Implementation of a DSP based real-time estimator of inductionmotors rotor time constant

Implementation of ac drives insensitive to parameter variations is an important need in the field of high performance drives. For drives controlled by the indirect rotor flux oriented control method (IRFOC), the rotor time constant (τ_r = L_r/R_r) exerts a dominant role in the loss of dynamic performance and its variation results in an undesirable coupling between flux and torque of the machine. This paper presents a new scheme for on-line estimation of rotor time constant using dq representation of the model in the stationary reference frame and measurements of accessible motor variables only (voltages, currents and speed). The estimator is tested by simulation in the MATLAB/SIMULINK environment and validated experimentally on a 1/4 hp squirrel cage motor and a 1/4 hp wound rotor motor with implementation on a TMS320C31 digital signal processor     

Application of the current-injected modeling approach toquasi-resonant converters

This paper describes how the current-injected (CI) method, which has been applied only to pulse-width modulation (PWM) DC-DC power converters, can be extended to quasi-resonant (QR) power converters. The methodology for extending this small-signal modeling approach is described in detail. It is also shown that QR dynamic models are easy to obtain since they are derived directly from PWM power converter models. These new models result in a unified block diagram from which zero-voltage-switching (ZVS) or zero-current-switching (ZCS) transfer functions of the basic topologies, such as buck, boost, and buck-boost operated in half-wave (HW) or full-wave (FW) modes, are found. As an application of this method, a ZVS boost power converter and ZCS boost power converter were fabricated and tested. In addition, small-signal models of these power converters were derived with the help of the state-space averaging (SSA) method. The agreement of the CI method simulations with the experimental results for the two QR power converters is comparable or better than that of the SSA method     

Field-oriented control of induction motors using neural-networkdecouplers

This paper presents a novel approach to the field-oriented control (FOC) of induction motor drives. It discusses the introduction of artificial neural networks (ANNs) for decoupling control of induction motors using FOC principles. Two ANNs are presented for direct and indirect FOC applications. The first performs an estimation of the stator flux for direct field orientation, and the second is trained to map the nonlinear behavior of a rotor-flux decoupling controller. A decoupling controller and flux estimator were implemented upon these ANNs using the MATLAB/SIMULINK neural-network toolbox. The data for training are obtained from a computer simulation of the system and experimental measurements. The methodology used to train the networks with the backpropagation learning process is presented. Simulation results reveal some very interesting features and show that the networks have good potential for use as an alternative to the conventional field-oriented decoupling control of induction motors