Sensorless drives for permanent magnet (PM) and reluctance synchronous motors are quite interesting options in modern ac drives. Fundamental-frequency observers provide a rotor position estimate, to be used instead of that measured by delicate sensors. The estimation algorithms are based on either the extended electromotive force or active flux concepts. They feature easy implementation, although their reliability may be undermined by both corrupted measurements and parameter variations, especially in the case of anisotropic rotor structures. Through an analytical sensitivity analysis, this article investigates the influence of model uncertainty on the observed position. The substantial relationships between cause (parameter uncertainty or measurement error) and effect (position error) are validated by experimental results, which also highlights the effects of the q-axis inductance and resistance values on the electric measurement sensitivities. The resulting functions and graphic plots are useful to draw interesting considerations about the motor selection and the control design of the sensorless drive.

The general trend toward more intelligent energy-aware ac drives is driving the development of new motor topologies and advanced model-based control techniques. Among the candidates, pure reluctance and anisotropic permanent magnet motors are gaining popularity, despite their complex structure. The availability of accurate mathematical models that describe these motors is essential to the design of any model-based advanced control. This paper focuses on the relations between currents and flux linkages, which are obtained through innovative radial basis function neural networks. These special drive-oriented neural networks take as inputs the motor voltages and currents, returning as output the motor flux linkages, inclusive of any nonlinearity and cross-coupling effect. The theoretical foundations of the radial basis function networks, the design hints, and a commented series of experimental results on a real laboratory prototype are included in this paper. The simple structure of the neural network fits for implementation on standard drives. The online training and tracking will be the next steps in field programmable gate array based control systems.

The high-frequency signals normally used for sensorless operations in anisotropic permanent-magnet synchronous motors cause unwanted torque ripple, noise, and vibrations. The target of the present research is to extend downwards the speed range in which the sensorless drive can work without any signal injection. Specifically, this paper proposes a new back-electromotive force observer that eliminates the instability that may arise in conventional observers when the motor drive operates in generating mode. Two different versions of the observer are proposed and discussed, to include motors with high harmonic content in the back-electromotive force. The detailed mathematical analysis of the proposed observer is supported by simulations and the practical feasibility is proven through experimental results.

As neodymium and other rare earth materials become a critical commodity, the exploitation of reluctance torque in synchronous motors is certainly an interesting option. Alternative motor topologies span from internal permanent magnet motors to pure reluctance machines. Anyway, any anisotropic structure suffers of some magnetic nonlinearity that call for more sophisticated models to get an efficient torque control. Neural network based algorithms are good candidates for modelling the current-to-flux linkages curves of synchronous reluctance motors, but so far their use was limited by the inherent complexity and the computational burden. This paper proposes the use of a special kind of neural networks, namely, the radial basis function networks, to get the magnetic model of any synchronous motor, including saturation and cross-coupling effects. Through experimental evidence, it will be shown that the structure is light enough to be implemented, trained and self-updated online on standard high-end ac drives. The model is used to track online the maximum torque-per-ampere working point of a synchronous reluctance motor drive.

The presence of rare-earth materials in permanent magnet synchronous motors has shifted the attention of the research towards the rediscovery of the synchronous reluctance motors. However, they present a highly nonlinear magnetic structure that makes the current control design a troublesome task. Standard proportional-integral controllers may fail in assuring the desired dynamic performances in the whole operating region. However, their simplicity is still attractive, when compared to other more complex controllers, such as the predictive ones. This paper aims at proposing two modified proportional-integral controllers tailored on the synchronous reluctance motors magnetic characteristics. At the heart of the process stands the knowledge of an accurate model of the motor. The design process is thus accurately described and both simulation and experimental tests are reported.