High Performance Speed Sensorless Finite-Set Predictive Thrust Control of a Linear Induction Motor based on MRAS and Fuzzy Logic Controller

This paper proposes a neural based MRAS (Model reference Adaptive System) speed observer suited for linear induction motors (LIM). Starting from the dynamical equation of the LIM in the synchronous reference frame in literature, the so-called voltage and current models of the LIM in the stationary reference frame, taking into consideration the end effects, have been deduced. Then, while the inductor equations have been used as reference model of the MRAS observer, the induced part equations have been discretized and rearranged so to be represented by a linear neural network (ADALINE). On this basis, the so called TLS EXIN neuron has been used to compute on-line, in recursive form, the machine linear speed. As machine under test, a complete dynamic model, based on the constructive elements of the LIM and taking into consideration the end effects by the definition of a proper air-gap function, has been adopted. The proposed MRAS observer has been tested in numerical simulation at high and low linear speeds of the inductor and has been further compared with both the classic MRAS observer taking into consideration the LIM's end effects and with the MRAS observer based on the RIM's equations, not considering the end effects.

The paper describes Model Reference Adaptive System (MRAS) observers for the speed estimation of an induction motor with direct torque and flux control. The first estimator is a reference frame MRAS (RF-MRAS) and the second estimator is a current based MRAS (CB-MRAS). At first, direct torque controlled induction motor drive with two estimators are implemented on Matlab-Simulink environment. Then, comparison of two observers is done by evaluation of the rotor speed difference. The simulation results confirm that both MRAS estimators are simple to simulate and experiment. By comparison of both observers, the CB-MRAS observer gives higher accuracy of the rotor speed estimation.

This study investigates the behavior of a Five-Phase Permanent Magnet Synchronous Motor (5P-PMSM) when an open-phase fault occurs. It applies a sensorless control approach that combines backstepping control with a Model Reference Adaptive System (MRAS) observer. Using measurable electrical quantities, including stator currents and voltages, the proposed method accurately estimates both rotor position and speed. The design of the backstepping controller follows a structured procedure to achieve global or semi-global stability, making it suitable for applications that demand reliable operation under changing conditions. Lyapunov theory is used to analyze and verify the stability of the combined observer and controller. The main objective of the research is to maintain motor performance and stable operation in the presence of an open-phase fault. Simulations conducted in MATLAB/Simulink compare normal motor performance with performance during the fault to evaluate the effectiveness of the control strategy. The findings show that the backstepping controller, supported by the MRAS observer, enhances fault tolerance and reduces the adverse effects. The simulation results confirm that the proposed approach sustains motor operation with minimal decline in performance, offering a dependable solution for industrial systems requiring resilience to faults.

In order to realize sensorless control for brushless doubly fed induction machine (BDFIM), this paper presents a model reference adaptive system (MRAS) observer, designed based on the error of the control winding current. Furthermore, a phase-locked loop is employed to estimate the current winding position and rotor speed. Consequently, a detailed theoretical derivation proves that the MRAS observer is stable and the dynamic performance is good. Thus, it does not cause any estimated speed error in steady state. Moreover, the estimated position error is bounded and trivial, thus its effects on the sensorless control of the BDFIM are neglected. The correctness, feasibility and robustness of the proposed sensorless control method are verified by means of experimental validation on a 30 kW test rig.

This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.

This paper considers the stability problem of the model reference adaptive control systems by means of the properties of hyperstable systems. A theorem concerning the hyperstability of model reference adaptive control systems is presented. This theorem directly gives a structure of the adaption mechanism. The results presented here include all the results obtained by Butchart, Shackcloth, Parks, Winsor, Roy, and Dressler. The hyperstability approach presented in this paper also allows for other solutions to the adaption mechanism and represents a general method for studying this type of adaptive systems. The results are directly applicable to the design of model reference adaptive control systems and they were verified for some particular cases by analogical simulation.

The objective of this research is to provide a method for design of control systems when the plant involved is highly variable in both structure and parameter values. This typifies many manufacturing processes, which in general are highly sensitive to intrinsic properties of the infeed material, have poorly understood or controlled boundary conditions, and often have highly non-linear characteristics. In addition, the disturbances most frequent in a manufacturing process are best characterized as parameter changes rather than state variable changes. The choice of a parameter adaptive control paradigm is obvious since it has the property of optimizing system response characteristics in the face of wide plant parameter variations. Model Reference Adaptive Systems (MRAS) enjoy an ease of performance specification, and again seem applicable; however, there is a restriction within current theory that the plant be characterized in its structure well enough to have a reference model of identical order. This restriction implies a static plant structure and more a priori information than is often present. During this contract the basic design characteristics of certain MRAS have been studied and problems with reduced order models identified, such as noise and input magnitude sensitivity and instability caused by high frequency learning signals. This has lead to certain solutions and resulting guidelines for design. This is then followed by design studies for a position servomechanism (which serves as the foundation of many automated manufacturing processes). This study expanded the domain from MRAS to self-tuning controllers (STC) and found that both can deal with reduced order models if the initial parameter values are not too distant from the actual values. However, the STC appears to be the more robust. 12 refs., 56 figs.

This paper describes a model reference adaptive system (MRAS) for the speed estimation of induction motor from measured terminal voltages and currents. The estimated speed is used as feedback in a vector control system. The MRAS approach has the immediate advantage in that the model is simple, very easy to implement and has direct physical interpretation. This paper studies the influence of the stator resistance variation on MRAS speed estimation. It has been shown that when a motor is running at high speed, the effect of error in stator resistance is usually quite negligible. But as the frequency approaches zero, this becomes more serious because the voltage drop on stator resistance becomes relatively larger as the frequency decreases. In comparison with rotor resistance adaptation, the stator resistance adaptation has received less consideration. In this paper, a new reference scheme has been used for stator resistance estimation. Under this reference scheme, one component of flux can be decoupled from the corrupting effects of the stator resistance and the other quadrature component can provide a near-instantaneous estimate of the stator resistance. In the system, the output of the stator resistance identifier updates the value of the stator resistance used in the MRAS speed estimator. Simulation of rotor field oriented speed sensorless control of VSI-fed induction motor using stator circuit variables is performed incorporating the on-line stator resistance estimation technology.

This article presents the sensorless control of a PMSM based on the FOC (Field Oriented Control) strategy and on PFC (Power Factor Correction), where the rotor speed is provided by speed observers. It presents the block structure of the PFC together with the numerical simulations achieved in Matlab/Simulink, on the improvement of the PF (Power Factor) of the supply circuit. It also presents the block structures of the FOC strategy and of the back-EMF (back electromotive force) sliding mode, MRAS (Model Reference Adaptive System), and PLL (Phase Locked Loop) type observers. The numerical simulations show the superiority of rotor speed estimation when using the back-EMF sliding mode observer compared to the rotor speed estimate provided by MRAS and PLL observers.

The efficiency of a separately excited DC motor is improved by controlling both armature current and field current simultaneously. The model reference adaptive control system (MRACS) can be useful for this control problem because parameter values and load disturbance (load torque) of controlled objects vary greatly. Two adaptive control systems for efficiency-optimized speed control are proposed on the basis of MRACS theory and the error system method. These are MRACS based on the current ratio method and on the voltage/current ratio method. In these systems, integral action is introduced into MRACS to cope with the step-load disturbance. Although the parameter variation of the voltage/current ratio MRACS method is large, the number of parameters to be adjusted is small as compared with that in the current ratio MRACS method. For each method, fast transient response and good characteristics are obtained for the parameter changes, step-load disturbance, and step desired signal in simulation studies. Efficiency is considerably improved by these methods at a light-load condition.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper presents a novel formulation of the Lyapunov-based ultimate boundedness theorem with explicit specification of transient and ultimate bounds, time to the ultimate bound, and the set of admissible initial conditions. The formulation is tailored to the structure of model reference adaptive control systems accounting for a partitioning of the state vector into a tracking error and a parameter estimation error. It allows for a computation of separate bounds for the single state vector partitions in favor of a single bound on the compound state, consisting of both partitions. This is of great practical importance because tracking error and parameter estimation error are of different magnitudes in general. Regarding model reference adaptive control systems, it is the state of the technology so far that the ultimate boundedness theorem is tailored to the structure of the considered system each time, again and again. It is hence manifest to formulate once a generic version of the theorem, tailored to model reference adaptive control systems, but not to reinvent the wheel for each case.

This paper deals with a model reference adaptive system (MRAS) estimator based on the second-order sliding-mode (SOSM) strategy for speed-sensorless direct torque control (DTC) of a six-phase induction motor. A compensated flux observer is proposed using the well-known supertwisting algorithm, which is inherently an SOSM technique. This observer is adopted as the reference model in the MRAS-based speed estimator to overcome the chattering problem of the classical sliding-mode techniques. The estimator is robust against parameter uncertainties and dc offsets. In addition, a supertwisting-algorithm-based second-order sliding-mode (ST-SOSM) controller is designed for the speed loop in the DTC strategy to increase its robustness under applying external load disturbances. The speed control loop needs the load torque signal. For this purpose, an ST-SOSM load torque observer is proposed, where it provides a robust performance without the penalty of high chattering. Simulation and experimental results confirm the validity and the effectiveness of the proposed approaches.

This project investigates the application of model reference adaptive system (MRAS) for the speed sensorless control of an induction motor. The rotor speed can be accurately estimated by employing the closed-loop observer named reactive MRAS. Therefore, this observer eliminates the need of a speed sensor for the control of the motor speed. The method is robust to stator and rotor resistance variations due to change of temperature. The dynamic system equations of the induction machines are formulated, and the motor control system performance is studied. Both scalar voltage-to-frequency (V/f) control and vector field oriented control (FOC) schemes, implemented using digital signal processor (DSP), are investigated. The design of the speed sensorless DSP-based controller is completed. Software packages have been developed to implement the design. An experimental system using the proposed controller has been built. Various tests have been conducted to verify the technical feasibility of the control technique. The experimental results confirm the feasibility of the proposed speed sensorless V/f control scheme using MRAS speed estimator. The designed V/f profile has been tested. Even with step change of the load or that of the command speed, the system can achieve the correct steady state after a short transient operation. The experimental results also confirm the feasibility of the proposed speed sensorless FOC control scheme using MRAS speed estimator. The current regulators meet the design requirements. Both the flux-producing current component and the torque-producing current component can be controlled separately. In the implementation, digital signal processor (DSP) TMS320 FL2407 and voltage source inverter (VSI) Skiip 342GD120-316CTV are employed. The modular strategy is adopted to develop the software package.

This paper introduces a speed-sensorless dual-field-oriented control (DFOC) strategy for induction motors (IMs). DFOC combines the advantages or rotor- and stator-field orientation to significantly reduce the parameter sensitivity of the control regarding the generation of the converter control variable. A simplified structure is also proposed, using only two regulators for the flux and speed control, eliminating the two current regulators. Related to sensorless control, the classical adaptation mechanism within an MRAS (model reference adaptive system) observer is replaced with artificial intelligence (AI)-based approaches. Specifically, artificial neural networks (ANNs) and recurrent neural networks (RNNs) are employed for rotor speed estimation. They offer significant advantages in managing complex and nonlinear systems, providing enhanced flexibility and adaptability compared to traditional MRAS methods. The effectiveness of the proposed sensorless control scheme is validated through both simulation and real-time implementation. The paper focuses on the ANN and RNN architectures, as deep learning models, in terms of the reliability and accuracy of rotor speed estimation under various operating conditions.

The paper investigates the improvement in sensorless vector control of induction machines through the use of a non-linear feedback integrator embedded in a standard rotor flux model reference adaptive system (MRAS)-based observer. The feedback integrator compensates for drift and dc-offset and is capable of open loop flux estimation down to frequencies below 0.1 Hz. The paper presents experimental results of sensorless speed controlled induction motor exploiting a MRAS observer. The sensorless system shows good performance down to frequencies of 0.2 Hz.