonlinearities. Both open- and closed-loop sensorless operations for the new scheme are investigated and compared with the conventional MRAS speed observer. The experimental results show great improvement in the speed estimation performance for open- and closed-loop operations, including zero speed.