Nimal Lobo

Sensorless Control of Single Switch-Based Switched Reluctance Motor Drive Using Neural Network

Neural networks (NNs) have proven to be useful in approximating nonlinear systems and in many applications, including motion control. Hitherto, NNs advocated in switched reluctance motor (SRM) control have a large number of neurons in the hidden layer. This has impeded their real-time implementation with DSPs, particularly at high rotational speeds, because of the large number of operations required by the NN controller within a sampling interval. One of the ideal applications of NNs in SRM control is in rotor position estimation using only current and/or voltage signals. Elimination of rotor position sensors is practically mandatory for high-volume, high-speed, and low-cost applications of SRMs, for example, in home appliances such as in vacuum cleaners. In this paper, through simulation and analysis, it is demonstrated that a minimal NN configuration is attainable to implement rotor position estimation in SRM drives. The NN is trained and implemented on an inexpensive DSP microcontroller. NN training data, current, and flux linkage are obtained directly from the system during its operation. Furthermore, the chosen method is implemented on a single-switch-converter-driven SRM with two phases. This configuration of the motor drive is chosen because it is believed that this is the lowest cost variable speed machine system available. Experimental verification of this motor drive system is provided to demonstrate the viability of the proposed approach for the development of low-cost motor drives.

Novel Two-phase Switched Reluctance Machine using Common-Pole E-Core Structure: Concept, Analysis, and Experimental Verification

A novel two-phase switched reluctance machine (SRM) with a stator composed of E-core structure having minimum stator core iron is proposed. The E-core stator has three poles with two poles at the ends having windings and a center pole containing no copper windings. The center stator pole in the E-core is shared by both phases during operation. The air gap around the common stator pole has constant and minimum reluctance irrespective of rotor position by its unique design, and the two remaining stator poles at the ends experience variable reluctance with respect to rotor position. The stator is constructed with two independent and physically separate E-cores, and the rotor is composed of ten poles. Other pole combinations are possible. Phase excitation in the novel SRM gives short flux paths, hence reducing the magnetomotive force required to drive the machine, resulting in significant reduction of copper wire and core losses compared to existing two-phase SRMs with flux paths that traverse the entire stator back iron. The concept and principle of operation of this novel SRM and its comparison to existing two-phase SRMs are detailed in this paper. Comparison between finite-element simulations and magnetic equivalent circuit (MEC) analysis for inductance are made and compared to experimentally measured characteristics. Furthermore, comparisons between a conventional two-phase SRM and the novel SRM are made in terms of its weight and output torque. Manufacturability and cost savings of the unique SRM structure are presented. It is shown that the E-core SRM using common stator pole has 50% less iron in the magnetic path compared to a conventional two-phase SRM.

Sensorless control of single switch based switched reluctance motor drive using neural network

Neural networks (NNs) have proved to be useful in approximating non-linear systems and in many applications including motion control. Hitherto NNs advocated in switched reluctance motor (SRM) control have a large number of neurons in the hidden layer. This has impeded their real time implementation with DSPs at high speeds because of the high number of operations required by the NN controller and insufficiency of available time between two sampling intervals for computation and control. One of the ideal applications of NNs in SRM control is in rotor position estimation using only SRM current and or voltage signals. Elimination of rotor position sensors is absolutely required for high volume, high speed and low cost applications of SRM, say, in home appliances such as in vacuum cleaners. In this paper, through simulation and analysis, it is derived and demonstrated that a minimal NN configuration is attainable to implement rotor position estimation in SRM drives. The neural network was trained and implemented with an inexpensive DSP microcontroller for performance evaluation. Neural network training data, current i, and flux-linkage /spl lambda/, has been obtained directly from the system during its operation and was verified using finite element analysis (FEA) tools. Further the chosen method is implemented on a single switch converter driven SRM with two phases. This configuration of the motor drive is chosen because it is believed that this is the lowest cost variable speed machine system available. The theoretical results are correlated experimentally with this converter and machine configuration in order to demonstrate the viability of the proposed approach for the development of low cost motor drives.

Novel flux linkage control of switched reluctance motor drives using observer and neural network-based correction methods

From the perspective of control of switched reluctance motor (SRM) drives, current control is typically used as an inner loop control method. In this paper, observer and artificial neural network (ANN)-based novel flux linkage control of SRM drives is presented and examined as an alternate approach to current control. The main advantage of flux linkage control is computational simplicity due to the insensitivity of controller gains to machine operation conditions, while current control depends on controller gains which are very sensitive to self-inductance of SRMs. Flux linkage control needs a reliable flux linkage estimator for desirable control of SRMs. Integration method to estimate flux linkage from measured phase voltages, currents and resistances is commonly used, but it is sensitive to measurement error and white noise. Another way to measure the flux linkage is to use a look-up table which is very sensitive to input currents because it is current- and position-based data. In this paper, a simple observer-based voltage and ANN-based current correction method is proposed to overcome the measurement error. Furthermore, ANNs with two layers and five neurons are applied to produce an acceptable flux linkage estimate at each corrected current and measured position, instead of a look-up table. Finally, simulation results are presented to validate its performance.

M-Phase N-Segment Flux-Reversal-Free Stator Switched Reluctance Machines

This paper describes a class of switched reluctance machines (SRM) whose stators have no flux reversals. Previously published work describes a two-phase flux-reversal-free-stator SRM and this paper extends the concept to an SRM with any number of phases. For the first time in literature, SRMs with flux reversal free stators and flux reversal free rotor back irons are developed in concept and presented in their realizable forms. Reversal of magnetic flux in the iron core results in increased core-loss when compared to iron cores which do not experience flux-reversals. The novel SRMs in this paper have balanced radial forces. In addition, the stators can be constructed in a segmental fashion. The stators of the segmental SRM are magnetically isolated from one another. Segmental stators are advantageous in high power applications and applications where the stator diameter is limited by the maximum size of the stamping die. This paper also contains a generalized design method which describes how to accomplish an n-segment stator from an m-phase SRM. Furthermore, a sub-class of SRMs is also presented which, in addition to be above stated advantages has no flux-reversals in the rotor back-iron. Three novel three- phase SRMs are compared to a conventional SRM through finite element (FE) simulations. Static and dynamic finite element simulations presented show core-loss, efficiency, output power, torque ripple and radial forces for all four machines. The flux- reversal-free-stator concept is verified by FE simulations.

Comparison of Two Switched Reluctance Motors with No Flux-Reversal in the Stator

A comparison of two configurations of a novel two-phase switched reluctance machine (SRM) with no flux reversal in the stator iron is presented in this paper. Conventional SRMs have flux reversals that occur in sections of the yoke during commutation. The two novel SRMs compared in this paper have six stator poles. Each phase comprises of three poles separated by 120deg. The two rotor configurations contain three poles and nine poles, respectively. The comparison includes inductance and torque profiles, self-starting capability and torque ripple, weight, radial forces, core losses and in addition the unique features of the flux-reversal-free-stator SRMs. The results provide an indication of which machine is best suited for a particular application. Data for the comparison is obtained from dynamic finite element simulations