This paper reviews the state of the art of Field Programmable Gate Array (FPGA) design methodologies with a focus on Industrial Control System applications. It begins 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. These 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: 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. The paper discusses FPGA generic architecture, hardware description languages, and their development tools. It also presents integrated system modeling and design, emphasizing the benefits of holistic modeling and the use of HDLs. The paper outlines FPGA-based controller design rules, including algorithm refinement, design methodology based on reuse modules, and the Algorithm Architecture “Adequation” methodology. The paper also discusses the contributions and limits of FPGAs used in electrical system controllers, highlighting the domains of use, benefits of using FPGAs for control of electrical systems, and dynamic reconfiguration of FPGAs. It concludes with a discussion on FPGAs in intelligent and complex control systems, including neural networks implemented in FPGA and fuzzy logic based control systems.This paper reviews the state of the art of Field Programmable Gate Array (FPGA) design methodologies with a focus on Industrial Control System applications. It begins 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. These 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: 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. The paper discusses FPGA generic architecture, hardware description languages, and their development tools. It also presents integrated system modeling and design, emphasizing the benefits of holistic modeling and the use of HDLs. The paper outlines FPGA-based controller design rules, including algorithm refinement, design methodology based on reuse modules, and the Algorithm Architecture “Adequation” methodology. The paper also discusses the contributions and limits of FPGAs used in electrical system controllers, highlighting the domains of use, benefits of using FPGAs for control of electrical systems, and dynamic reconfiguration of FPGAs. It concludes with a discussion on FPGAs in intelligent and complex control systems, including neural networks implemented in FPGA and fuzzy logic based control systems.