Low-power VLSI Design of Fuzzy Logic Based Automatic Controller for Total Artificial Heart. Bashir I. Morshed Department of Electronics



Similar documents
A FUZZY MATHEMATICAL MODEL FOR PEFORMANCE TESTING IN CLOUD COMPUTING USING USER DEFINED PARAMETERS

Project Management Efficiency A Fuzzy Logic Approach

JAVA FUZZY LOGIC TOOLBOX FOR INDUSTRIAL PROCESS CONTROL

FUZZY Based PID Controller for Speed Control of D.C. Motor Using LabVIEW

Efficient Interconnect Design with Novel Repeater Insertion for Low Power Applications

A Fuzzy System Approach of Feed Rate Determination for CNC Milling

Introduction to Fuzzy Control

Soft Computing in Economics and Finance

Knowledge Base and Inference Motor for an Automated Management System for developing Expert Systems and Fuzzy Classifiers

FPGA Implementation of an Advanced Traffic Light Controller using Verilog HDL

IMPLEMENTATION OF FUZZY EXPERT COOLING SYSTEM FOR CORE2DUO MICROPROCESSORS AND MAINBOARDS. Computer Education, Konya, 42075, Turkey

Computational Intelligence Introduction

High Frequency Trading using Fuzzy Momentum Analysis

Immersive Audio Rendering Algorithms

EMPLOYEE PERFORMANCE APPRAISAL SYSTEM USING FUZZY LOGIC

Conception and Development of a Health Care Risk Management System

Fuzzy Logic Based Reactivity Control in Nuclear Power Plants

Problems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment,

Applications of Fuzzy Logic in Control Design

Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR

Performance Comparison of an Algorithmic Current- Mode ADC Implemented using Different Current Comparators

Artificial Intelligence: Fuzzy Logic Explained

Improved incremental conductance method for maximum power point tracking using cuk converter

NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling

Fuzzy Candlestick Approach to Trade S&P CNX NIFTY 50 Index using Engulfing Patterns

ABSTRACT. Keyword double rotary inverted pendulum, fuzzy logic controller, nonlinear system, LQR, MATLAB software 1 PREFACE

A Fuzzy Controller for Blood Glucose-Insulin System

Electrical and Computer Engineering (ECE)

Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System

stable response to load disturbances, e.g., an exothermic reaction.

Room Temperature based Fan Speed Control System using Pulse Width Modulation Technique

DESIGN AND STRUCTURE OF FUZZY LOGIC USING ADAPTIVE ONLINE LEARNING SYSTEMS

ELEC 5260/6260/6266 Embedded Computing Systems

Keywords: Learning, neural networks, fuzzy systems, perceptron, neuro-fuzzy. 1 Introduction

NATIONAL SUN YAT-SEN UNIVERSITY

T1-Fuzzy vs T2-Fuzzy Stabilize Quadrotor Hover with Payload Position Disturbance

STATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC

Implementation of Fuzzy and PID Controller to Water Level System using LabView

Strictly as per the compliance and regulations of:

Self-Evaluation Configuration for Remote Data Logging Systems

EVALUATION OF FAIR MARKET PRICE OF RESOURCES IN OIL AND GAS INDUSTRY USING FUZZY SETS AND LOGICS

An Efficient AC/DC Converter with Power Factor Correction

Intelligent Control Design Using S12 Microcontroller: A Student Design Workshop

Maximum Power Tracking for Photovoltaic Power Systems

HIGH SPEED AREA EFFICIENT 1-BIT HYBRID FULL ADDER

DC/DC BUCK Converter for Renewable Energy Applications Mr.C..Rajeshkumar M.E Power Electronic and Drives,

Leran Wang and Tom Kazmierski

Fuzzy Logic Application-Specific Processor for Traffic Control in ATM Network

SIMULATION OF CLOSED LOOP CONTROLLED BRIDGELESS PFC BOOST CONVERTER

Electric Power Steering Automation for Autonomous Driving

A STUDY ON THE CONVENTIONAL AND FUZZY CONTROL STEEL-CUTTING PROCESS

A Study of Speed Control of PMDC Motor Using Auto-tuning of PID Controller through LabVIEW

DEVELOPMENT OF FUZZY LOGIC MODEL FOR LEADERSHIP COMPETENCIES ASSESSMENT CASE STUDY: KHOUZESTAN STEEL COMPANY

Intelligent Mechatronic Model Reference Theory for Robot Endeffector

Applying Soft Computing to Estimation of Resources Price in Oil and Gas Industry

Fuzzy logic decision support for long-term investing in the financial market

Fast Fuzzy Control of Warranty Claims System

Current vs. Voltage Feedback Amplifiers

An Automatic Optical Inspection System for the Diagnosis of Printed Circuits Based on Neural Networks

Real Time Traffic Balancing in Cellular Network by Multi- Criteria Handoff Algorithm Using Fuzzy Logic

A Novel Low Power Fault Tolerant Full Adder for Deep Submicron Technology

Curriculum Vitae. 01 August 1973, Gümüşhane, TURKEY. Phone : / Ext.: : kilic@erciyes.edu.tr

What is Modeling and Simulation and Software Engineering?

Computer Aided Design of Home Medical Alert System

A Fuzzy-Based Speed Control of DC Motor Using Combined Armature Voltage and Field Current

SECOND YEAR. Major Subject 3 Thesis (EE 300) 3 Thesis (EE 300) 3 TOTAL 3 TOTAL 6. MASTER OF ENGINEERING IN ELECTRICAL ENGINEERING (MEng EE) FIRST YEAR

Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification

A Trust-Evaluation Metric for Cloud applications

Product Selection in Internet Business, A Fuzzy Approach

PC BASED PID TEMPERATURE CONTROLLER

A FUZZY LOGIC APPROACH FOR SALES FORECASTING

DEVELOPMENT OF MULTI INPUT MULTI OUTPUT COUPLED PROCESS CONTROL LABORATORY TEST SETUP

Detection of Heart Diseases by Mathematical Artificial Intelligence Algorithm Using Phonocardiogram Signals

TrAgent: A Multi-Agent System for Stock Exchange

GE Healthcare. Avance Carestation. Innovating with you, shaping exceptional care

Teaching Concepts in Fuzzy Logic Using Low Cost Robots, PDAs, and Custom Software

Review of Mobile Applications Testing with Automated Techniques

Soft Computing In The Forecasting Of The Stock Exchange Of Thailand

ISIO 200. Binary Input/Output (I/O) Terminal with IEC Interface

J.Instrum.Soc.India 30(1)29-34 PROGRAMMABLE CONTROL OF TEMPERATURE: A SIMPLE AND VERSATILE METHOD. N. Asha Bhat and K. S. Sangunni.

Numerical Research on Distributed Genetic Algorithm with Redundant

Low-Power Error Correction for Mobile Storage

Type-2 fuzzy logic control for a mobile robot tracking a moving target

Fuzzy Logic Based Revised Defect Rating for Software Lifecycle Performance. Prediction Using GMR

Dr. Yeffry Handoko Putra, S.T., M.T

Simulation of VSI-Fed Variable Speed Drive Using PI-Fuzzy based SVM-DTC Technique

Detection of DDoS Attack Scheme

Design of Low Power One-Bit Hybrid-CMOS Full Adder Cells

The Use of Hybrid Regulator in Design of Control Systems

A high Speed 8 Transistor Full Adder Design using Novel 3 Transistor XOR Gates

Efficient DNS based Load Balancing for Bursty Web Application Traffic

Ph. D. thesis summary. by Aleksandra Rutkowska, M. Sc. Stock portfolio optimization in the light of Liu s credibility theory

Using a Failure Modes, Effects and Diagnostic Analysis (FMEDA) to Measure Diagnostic Coverage in Programmable Electronic Systems.

Bioinstrumentation. Kuo-Sheng Cheng, Ph.D. Department of Biomedical Engineering National Cheng Kung University

dspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor

Modeling and Simulation of a Novel Switched Reluctance Motor Drive System with Power Factor Improvement

S/5 Anesthesia Monitor. The clinician s choice

FLBVFT: A Fuzzy Load Balancing Technique for Virtualization and Fault Tolerance in Cloud

ONLINE HEALTH MONITORING SYSTEM USING ZIGBEE

LOW POWER MULTIPLEXER BASED FULL ADDER USING PASS TRANSISTOR LOGIC

Transcription:

Low-power VLSI Design of Fuzzy Logic Based Automatic Controller for Total Artificial Heart Bashir I. Morshed Department of Electronics

References 2 [1] H. Cheung-Hwa, "Fuzzy logic automatic control of the Phoenix-7 total artificial heart", Japanese Society for Artificial Organs, accepted for publication on Feb. 2004. [2] H. C. Kim, et al., "Development of a microcontroller based automatic control system for the electrohydraulic total artificial heart", IEEE Trans. Biomedical Engineering, vol. 44, no. 1, pp. 77-89, 1997. [3] M. Sasaki, et al., "Fuzzy multiple-input maximum and minimum circuits in current mode and their analyses using bounded-difference equations", IEEE Trans. Computers, vol. 39, no. 6, pp. 768-774, 1990. [4] M. Sasaki, F. Ueno, "A Fuzzy logic function generator (FLUG) implemented with current mode CMOS circuits", Intl. Symp. Multiple- Valued Logic, pp. 356-362, 1991. [5] M. Sasaki, F. Ueno, "A VLSI implementation of Fuzzy logic controller using current mode CMOS circuits", Intl. Conf. Industrial Fuzzy Control and Intelligent Systems, pp. 215-220, 1993. [6] M. Sasaki, F. Ueno, "A Novel Implementation of Fuzzy logic controller using new meet operation", IEEE Conf. Fuzzy Systems, vol. 3, pp. 1676-1681, 1994.

Contents 3 Introduction Artificial heart Controller algorithms Fuzzy logic Fuzzification and defuzzification Design challenges Preliminary results Time table Conclusion

Objective 4 To design an automatic controller to be used in totallyimplantable artificial heart. Desired properties: Low-power and high speed operation, Real-time monitoring and control, Proper operation within some supply voltage variation, Self-regulating and adaptive. Suitable techniques: Fuzzy logic based control algorithm, CML based CMOS (full-custom ASIC) technology. Design challenges: CML based special circuitry to implement Fuzzy functions, Combining CML blocks to implement Fuzzy logic blocks.

Introduction 5 Artificial hearts are being implanted to the patients with critical heart problems, so that they can survive until a heart transplant is possible. Milestones: 1952: First successful open heart surgery by F. John Lewis. 1967: Christiaan Barnard performs the first whole heart transplant. 1982: Willem DeVries first implanted a permanent artificial heart designed by Robert Jarvik. Ongoing researchers are focusing on durable and adaptive artificial hearts so that they can operate independently and reliably without any human control or monitoring.

Total Artificial Heart (TAH) 6 A TAH is an implantable device entirely replacing the human heart for a certain period of time. Must be capable of doing all functions of heart according to specific needs of human body. Two mechanical pumps (diaphragm type) replace the ventricles and are controlled by internal electronic device. Controller Simplified blocks of TAH [2]

7 Proposed Configuration [2]

Controller for TAH Automatic controller monitors and regulates: heart rate, percent systole, drive pressure, separately for both of the left and the right ventricles. Factors to be considered: Patient's physical activity, requirement of oxygen circulation, blood pressure at preload and afterload, Starling's Response, etc. 8

Some Interesting Phenomena Starling's Response: deals with the variable heart volume and rate needed for specific individual. The well trained athlete's heart rate does not increase as much nor as quickly as that of an average individual during strenuous activity. Pulmonary edema: if the right ventricle is over-driven, then fluid can actually be forced into the lungs. The amount of blood flowing from the right ventricle has to be sufficient enough to supply the required amount of blood to the left ventricle. The pressure at afterload on the right ventricle must be less compared to that of the left side. 9

Controller Algorithm 10 Various controlling algorithms and techniques: PID based, RAM/ROM lookup table based, Fuzzy logic based, etc. The advantages of Fuzzy logic based controller: Simple rule based operation makes FL very fast, Potential for real-time automatic control eliminating continuous manual monitoring under varying hemodynamic conditions, Inherent adaptability property of the logic, Highly stable nature and nonlinear control surface, No ADC/DAC.

Controller Design Design options: Micro-controller based, PLA/PAL based, Full custom ASIC. Advantage of full custom ASIC: Efficient and compact design blocks of Fuzzy rules using current mode logic (CML) [3-6], Very low power and high speed of operation, Efficient implementation of control algorithm. 11

Fuzzy Logic (FL) 12 Lotfi A. Zadeh (1973) is the founder of Fuzzy logic. It is basically a convenient way to map an input space to an output space. The basic idea is soft computing alike human logic. Rather than attempting to model a system mathematically, FL incorporates a simple rule-based approach to a solving control problem IF (X) AND/OR (Y) THEN (Z). There are mainly two different models: Mamdani Sugeno

Discrete vs Continuous Logic Continuous Logic (Fuzzy) Discrete Logic (Digital) 13

Precision vs Significance Digital Robot Fuzzy Robot 14

15 Structure of Fuzzy Logic

16 Membership Functions

17 Fuzzification and Defuzzification

18 Mamdani Model

19 Sugeno Model

Example 1 (Mamdani): Rules If then If then If then 20

21 Example 1 (Mamdani): Surface

Example 2 (Sugeno): Rules If or then If and then If or then 22

23 Example 2 (Sugeno): Surface

24 Conventional vs Fuzzy Logic

Design Challenges 25 Design/verification of the following functional blocks: Minimum/Maximum operation (current mode), FL Function generator (FLUG) with rules block, Membership function circuit, Meet operation (replacing weighted average), Bias circuit, variable gain current mirror, etc. Modifications to adopt membership functions other than Gaussian and Mamdani model. Combining the basic blocks and acceptable operation with supply voltage variation. Compatible input/output to meet requirements of TAH.

26 Preliminary Results

Preliminary Results (cont.) Delay effect 27 Minimum device size

Time Table Research Steps Begin End Literature survey 01 Feb. 28 Feb. Design of Fuzzy logic functions 28 Feb. 13 Mar. Design of Fuzzy blocks for TAH controller 14 Mar. 27 Mar. Simulation and verification of all blocks 28 Mar. 05 April Project presentation - 06 April Preparing final report 06 April 17 April Submission of final report - 18 April 28

Conclusion Project goals: The designs proposed by Sasaki will be tested on CMOS 0.18u technology. A few design blocks of the Fuzzy controller for TAH will be designed, tested and verified. Proper operation for operating voltage variation within certain degree of errors. Limitations for simulation: Modeling TAH and various parameters associated with that. The adaptability property. 29