FPGA Implementation of Adaptive Neuro-Fuzzy Inference Systems Controller for Greenhouse Climate

Lachouri, Charaf eddine; Mansouri, Khaled; Belmeguenaï, Aïssa; Lafifi, Mohamed Mourad

doi:10.14569/ijacsa.2016.070136

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…On the other hand, as the need for high-speed computing intensifies, FISs have migrated to VLSI, leading to a substantial improvement in their processing speed [16]. Nonetheless, the considerable expenses associated with VLSI chip redesign or modification have necessitated the adoption of field-programmable gate arrays (FPGAs) [17,18]. Therefore, FPGAs have emerged as a favorable choice for implementing FISs owing to their cost-effectiveness and flexibility in hardware design modifications.…”

Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

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Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.

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Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

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Deep MLP for systems modeling

Salah

Fourati²

2017

2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

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Intelligent Climate Control System inside a Greenhouse

Labidi¹,

Chouchaine²,

Mami³

2021

IJACSA

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An agricultural greenhouse is an environment to ensure intensive agricultural production. The favorable climatological conditions (temperature, lighting, humidity ...) for agricultural production must be reproduced in a non-natural way by controlling these parameters using several actuators (heating/air conditioning, ventilation, and humidifier/ dehumidifier). The objective of this study is to control the humidity inside the greenhouse; it is a problem that remains to be negotiated. To that end, an actuator based on a humidifier and a dehumidifier was installed in an experimental greenhouse and activated by a fuzzy logic controller to achieve the desired optimal indoor humidity in the greenhouse.

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FPGA Implementation of Adaptive Neuro-Fuzzy Inference Systems Controller for Greenhouse Climate

Abstract: Abstract-This paper describes a Field-programmable Gate

Cited by 4 publications

References 16 publications

A Hardware Realization Framework for Fuzzy Inference System Optimization

A Hardware Realization Framework for Fuzzy Inference System Optimization

Deep MLP for systems modeling

Intelligent Climate Control System inside a Greenhouse

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