Abstract
Indoor localization systems are an important topic in the field of manufacturing process. A computational infrastructure based on Bluetooth low energy technology with state estimators for filtering is used to localize employees in the shop floor. The researchers' motivation is two-folds: implement an indoor tracking system while promoting manage production time. In this paper, we discuss the first prototype of a localization system adapted to address these goals. Experimental results show that the system for our case study, achieves a localization accuracy of less than three meters. © 2022 IEEE.

Abstract
Purpose The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance. Design/methodology/approach In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements. Findings The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model. Originality/value The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.

Abstract
This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.

Abstract
Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly nonlinear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the predefined model controller.

Abstract
Modeling is a key step in the design of energy and control systems. It allows us to simulate and predict the behavior of electronics converters, even before constructing them. This is instrumental, for example, for sizing, component selection and preliminary validation of the converter's functionality. It also enables us to design model-based controllers for the converter and regulate the amount of transferred power, which can be done using simulation tools. This article introduces the main tools employed in the mathematical modeling of power converters, with a particular focus on linear approximations and average models. © 2023 Elsevier Inc. All rights reserved.

Abstract
The goal of this article is to introduce the fundamental notions and concepts of stability analysis for linear and nonlinear systems in the context of electronic power conversion. Power electronic circuits have strong nonlinear behavior in their essence; often we need to linearize them to understand their properties and study their stability with the applied control laws. We present different concepts of stability (internal, input-output, Lyapunov-based), observability and controllability, as well as practical tests to check these properties. We then apply these tests in the context of a single power converter example, a DC/DC boost converter. © 2023 Elsevier Inc. All rights reserved.