PI and fuzzy estimators for tuning the stator resistance in directtorque control of induction machines

Direct torque control (DTC) of induction machines uses the stator resistance of the machine for estimation of the stator flux. Variations of stator resistance due to changes in temperature or frequency make the operation of DTC difficult at low speeds. A method for the estimation of changes in stator resistance during the operation of the machine is presented. The estimation method is implemented using proportional-integral (PI) control and fuzzy logic control schemes. The estimators observe the machine stator current vector to detect the changes in stator resistance. The performance of the two methods are compared using simulation and experimental results. Results obtained have shown improvement in DTC at low speeds     

Learning techniques to train neural networks as a state selectorfor inverter-fed induction machines using direct torque control

Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control induction machines using direct torque control (DTC). A neural network is used to emulate the state selector of the DTC. The training algorithms used in this paper are the backpropagation, adaptive neuron model, extended Kalman filter, and the parallel recursive prediction error. Computer simulations of the motor and neural-network system using the four approaches are presented and compared. Discussions about the parallel recursive prediction error and the extended Kalman filter algorithms as the most promising training techniques is presented, giving their advantages and disadvantages     

Tuning the stator resistance of induction motors using artificialneural network

Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC) in which the stator resistance is a main parameter. In this paper, an artificial network (ANN) is used to accomplish tuning of the stator resistance of an induction motor. The parallel recursive prediction error and backpropagation training algorithms were used in training the neural network for the simulation and experimental results, respectively. The neural network used to tune the stator resistance was trained on-line, making the DTC strategy more robust and accurate. Simulation results are presented for three different neural-network configurations showing the efficiency of the tuning process. Experimental results were obtained for one of the three neural-network configurations. Both simulation and experimental results showed that the ANN have tuned the stator resistance in the controller to track actual resistance of the machine     

Switched reluctance motor modeling with on-line parameteridentification

A nonlinear model with on-line parameter estimation using recursive identification for switched reluctance motors (SRMs) is presented. The model is robust toward parameter variations in the motor or any system disturbances. The parameters of the model are adjusted to account for errors in rotor position, which allows the use of crude inexpensive position sensors. The proposed modeling approach allows self-tuning of SRMs in a production unit. The simulations and experiments performed to test the model demonstrate the accuracy of estimation of the model     

Design and implementation of a closed-loop observer and adaptivecontroller for induction motor drives

This paper discusses the implementation and experimental results of a closed-loop rotor flux observer and model reference adaptive system (MRAS) of a direct field-oriented control (FOC) of an induction motor drive. The motor was supplied from a high-frequency (20 kHz) AC resonant link via a MOS-controlled-thyristor (MCT)-based bidirectional converter. Hardware and software implementations of the various motor control functions are presented. The closed-loop observer combines the current and voltage models via a speed-dependent gain (SDG). The current model was formulated to operate in the rotor reference frame and requires only an encoder angle and not the actual rotor speed for implementation. The closed-loop observer permits the use of a pure analog integrator to calculate an adequate stator flux. The use of an AC resonant link further complicated an all-digital calculation of the stator flux. The observer and adaptive controller were tested on a 400-Hz 2-hp induction motor for low and high speeds. The closed-loop observer showed sensitivity at low speeds to the rotor circuit time constant which attributed to the current model rotor flux estimation. At high speed, the closed-loop observer followed the voltage model rotor flux estimation attributes. The MRAS was able to improve the complete speed response by correcting the current model rotor flux observer for errors in estimation of its parameters     

Performance of MCT's in a current-regulated AC/DC PDM converter

An AC/DC current-regulated pulse density modulated (PDM) power converter is presented. The MOS-controlled thyristor (MCT) is used in this power converter because of its promising advantages over existing power devices. A unique bidirectional switch configuration is used, which allows the MCT to switch easily at zero voltage. The converter performance is evaluated driving a 5 hp induction motor. MCT switching was performed satisfactorily, but the device fails before its ratings are reached. The MCT will be better than other devices when used in applications that require critical timing and high voltage blocking capability     

A simple two-switch cycloconverter for variable-frequency low-speedapplications

A two-switch cycloconverter circuit that converts single-phase alternating current into an effective three-phase alternating current is presented. Since this design features only two bidirectional switches, it has a simple and inexpensive construction. The design requires a control circuit, which assures that the average output voltage on its two switches is sinusoidal with a 60° difference in phase. The results of some simulation studies and an experimental model of the two-switch cycloconverter are discussed. Some problems and potential areas of circuit improvement are presented     

A New Torque and Flux Controller for Direct Torque Control of Induction Machines

This paper presents the implementation of a high-performance direct torque control (DTC) of induction machines drive. DTC has two major problems, namely, high torque ripple and variable switching frequency. In order to solve these problems, this paper proposed a pair of torque and flux controllers to replace the hysteresis-based controllers. The design of these controllers is fully discussed and a set of numerical values of the parameters for the proposed controllers is given. The simulation of the proposed controllers applied to the DTC drive is presented. The simulation results are then verified by experimental results. The hardware implementation is mainly constructed by using DSP TMS320C31 and Altera field-programmable gate array devices. The results prove that a significant torque and stator flux ripples reduction is achieved. Likewise, the switching frequency is fixed at 10.4 kHz and a more sinusoidal phase current is obtained     

实数编码的遗传算法及其在逆变器馈电交流电机中的应用

实数编码的遗传算法及其在逆变器馈电交流电机中的应用＠王秀峰￥南开大学计算机与系统科学系＠ＬｕｉｓＡＣａｂｒｅｒａ＠ＭａｌｉｋＥＥｌｂｕｌｕｋ＠ＨｅＷｅｉ遗传算法，神经网络实数编码的遗传算法及其在逆变器馈电交流电机中的应用王秀峰（南开大学计算机与系统科学系天...     

A self-tuning controller for switched reluctance motors

A self-tuning controller for switched reluctance motors is presented, A torque ripple minimizing controller is implemented with online system identification and adaptation technique. The controller uses a nonlinear model of the machine that is updated online using a recursive identification algorithm. The real-time approach improves the overall robustness of the system. Experimental results are presented to validate the self-tuning algorithm