scholarly journals PID Controller with Fuzzy Inference System as Simple Feedback for Closed Loop Control of Intravenous Anesthesia

2019 ◽  
Vol 9 (1) ◽  
pp. 3163-3168
Keyword(s):  
Fuzzy Inference System ◽  
Pid Controller ◽  
Fuzzy Inference ◽  
Fast Response ◽  
Open Loop ◽  
Inference System ◽  
Loop Control ◽  
Field Programmable ◽  
Simple Feedback ◽  
Genuine Effort

Anesthesia is an imperative activity during operation. Anesthesiologist perceives patient’s physiology and adjusts the drug rate. Sometimes, it may go overdose or under-dose which is proscribed. This process is merely in need of an expertise which in turn, may not be available every time and everywhere. But we can store this expertise with the help of fuzzy inference system and utilize it to judge the depth of anesthesia. A genuine effort has been made to design a PID controller with fuzzy inference system as simple feedback to control intravenous anesthetic drug delivery. This paper demonstratesthe design and implementation of experts’ based system with PID controller and comparison of the same with open loop target controlled infusion and fuzzy inference system without a controller. The conclusion of this study is that the PID controller with fuzzy inference system as simple feedback gives fast response with no overshoot. Ultimate prototype is implemented on Xilinx’s Spartan 3E Field Programmable Gate Array.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

A new congruency-based hysteresis modeling and compensating of a piezoactuator incorporating an adaptive neuron fuzzy inference system

2015 ◽  
Vol 231 (9) ◽  
pp. 1712-1724 ◽  
Author(s):  
Phuong-Bac Nguyen ◽  
Seung-Bok Choi ◽  
Byung-Keun Song
Keyword(s):  
Fuzzy Inference ◽  
Open Loop ◽  
Inference System ◽  
New Approach ◽  
Loop Control ◽  
First Order ◽  
Minor Loop ◽  
Hysteresis Modeling ◽  
Adaptive Neuron ◽  
Relationship Of

This paper proposes a new approach to modeling and compensating for a rate-independent hysteresis of a piezoactuator. The model—namely, congruency-based hysteresis—is developed based on two very important characteristics of the hysteresis. These are congruency and wipe-out. The proposed approach consists of two branches for cases of monotonic increase and monotonic decrease of input excitation. In order to realize this model, datasets of first-order minor-loop values should be determined in advance. This can be done using the adaptive neuron fuzzy system (ANFIS) technique and experimental data. With this technique, an input-output relationship of first-order minor-loop values is estimated effectively. In addition, the ANFIS technique is also used in constructing datasets of inverse first-order minor-loop values, which are essential parts of a congruency-based hysteresis compensator. Several experiments in modeling and open-loop control are conducted to show the effectiveness of the proposed approach. In addition, a comparative work between the proposed approach and one of previous works is undertaken to demonstrate the benefit of the proposed method.

scholarly journals Robotics Vision-based Heuristic Reasoning for Underwater Target Tracking and Navigation

10.5772/5782 ◽  
2005 ◽  
Vol 2 (3) ◽  
pp. 25 ◽  
Author(s):  
Chua Kia ◽  
Mohd Rizal Arshad
Keyword(s):  
Target Tracking ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vision System ◽  
Open Loop ◽  
Underwater Robotics ◽  
Perspective View ◽  
Inference System ◽  
Heuristic Reasoning ◽  
Underwater Target

This paper presents a robotics vision-based heuristic reasoning system for underwater target tracking and navigation. This system is introduced to improve the level of automation of underwater Remote Operated Vehicles (ROVs) operations. A prototype which combines computer vision with an underwater robotics system is successfully designed and developed to perform target tracking and intelligent navigation. This study focuses on developing image processing algorithms and fuzzy inference system for the analysis of the terrain. The vision system developed is capable of interpreting underwater scene by extracting subjective uncertainties of the object of interest. Subjective uncertainties are further processed as multiple inputs of a fuzzy inference system that is capable of making crisp decisions concerning where to navigate. The important part of the image analysis is morphological filtering. The applications focus on binary images with the extension of gray-level concepts. An open-loop fuzzy control system is developed for classifying the traverse of terrain. The great achievement is the system's capability to recognize and perform target tracking of the object of interest (pipeline) in perspective view based on perceived condition. The effectiveness of this approach is demonstrated by computer and prototype simulations. This work is originated from the desire to develop robotics vision system with the ability to mimic the human expert's judgement and reasoning when maneuvering ROV in the traverse of the underwater terrain.

scholarly journals Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 19 ◽  
pp. 53-61
Author(s):  
Aristide Timene ◽  
Ndjiya Ngasop ◽  
Haman Djalo
Keyword(s):  
Fuzzy Inference System ◽  
Pid Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Proportional Integral Derivative ◽  
Hydraulic Actuator ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Depth Control ◽  
Tillage Operations

This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software. The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller. The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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