Modern electric machines are attracting great interest from the research community as a result of the increasing number of current applications, including electric vehicles and wind power generators, among others. Different machines, power converters, and control technologies are used, and the number of sensors is usually minimized to reduce the total cost of the system. Particularly interesting are the current and speed sensors, which are essential to the normal functioning of the entire system. This work analyzes different calibration techniques of these sensors, using as a case example a five-phase induction motor drive. Experimental results are included to show the influence of calibration techniques on the system analyzed. The obtained results can be extrapolated to any other similar system.

Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.

Transceivers built to modern communication standards tend to be as digital as possible, including the radio‐frequency stages. This forces the digital‐to‐analogue converters (DACs) in the transmitter section to have a large bandwidth. DACs based on sigma‐delta (SD) modulation represent a good choice in modern digital technologies as they have a simple analogue circuitry with limited accuracy requirements. Error‐feedback (EF) architectures are widely used in the realisation of SD modulators. In many applications, DAC output has a small number of bits. In that case, the noise transfer function (NTF) must be of high order (to achieve a high dynamic range) and of the infinite impulse response (IIR) type (for the sake of stability). Concerning its implementation, one of the main challenges comes from the speed limitation of the technology. In this sense, time‐interleaving (TI) allows the designer a trade‐off between complexity and speed. Transforming the EF architecture into its TI counterpart is not straightforward for IIR NTFs. A procedure for this transformation is proposed, and a case study is described for a third‐order modulator. A method of coefficient rounding is also proposed to simplify the digital implementation of the modulator while avoiding mismatches between the parallel paths of the TI modulator.

The Finite State Model Predictive Control (FSMPC) of variable speed drives is the subject of many works in the recent literature. Many variants of FSMPC exist, each aiming at an aspect such as the complexity of the cost function, switching frequency, current quality, etc. In the case of multiphase drives, two popular variants are the monovector and multivector techniques. Despite past efforts to compare different techniques, the field must still reach a consensus regarding the relative merits of each one. This paper presents a new method to compare two families of FSMPC. The method is based on a reduced set of figures of merit using the current modulation index as the variable. The comparison is made for the equal usage of the power converter in terms of commutations. The results point to better values for the figures of merit for the monovector that, in addition, portrays more flexibility and better DC link usage.

Multiphase drives are of interest in transportation applications such as electric propulsion, where energy efficiency has a direct impact on range and on the feasibility of extended missions. Efficiency can be improved during the design phase, but for a given hardware platform, further gains must come from the control of the electrical drive. In this context, this paper addresses the issue of weighting factor elimination versus weighting factor selection in predictive stator current control of multiphase drives, where three cost function designs are compared. The first is a conventional formulation with a single weighting factor selected offline and held constant at all operating points. The second is a ranking-based scheme inspired by the Technique for Order of Preference by Similarity to Ideal Solution, in which the cost function terms are treated as separate targets and the candidate control action is selected from the aggregated classification, so that no weighting factor is required. The third retains the structure of the first, but this factor is programmed with the operating point using values obtained offline from the minimization of a meta cost function that combines multiple objectives. Experimental tests are conducted on a five-phase induction motor to assess and compare the performance of the three methods. The results show that no constant weighting factor performs well over the entire operating range, and that the ranking-based scheme, although it removes the weighting factor, still yields a fixed compromise. Scheduling the weighting factor with the operating point instead reduces the tracking error and the switching frequency simultaneously over most of the tested range, and hence the switching losses, which no constant value can achieve.