This paper investigates discretization methods of the continuous-time state-space model of an induction motor in order to design a real-time emulator (RTE). RTE allows rapid system prototyping and reduces cost of tests related to an experimental platform. RTE performances depend strongly on the choice of discretization method and the sampling period and on the setting of the data format. Shift operator, Tustin and delta operator approximations are investigated in terms of accuracy and stability. The best compromise between approximation method, sampling period value and fixed-point data arithmetic is discussed. An FPGA-based architecture of the induction machine is designed optimizing consumed FPGA resources. Simulation results carried out using Model Sim are demonstrate the superiority of the digital induction motor model based on delta operator.

Three-phase AC motor drives play a key role in several applications, including energy conversion and automotive. Mainly, three-phase AC motor drives operate as closed loop control systems, where accurate feedback measurement sent by the current sensors is crucial to guarantee the good operation of the system. However, current sensors are potentially subject to several malfunctions that significantly affect the performance of the drive system. Accordingly, this paper proposes an efficient method for current sensor fault detection, and identification in three-phase AC motor drive system using a 2D Convolutional Neural Network (CNN). The proposed approach does need any additional extra-hardware components, since it uses only the signals already sent by the motor drive closed loop control. Indeed, it utilizes the 2D trajectory graph of the normalized motor current vector as input to a novel CNN Autoencoder model, which is introduced for feature extraction and classification. The efficiency and generalization capabilities of the proposed CNN autoencoder (PCAE) are benchmarked against a standard CNN model and conventional CNN autoencoders. The lightweight architecture of the PCAE enables its real-time implementation on a Raspberry pi 4 with a 750w experimental setup induction motor. The experimental results highlight that the proposed PCAE model can effectively detect and classify ten types of current sensor faults, in addition to distinguishing the healthy operation case. Moreover, the proposed approach achieves superior accuracy (99%), compared with conventional CNN (95%) and standard CNN-Autoencoder (96%) models.

This paper deals with the freeRTOS-based permanent magnets synchronous motor (PMSM) control algorithm developed by ST microelectronics and the optimal diagnostic tasks implementation in a freeRTOS-based e-bike control firmware. The purpose of this work is to investigate the STM32 freeRTOS-based firmware, to develop and implement optimally the diagnostic tasks of an e-bike PMSM. The implementation of the diagnostic tasks is achieved after a detailed and insightful analysis of the STM32 PMSM control firmware and realized through the utilization of a feature provided via freeRTOS which is: task notification and deferred interrupt handling.

The design of embedded control systems is rapidly change during the last decade. Guaranteeing the safety of these systems is a very challenging topic. It is possible to identify two main factors that are involved in the control of those systems: Hardware/Software (HW-SW) codesign and dynamic reconfiguration. Furthermore, the use of an embedded operating system associated with a functional module library is required for the establishment of an efficient co-design solution. This paper aims to propose an appropriate co-design methodology for closed loop DC motor control based on Field Programmable Gate Arrays (FPGA) and low cost platform Beaglebone board supported by a Linux operating system. The dynamic reconfiguration of the control system is provided by an embedded web server implemented on Beaglebone.

The design of embedded control systems is rapidly change during the last decade. Guaranteeing the safety of these systems is a very challenging topic. It is possible to identify two main factors that are involved in the control of those systems: Hardware/Software (HW-SW) codesign and dynamic reconfiguration. Furthermore, the use of an embedded operating system associated with a functional module library is required for the establishment of an efficient co-design solution. This paper aims to propose an appropriate co-design methodology for closed loop DC motor control based on Field Programmable Gate Arrays (FPGA) and low cost platform Beaglebone board supported by a Linux operating system. The dynamic reconfiguration of the control system is provided by an embedded web server implemented on Beaglebone.

In this article, the authors present a new approach based on the hardware and software architecture using embedded Linux and field-programmable gate array for implementation of remote laboratories. It combines a set of software and hardware resources in the interest of offering a multidisciplinary low-cost open platform for engineering education. Thus, the proposed approach allows students to develop low-cost and easily programmable prototypes of electrical systems control, robotics, and other embedded devices that feature Internet connectivity, Input/output, networking, and operating systems. In the proposed work, the authors present a codesign solution with flexible hardware devices, providing characteristics of multipurpose use with many experimental devices, and fully configurable graphical user interface. The physical setup and communication principles of hardware architecture are based on two types of devices: the Beaglebone running embedded Linux operating system and the field-programmable logic gate array. The graphical user interface is designed as a web page based on HTML and PHP programming languages; this allows the teachers/students to control the system easily, parameterize, and observe the behavior of the controller/system remotely.