Malik E. Elbuluk

PI and fuzzy estimators for tuning the stator resistance in direct torque 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.

Fuzzy controller for inverter fed induction machines

A fuzzy logic controller for direct self-control of an induction machine is presented. A response faster than that of the conventional direct self-controller during startup and during a step change in torque is achieved. To improve the system performance at low speeds a fuzzy resistance estimator is proposed to eliminate the error due to the change in stator resistance. At constant flux and torque commands any change in stator resistance of the induction machines causes an error in stator current. This error is utilized by the fuzzy resistance estimator to correct the stato resistance used by the controller to match the machine resistance. Both fuzzy controller and fuzzy resistance estimator are simulated for a 3 hp induction motor. The simulation results demonstrate good performance.<<ETX>>

Torque ripple minimization in switched reluctance motors using adaptive fuzzy control

An adaptive fuzzy control scheme for torque ripple minimization of switched reluctance machines (SRM) is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque upto the motor base speed. The torque is generated over the maximum positive torque producing region of a phase. This increases the torque density and avoids high current peaks. The controller is robust towards errors in the rotor position information which means inexpensive crude position sensors can be used. Detailed simulation and experimental results are presented. The controller shows good response in both cases.

Sampled-data modeling and digital control of resonant converters

A sampled-data model to describe the dynamics of large signals and of small perturbations away from a cyclic steady state is developed. Associated transfer functions are obtained. The application of the model is illustrated by correlating the analysis with simulation results obtained for a series resonant DC/DC power converter. A discrete-time microprocessor-based controller, designed using the aforementioned dynamic model, has been built and tested using a simulation for a series-resonant DC/DC converter set up on the Massachusetts Institute of Technology Parity Simulator. The control methods implemented are state feedback and periodic output feedback, each designed to achieve a specified set of closed-loop poles. The controller has been implemented using the Parity Simulator generalized controller. Results of the closed-loop response showed an improvement over the open-loop response. In addition, the effect of the microprocessor computation delay on the closed-loop dynamics of the converter is investigated. >

A sliding mode observer for sensorless control of permanent magnet synchronous motors

A number of estimation techniques have been developed to achieve speed and position sensorless permanent magnet synchronous motor (PMSM) drives. Most of them suffer from variation of motor parameters such as the stator resistance, stator inductance and torque constant. Also, it is known that conventional linear estimators are not adaptive to variations of the operating point in a nonlinear system. The sliding mode technique has shown promising results when estimating or controlling nonlinear systems. This paper presents a sliding mode observer (SMO) for estimating the position and speed of a permanent magnet synchronous motor (PMSM) to achieve sensorless drive system. The technique can be generalized to other motor drives and motion control systems. The effect of the variations of motor parameters such as torque constant, stator resistance and stator inductance on the position and/or speed estimations, over a wide speed range, have been studied. Compared to other methods, the observer is more robust to operating conditions and parameter uncertainties. Simulations show that the observer is robust to 200% parameter detuning.

Torque ripple minimization in switched reluctance machines over a wide speed range

A torque ripple minimization technique for switched reluctance motors (SRM) suitable for applications requiring wide speed range of operation is presented. A new, simple, yet efficient commutation strategy is proposed. The commutation algorithm is speed dependent and uses a real-time approach instead of precalculated stored data. The model used for the SRM in the controller can also be updated in real-time. Simulation and experimental results verify the feasibility of implementing the proposed control algorithm in real-time.

Tuning the stator resistance of induction motors using artificial neural 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.

Neural-network-based model reference adaptive systems for high-performance motor drives and motion controls

A number of estimation techniques have been developed to achieve speed or position sensorless motor drives. However, most of these suffer from the variation of motor parameters such as the stator resistance, stator inductance or torque constant. It is known that conventional linear estimators are not adaptive to variations of the operating point. Also, model reference adaptive systems (MRAS) have been shown to give better solutions for on-line adaptation and estimation problems, but the adapting mechanism is mostly linear. Neural networks (NN) have shown better results when estimating or controlling nonlinear systems. This paper presents model reference adaptive systems with neural network-based adaptation mechanism, to achieve more robust control systems. The technique can be generalized to many motor drives and motion control systems. It is applied in this paper to a permanent magnet synchronous motor (PMSM) drive. The effects of torque constant and stator resistance variations on the position and/or speed estimations over a wide speed range have been studied. In particular, the rotor speed and/or position neural estimators with on-line adaptation of torque constant and stator resistance are studied. The neural network estimators are able to track the varying parameters, speed and position at different speeds with consistent performance. Compared to other methods, they are adaptive to operating conditions and are easy in design. Simulation results with experimental implementation and results that justify the claims are presented.

Fuzzy implementation of direct self-control of induction machines

A system with fast torque response is very beneficial in applications where direct torque control is highly desirable. The response of direct self control is slower during start-up and during change in command torque. Fuzzy control is used for the implementation of direct self control to improve its slow response. Experimental implementation of the fuzzy logic controller was carried out to verify the behavior of the controller. The controller was implemented with a single board computer that uses a TMS320C14 DSP. The experimental results with fuzzy control are compared with those of the conventional direct self controller. The starting flux and torque response and the responses to step changes in command torque with fuzzy implementation showed a considerable improvement over the conventional control. The steady state response for both controllers are the same. >

Energy-efficient C-dump converters for switched reluctance motors

Two energy-efficient converter topologies, derived from the conventional C-dump converter, are proposed for switched reluctance motor (SRM) drives. The proposed topologies overcome the limitations of the conventional C-dump converter, and could reduce the overall cost of the SRM drive. The voltage ratings of the dump capacitor and some of the switching devices in the proposed converters are reduced to the supply voltage (V/sub dc/) level compared to twice the supply voltage (2V/sub dc/) in the conventional C-dump converter. Also, the size of the dump inductor is considerably reduced. The converters have simple control requirements, and allow the motor phase current to freewheel during chopping mode. Simulation and experimental results of the converters are presented and discussed.