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The study proposes a parameter optimisation of the high-frequency (HF) signal injection sensorless algorithm applied to an interior permanent magnet synchronous machine (IPMSM). The latter is intended for electric vehicle (EV) traction applications with dedicated mission profile under specific constraints. This optimisation aims to minimise two conflicting objectives: (i) minimising the position estimation error which impacts the torque accuracy and (ii) minimising torque ripples responsible for additional stress on the motor shaft, causing unwanted noise in the vehicle powertrain. The main contribution of this study is to provide guidelines for the selection of the HF signal injection tuning parameters so as to satisfy these objectives. To do so, a genetic algorithm has been implemented and the optimisation process is based on a reliable simulation model. Using a simulation model is deliberately chosen in order to reflect the behaviour of a motor series. Besides, in order to integrate the specificities of the target application, the optimisation process is based on a typical EV mission profile. A set of non-dominated solutions satisfying the tradeoff between the position error and the torque ripples is obtained, discussed and finally tested with success on an experimental setup.

This paper studies the suitability of System-on-Chip (SoC) devices to perform Embedded Real-Time Simulators (eRTS) of electrical systems. These new devices combine powerful ARM processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for controlling, monitoring and emulating a complete electrical system. There are two main categories of elements with their own respective characteristics and constraints: electromechanical/electromagnetic systems, and switching elements. Accordingly, the proposed investigation will focus on a Doubly-Fed Induction Generator (DFIG) on one hand, and on a Modular Multi-level Converter (MMC) on the other hand. In addition to Real-Time (RT) simulation results, SoC time/resources evaluations will be presented for different implementation strategies: full-software, full-hardware based on High-Level Synthesis (HLS) tools, and using 64/32-bit floating- and fixed-point formats. The validity of the MMC implementation will be tested experimentally.

Model-predictive control (MPC) is an advanced model-based control technique. It allows flexible control schemes with fast time responses. However, like most optimization-based control strategies, MPCs are usually computationally intensive and suffer from a lack of robustness toward parametric variations. The computational burden of these controllers is nowadays being relieved thanks to the progress of digital devices; however, the robustness is still a major issue that prevents the use of MPC controllers in some applications. Systems presenting slow and fast dynamics at the same time are usually controlled by cascaded control loops. MPC techniques are restricted to the control of inner loops, while outer loops use more robust control techniques. The aim of this paper is to provide MPC techniques for both the inner and outer loops of an active front-end rectifier. A first dual-MPC cascaded control shows the drawbacks of usual MPC techniques for outer loops. It is thereafter modified to achieve a robust cascaded dual-MPC that eliminates the variable resistive load parameter from the system dynamical model and formulates a new prediction model containing only known parameters and measurable quantities. Finally, a third method based on the previous one is also presented in order to reduce switching losses. Experimental results for the three methods are presented and compared with double precision offline software simulation results to validate the feasibility of the proposed techniques.

Comunicació presentada a IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society (October 21-23, 2018 Washington D.C., USA.) System-on-Chip (SoC) devices combine powerful general purpose processors, a Field-Programmable Gate Array (FPGA) and other peripherals which make them very convenient for Hardware-in-the-Loop (HIL) simulation. One of the limitations of these devices is that control engineers are not particularly familiarized with FPGA programming, which need extensive expertise in order to code these highly sophisticated algorithms using Hardware Description Languages (HDL). Notwithstanding, there exist High-Level Synthesis (HLS) tools which allow to program these devices using more generic programming languages such as C, C++ and SystemC. This paper evaluates SoC devices to implement a Modular Multi-Level Converter (MMC) model using HLS tools for being implemented in the FPGA fabric in order to perform HIL verification of control algorithms in a single low-cost device.

Grid connected power converters are widely used to interface between grid – on one side- and loads or renewable energy sources – on the other side. This work presents an indirect sliding mode power control (ISMPC) for three phase grid connected power converter. This control is characterised by improved robustness with regard to external disturbances and parameters changes, while insuring at the same time low current total harmonic distortion factor. The development and implementation of the ISMPC algorithm on low cost Microsemi SmartFusion digital solution is also described. This digital solution includes in the same chip a Field Programmable Gate Array (FPGA) fabric and a Cortex-M3 processor associated to large number of peripheral cores. The developed ISMPC is compared with standard and commonly used controls for grid connected converters to show its interest and advantages. Moreover, an overview of the experimental set-up, the description of the algorithm modular partitioning between FPGA and processor, the resources optimisation as well as the execution time reduction are presented. Selected experimental results corresponding to different operation steps are presented to illustrate performances and effectiveness of the proposed ISMPC algorithm.

Model predictive control (MPC) is a model-based control strategy that introduces many advantages but also some drawbacks. The biggest challenge that faces MPC controller is its sensitivity towards parametric variation. This study aims to robustify MPCs by adding a least mean square (LMS) identification algorithm within their structure. The feasibility of this method is tested on a simple example which is the MPC current control of a permanent magnet synchronous motor driven by a two-level power converter. Many tests are firstly performed without including inside the MPC algorithms the LMS online tuning procedure. These tests are later compared with the ones obtained with the same control technique but where the identification algorithm is turned on. Results clearly show all the benefits of using this approach on the controller performances.

The aim of this paper is to quantify the interest of using hardware field-programmable gate arrays (FPGAs) to implement complex control algorithms. As a benchmark, authors have chosen a sensorless speed controller for a synchronous motor. The estimation of the rotor position and speed is achieved using an extended Kalman filter (EKF), eliminating the need of their corresponding mechanical sensors. Due to the EKF complexity, such sensorless controller is systematically implemented in a software digital signal processor (DSP) device. The execution time is frequently evaluated to several tens or hundreds of microseconds. The motivation here is to prove that, when exploiting the treatment fastness of FPGAs (less than 6 μs ), it is possible to enhance the control bandwidth. To reach this objective, a comparison between the developed FPGA-based sensorless speed controller and its DSP-based counterpart is made. The same sensorless controller (with the same complexity) has been implemented in both cases. To prop up this comparison, simulation, hardware-in-the-loop, and experimental tests are presented.

A general algorithm of a Space Vector approach is implemented on a 6-leg VSI controlling a PM synchronous machine with three independent phases. In this last case, the necessity of controlling the zero-sequence current motivates the choice of a special family of vectors, different of this one used in Pulse Width Modulation (PWM) intersective strategy and in common Space Vector PWM (SVPWM). To preserve the parallelism of the algorithm and fulfill the execution time constraints, the implementation is made on a Field Programmable Gate Array (FPGA). Comparisons with more classical 2-level and 3-level PWM are provided. Fui8 within the SOFRACI project

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Sensorless control of synchronous machine (SM) is a well-investigated research topic. However, avionics manufacturers still have restrictions about its applications due to the complexity of the on-board systems. This study presents a study investigating the use of an extended Kalman filter (EKF) to estimate the position and speed of a brushless exciter synchronous starter/generator. In spite of the machine complexity and the critical choice of the EKF covariance matrices, the EKF observer presents satisfying simulation results in estimating the position and the velocity of the mentioned motor during the start-up. Experimental results performed on an SM with an EKF implemented on a field programmable gate array are also shown to prove the efficiency of this type of implementation.

The aim of this paper is to present a fully integrated solution for synchronous motor control. The implemented controller is based on Actel Fusion field-programmable gate array (FPGA). The objective of this paper is to evaluate the ability of the proposed fully integrated solution to ensure all the required performances in such applications, particularly in terms of control quality and time/area performances. To this purpose, a current control algorithm of a permanent-magnet synchronous machine has been implemented. This machine is associated with a resolver position sensor. In addition to the current control closed loop, all the necessary motor control tasks are implemented in the same device. The analog-to-digital conversion is ensured by the integrated analog-to-digital converter (ADC), avoiding the use of external converters. The resolver processing unit, which computes the rotor position and speed from the resolver signals, is implemented in the FPGA matrix, avoiding the use of external resolver-to-digital converter (RDC). The sine patterns used for the Park transformation are stored in the integrated flash memory blocks.