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Planetary exploration missions and deep space probes require electronics capable of low-temperature operation. Such electronics will not only improve circuit performance and reliability, but also increase system efficiency, and reduce development and launch costs. DC/DC converters are an essential part of most aerospace power management distribution systems. Therefore, dc/dc converters that can operate at cryogenic temperatures are crucial for space missions where low temperatures are encountered. An important component of a dc/dc converter is the pulsewidth modulation (PWM) chip that provides the control to the converter main switches. In the process of designing low-temperature dc/dc converters, experimental investigations were carried out to evaluate the performance of a number of high-speed PWM chips as a function of temperature in the range of 25 [degrees]C to -190 [degrees]C. These IC chips ranged in their electrical characteristics, modes of control, packaging options, and applications. This paper presents and discusses the experimental procedures along with the experimental data obtained on the investigated chips. Index Terms--Cryogenic electronics, current-mode pulsewidth modulation (PWM) control, dc/dc converters, deep space power, pulsewidth modulation (PWM) ICs, voltage-mode pulsewidth modulation (PWM) control.

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance, or torque constant. For example, the conventional linear estimators are not adaptive to variations of the operating point. The model reference adaptive systems (MRASs) have been shown to give better solutions with online adaptation, but the adapting mechanism is mostly linear. Neural networks (NNs) have shown better results when estimating or controlling nonlinear systems. This paper combines the online adaptation of MRASs with the ability of NNs for better modeling of nonlinear systems. It presents an MRAS using an NN in the adaptation mechanism. The technique is applied to a permanent-magnet synchronous motor 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. The NN estimators are able to track the varying parameters at different speeds with consistent performance. Simulation and experimental implementations and results are presented. Index Terms--Model reference adaptive systems, motion control, motor drive, neural networks, online parameter adaptation, permanent-magnet synchronous motor.

An adaptive fuzzy control scheme for torque-ripple minimization of switched reluctance machines is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque up to 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 toward 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. Index Terms - Adaptive fuzzy logic control, high-performance drive, real-time digital signal processor implementation, switched reluctance machine.

A torque-ripple minimization technique for switched reluctance motors (SRM's) is presented. The technique is particularly suitable for applications requiring wide speed range of operation. 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. Index Terms - Switched reluctance machines, switched reluctance machine control, torque-ripple minimization.

A nonlinear model with on-line parameter estimation using recursive identification for switched reluctance motors (SRM's) 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 SRM's in a production unit. The simulations and experiments performed to test the model demonstrate the accuracy of estimation of the model. Index Terms - Parameter identification, switched reluctance motor modeling, switched reluctance motors.

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 the 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.

An induction machine operated with a direct self controller (DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic is used in conjunction with direct self control to minimize these problems. A fuzzy logic controller chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determined by observing the vector diagram of flux and currents. The operation of the direct self controller becomes difficult at low speeds due to the effect of change in stator resistance on the flux measurements. 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 stator 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 a good performance.