The performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors.

This article discusses the maximum torque per ampere (MTPA) control of synchronous motors, which have become an indispensable part of highly efficient motor drives. It explains the nature of torque produced by synchronous motors, ways to find its maximum and algorithms to operate at this point, despite changes of loads and motor parameter variations. The authors propose a classification of the MTPA methods, based on the features of each algorithm or group of similar methods. They demonstrate the conventional control scheme and discuss the modifications necessary for the implementation of each method. This article reviews existing MTPA control algorithms, discusses their pros and cons, and suggests possible areas of usage for each group of methods. The authors of the article share their vast experience in the industry and research aspects, which were obtained by developing industrial, commercial, traction, and military drives, and report on their views on the perspective of each method taken into consideration.

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.