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Design paradigms of intelligent control systems on a chip

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arxiv 1811.08426 v1 pith:B3K2GP7C submitted 2018-11-20 cs.OH cs.AR

classification cs.OHcs.AR
keywords fpgadesignchipdiscussedfuzzylogicalgorithmauthors
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper focuses on the Field Programmable Gate Array (FPGA) design and implementation of intelligent control system applications on a chip, specifically fuzzy logic and genetic algorithm processing units. Initially, an overview of the FPGA technology is presented, followed by design methodologies, development tools and the use of hardware description languages (HDL). Two FPGA design examples with the use of Hardware Description Languages (HDLs) of parameterized fuzzy logic controller cores are discussed. Thereinafter, a System-on-a-Chip (SoC) designed by the authors in previous work and realized on FPGA featuring a Digital Fuzzy Logic Controller (DFLC) and a soft processor core for the path tracking problem of mobile robots is discussed. Finally a Genetic Algorithm implementation (previously published by the authors) in FPGA chip for the Traveling Salesman Problem (TSP) is also discussed.

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Cited by 1 Pith paper

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  1. Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization

    cs.LG 2025-06 reject novelty 5.0 of 10

    A plug-and-play mechanism that mixes genetic-algorithm evolution into RL training for neural routing solvers gives small benchmark gains, but its stability theorem is not valid as proven.

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