A maximum torque per ampere control strategy for induction motor drives

Presents robust adaptive control technique for induction motors. The method is robust to parameter variations and the stability analysis is much simpler than other approaches such as Marine's adaptive input-output linearization method. Another main advantage of the authors' method is that they only require a reduced-order system to be linearly parametrizable (LP); the rest of the system dynamics can be highly nonlinear with no LP requirement. Full state feedback is not needed for implementation. In particular, the rotor flux measurement is not needed which consequently eliminates one of the major disadvantages in most induction motor control schemes. Load torque and rotor resistance can be unknown but bounded.

This paper deal with Backstepping control strategy for induction motor drive, using rotor flux and load torque observers. The proposed study uses a modified motor model with new variables, and Luenberger observers for both rotor flux and load torque. This work proposes a precise calculation method for controller parameters based on response time and damping coefficient. Simulation is performed in Simulink, results show that Backstepping works well by adding observers.

In this chapter, a method is studied to estimate the rotor resistance for indirect field-oriented control (IFOC) of induction motor drive. In model reference adaptive system (MRAS)-based sensorless speed estimation techniques, usually, the rotor speed estimation is achieved by assuming the rotor resistance to remain unchanged during the operation. However, in practice, the rotor resistance of the motor varies with time depending on the drive’s temperature. This variation in rotor resistance accounts for error in the estimation of the rotor speed of the sensorless MRAS scheme-based induction motor speed estimator. To minimize this error caused due to rotor resistance variation, a rotor resistance estimation method is provided. To verify the feasibility and accuracy of the given method, a three-phase induction motor is being used. From the simulation results obtained, it can be observed that the proposed method provides an accurate estimation of rotor resistance, and simultaneous rotor speed estimation is achieved with lesser effects of rotor resistance variation.

This paper proposes a new hybrid vector control scheme for induction motor without velocity and position sensors. It has the following interesting and distinguished features. It is a completely seamless hybrid of indirect and direct orientation schemes and indirect one is quite new and simple. Employed field orientation is pure rotor flux orientation. Field orientation scheme requires neither velocity nor position estimation at all. Estimates of both rotor and stator fluxes can be obtained. It is completely free from variation of rotor electrical parameters in most of operation cases. Instant estimate of rotor velocity for speed control is available. Extensive numerical experiments are carried out in order to examine torque response with constant velocity in both of motoring and regenerating operations, torque response in four-quadrant operation with variable velocity, and speed response to variable command in which maximum and minimum values are 500 and 1 (rad/s) respectively, acceleration and deceleration are ±400 (rad/s2). Result of the experiments shows possibility of high potential usefulness of proposed hybrid scheme.

This paper presents a new robust adaptive current control strategy for induction motor drive systems. This control scheme is based on an adaptive pole placement control strategy (APPC) integrated to a sliding mode control scheme (SMC). The proposed APPC employs the internal model principle for achieving a zero steady state tracking error of machine stator currents. Moreover, this current controller can be implemented on the stator reference frame transformations. With this control scheme, fast transient response and robustness to parametric uncertainties and unmodeled disturbances are obtained. These disturbance can be owing to saturation, temperature changes or loading conditions. Simulation and experimental results are presented for showing the effectiveness of the proposed close-loop current control strategy.

Accurate knowledge of stator and rotor resistance variations in an Induction Motor (IM) has received a lot of attention due to its significance in improving the performance of the Machine. The paper deals with the application of the Extended Kalman Filter (EKF) algorithm in the purpose of stator and rotor faults detection in IM. The hypothesis on which detection is based is that the apparent rotor (stator) resistance will increase when a rotor bar breaks (shorted stator-turns). Mathematical model of the extended state estimator is presented, in which, the stator and rotor resistances are added as additional electromagnetic state variables.

This paper presents the mapping of a hybrid of two most popular control strategies for induction motor drive: field oriented control (FOC) and direct torque control (DTC), with the multilayer feedforward neural network. One neural network is used to map FOC scheme and another network is used to map DTC scheme. The training is made separately with the input-output data obtained from the simulation result of the structural based models. Two trained networks are combined to represent the neural network hybrid control (NNHC). The criterion for switching between the two control schemes is the operating speed of the motor. The simulation results show that this proposed scheme is characterized by fast dynamic performance and good steady state characteristic. Real time implementation of NNHC scheme is discussed.

The detection of broken rotor bars in induction motors is studied. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. Here, the apparent rotor resistance is that in the balanced steady-state single-phase electrical model of an induction motor. To detect broken rotor bars, measurements of stator voltage, stator current, stator excitation frequency, and rotor velocity are taken over a small range of velocity. These measurements are processed by a near least square error estimator to produce estimated motor states and parameters. In particular, rotor resistance is estimated and compared with its nominal value to detect broken rotor bars. As part of this estimation process, it is necessary to compensate for the thermal variation in rotor resistance. The broken rotor bar detector is evaluated experimentally using one stator and three rotors, one with a broken bar, from identical 3 hp induction motors. The estimated rotor resistance is clearly greater for the rotor with the broken bar. >

This paper proposes a finite control set model predictive control (FCS-MPC) based dual-motor control strategy for induction motor (IM) and permanent magnet synchronous motor (PMSM). By establishing a dual-motor current prediction model, the unified cost function and optimization strategy are constructed, and the torque synchronization constraint and total torque ripple suppression constraint are designed. Compared with the traditional control strategy, the proposed control algorithm verifies the feasibility of FCS-MPC in the IM-PMSM dual-motor system. Moreover, the proposed strategy has good transient performance, and realizes the torque synchronous control and total torque ripple suppression of the dual-motor system.KeywordsInduction motorPermanent magnet synchronous motorFinite control set model predictive controlDual-motor

The vector control method is widely used for induction motor drives. In these applications, a rotational transducer such as a shaft encoder is used. A rotational transducer, however, cannot be mounted in some cases, such as motor drives in a hostile environment or high-speed motor drives. Several sensorless vector-controlled induction machine drive methods have been proposed. These methods have the disadvantage that rotor resistance variation causes errors in the estimation of the motor speed. It is difficult to estimate the motor speed and the rotor resistance simultaneously, because the equivalent rotor resistance is the ratio of the rotor resistance to the slip at steady state. Therefore, we have proposed a method of simultaneously estimating the motor speed and the rotor resistance of an induction motor by superimposing ac components on the field current command. This paper presents a modified method of the rotor resistance adaptation. The validity of the proposed method is verified by experiments. © 1998 Scripta Technica, Electr Eng Jpn, 125(2): 65–72, 1998

Generally, vector controllers in induction motors tend to suffer from the influence of rotor resistance variation caused by temperature and load torque conditions. Several rotor resistance identification methods have been proposed in the literature. In this paper, a novel rotor resistance identification technique is proposed based on variables in the magnetic suspension control loops. When static force, such as a shaft weight, is applied, force commands are automatically generated in the opposite direction to satisfy the force equilibrium. However, the amplitude and direction of the force command vary in accordance with the rotor resistance variation. Thus, the rotor resistance can be identified from the suspension force command. In addition, influence of temperature drift in gap sensors is investigated. The effectiveness is confirmed experimentally. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(1): 86–97, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20423

This paper analyzed a dead-time effect and zero-current clamping phenomenon in FOC control strategy, and then proposed corresponding compensation measures. The Field Oriented Control (FOC) strategy of brushless motor is improved according to this Dead-time Compensation measure. On the basis of the existing FOC control theory, a dead-time hysteresis loop is added near the current zero-crossing point to prevent current fluctuation and zero-point clamping from causing errors. The dead-time compensation is carried out in each PWM cycle according to the principle that the actual volt-second area is equal. Finally, the feasibility of the method was verified by experiments, the results showed that the compensation method effectively improves the current waveform and improves the stability of the motor operation, and it proved to be of good use value.

The main drawback of the indirect vector control of the squirrel cage induction motor (SCIM) is the impairing of the drive performance because of the lost of the field orientation caused by the difference between rotor resistance of the machine, varying with temperature, saturation, skin effect, and its corresponding in the model of the controller (fixed). Rotor resistance estimator of the SCIM was designed using support vector machines (SVM) together with model reference adaptive system. The drive with variable rotor resistance was simulated, and flux error records were obtained from current and voltage models. SVM was trained with those flux error records and variable rotor resistance; the great generalization potential of the SVM was confirmed during the training stage, since with a small number of support vectors, the resistance value was predicted for a great quantity of samples that were not considered in the training stage. The drive with the rotor resistance estimator in closed loop showed a well performance when was undergone to other operation conditions different to that from the training signals were obtained and nevertheless to achieve an excellent estimation of the rotor resistance

This paper presents the vector control of Induction Motor (IM) supplied by a photovoltaic generatorwhich is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution is used toovercome the induction motor rotor resistance variation problem, which can affect negatively the performanceof the speed control. To overcome the rotor resistance variation, an adaptive Proportional-Integral controller isdeveloped with gains adaptation based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to guaranteea high performances of electric drive systems against the parametric variations. The proposed control algorithmis tested by Matlab-Simulink. Analysis of the obtained results shows the characteristic robustness to disturbancesof the load torque and to rotor resistance variation compared to the classical PI control and Model ReferenceAdaptive System (MRAS) rotor resistance observers.

It is well known that the performance of the (industry standard) field‐oriented control (FOC) for induction motors is highly sensitive to uncertainties in the rotor resistance. In this paper we describe how to use supervisory control to obtain an adaptive implementation of FOC for current‐fed machines. The unknown rotor resistance is assumed to belong to a discrete set, while the uncertain load torque ranges in a given compact set. Even though no restrictions area prioriimposed on the size of these sets, their definitions reflect the prior knowledge of the designer, which is effectively incorporated in the supervisory control algorithm. The supervisor selects from these sets values for the parameters to be applied to the FOC, a choice that is made by continuously comparing suitably defined performance signals. We prove that the proposed supervisor achievesglobal stabilizationof the system when the load torque is known to belong to a given finite set of values. Apparently, this is the first globally convergent adaptive algorithm for current‐fed machines which simply adds adaptation to the widely popular FOC and is not a radically new complicated controller, hence it is more likely to be adopted by practitioners. Some simulation results illustrate the properties of the algorithm. Copyright © 2001 John Wiley & Sons, Ltd.