FPGAs in Industrial Control Applications

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

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto-Magnetic EAS Devices

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The paper discusses fault diagnosis of the electronic circuit board, part of acousto-magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.Peer reviewe

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic

New control structure for high voltage fields

In electrostatic painting, a high voltage is applied to the paint, attracting the paint particles toward a grounded work object, drastically increasing the efficiency of the painting process. However, combining high voltage and highly flammable paint is a potential hazard that is reduced by an automatic fire extinguishing system and strict controller limits defined by safety standards. The thesis investigates alternative controller strategies attempting to improve the performance of ABB's high-voltage control system. The dynamics of electrostatic gas discharge, or corona discharge, is studied to estimate the relation between the applied voltage and the corona current passing through the electrostatic field. However, given the data available for the real-time system, the estimation problem is concluded to be structurally unidentifiable, resulting in the estimators not converging to the actual state of the system. Despite that, a simple estimator is utilized in a current limiting controller. This controller is activated when the system leaves its normal working area. Simulation results indicate that this controller can decrease the amount of unnecessary safety-related stops and reduce the reaction time for actual safety-hazard incidents. Furthermore, a data-driven approach is selected to model and create a controller for the system generating the high-voltage output. The model of the dynamics of the high-voltage system is created using neural networks and open-loop high-resolution data collected with a self-developed data acquisition program. Then, the estimated model is used in a reinforcement learning environment to create a theoretically optimal controller valid for the entire nonlinear workspace. Due to limited computational resources, and errors in the data, the thesis presents a lower-resolution proof of concept for both the neural network model and controller. Additionally, the thesis presents a basis of knowledge on ABB's electrostatic painting system, featuring recommendations and suggestions for future work.In electrostatic painting, a high voltage is applied to the paint, attracting the paint particles toward a grounded work object, drastically increasing the efficiency of the painting process. However, combining high voltage and highly flammable paint is a potential hazard that is reduced by an automatic fire extinguishing system and strict controller limits defined by safety standards. The thesis investigates alternative controller strategies attempting to improve the performance of ABB's high-voltage control system. The dynamics of electrostatic gas discharge, or corona discharge, is studied to estimate the relation between the applied voltage and the corona current passing through the electrostatic field. However, given the data available for the real-time system, the estimation problem is concluded to be structurally unidentifiable, resulting in the estimators not converging to the actual state of the system. Despite that, a simple estimator is utilized in a current limiting controller. This controller is activated when the system leaves its normal working area. Simulation results indicate that this controller can decrease the amount of unnecessary safety-related stops and reduce the reaction time for actual safety-hazard incidents. Furthermore, a data-driven approach is selected to model and create a controller for the system generating the high-voltage output. The model of the dynamics of the high-voltage system is created using neural networks and open-loop high-resolution data collected with a self-developed data acquisition program. Then, the estimated model is used in a reinforcement learning environment to create a theoretically optimal controller valid for the entire nonlinear workspace. Due to limited computational resources, and errors in the data, the thesis presents a lower-resolution proof of concept for both the neural network model and controller. Additionally, the thesis presents a basis of knowledge on ABB's electrostatic painting system, featuring recommendations and suggestions for future work