Industrial control systems / Robert C. Gilbert and Angela M. Schultz, editors.

Format:

Book

Contributor:

Gilbert, Robert C.

Schultz, Angela M.

Series:

Mechanical engineering theory and applications.

Engineering tools, techniques and tables.

Mechanical engineering theory and applications

Engineering tools, techniques and tables

Language:

English

Subjects (All):

Adaptive control systems.

Physical Description:

1 online resource (263 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2011.

Language Note:

English

Summary:

In this book, the authors present current research in industrial control systems. Topics discussed include the development of friction identification, modelling, and compensation methods for feed drive motions of CNC machine tools; MPC (Model Predictive Control) control algorithms for industrial applications; non-destructive dynamic monitoring of accelerated ion beams; and, Jacobian trajectory tracking for serial robot manipulators and intelligent control systems for an industrial manipulator.

Contents:

Intro

INDUSTRIAL CONTROL SYSTEMS

CONTENTS

PREFACE

Chapter 1 DEVELOPMENT OF FRICTION IDENTIFICATION, MODELING, AND COMPENSATION METHODS FOR FEED DRIVE MOTIONS OF CNC MACHINE TOOLS

ABSTRACT

1. INTRODUCTION

2. DESCRIPTIONS OF EXPERIMENT DESIGN AND MEASUREMENT

2.1. Experimental Setup

2.2. Measurement and Control

2.3. Experiments

2.3.1. Breakaway Experiment

2.3.2. Constant-Velocity Experiment

3. FRICTION IDENTIFICATION TECHNIQUES

3.1. Estimation of Position-Dependent Friction

3.2. Estimation of Velocity-Dependent Friction

3.3. Integration of Obtained Position-Dependent and Velocity-Dependent Frictions

4. DESIGN OF VELOCITY-BASED FRICTION COMPENSATOR

5. EXPERIMENTAL RESULTS OF SINUSOIDAL MOTION TESTS

6. EXPERIMENTAL RESULTS OF CIRCULAR MOTION TESTS

CONCLUSION

REFERENCES

Chapter 2 DYNAMIC MATRIX CONTROL WITH INTERNAL MODEL BASED ON ANN OF A CONTINUOUS EXTRACTIVE PROCESS FOR BIOETHANOL PRODUCTION

NOMENCLATURE

1.1. Dynamic Matrix Control (DMC)

2. EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION

3. PLANT MODEL BASED ON ANN

3.1. ANN Configurations

3.2. ANN Model Selection

3.3. Validation Parity Plot

4. USE OF VARIABLE CONTRIBUTIONS TO THE ANN OUTPUT TO IDENTIFY THE IMPORTANCE OF VARIABLES

5. USE OF RANDOM STEP DISTURBANCES TO SELECT THE MANIPULATED AND CONTROLLED VARIABLES

6. NETWORK TRAINING

7. NONLINEAR PREDICTIVE CONTROL

Chapter 3 NONDESTRUCTIVE DYNAMIC MONITORING OF ACCELERATED ION BEAMS

INTRODUCTION

1. DYNAMIC NONDESTRUCTUVE BEAM DIAGNOSTICS FOR INDUSTRIAL CYCLOTRON

1.1. Simulation

1.2. Dynamic Beam Diagnostic System

1.2.1. Transparent Profilometers

1.2.2. Charge-Frequency Converters

1.2.3. Measurements.

2. DYNAMIC NONDESTRUCTUVE BEAM DIAGNOSTICS FOR CIRCULATING BEAM OF RESEARCH ACCELERATOR

2.1. Detector Design

2.2. Readout Electronics and Measurements

Chapter 4 ADAPTIVE JACOBIAN TRAJECTORY TRACKING FOR SERIAL ROBOT MANIPULATOR PASSING THROUGH SINGULARITIES

2. KINEMATICSJACOBIAN OF SERIAL ROBOT MANIPULATORS

3. ARTIFICIAL NEURAL NETWORKS (ANNS)

4. COLLECTING TRAINING DATA

5. NETWORK'S IMPLEMENTATION

5.1. Training Stage

5.2. Testing Stage

CONCLUSIONS

Chapter5INTELLIGENTCONTROLSYSTEMFORANINDUSTRIALMANIPULATOR

Abstract

1Introduction

2AdaptiveLearningTechniqueforLarge-ScaleTeachingSignals

2.1Background

2.2Model-basedroboticservosystem

2.2.1Computedtorquecontrolmethod

2.2.2TeachingsignalforRNN

2.3Independentrecurrentneuralnetworksforanindustrialrobotwithsixjoints

2.3.1AdaptivelearningofRNNs

2.3.2LearningresultsofRNNs

2.3.3Discussion

2.4AdvancedservosystemusingintegratedRNNs

3FineGainTuningforModel-BasedRoboticServoControllersUsingGeneticAlgorithms

3.1Background

3.2RoboticServoController

3.2.1ResolvedAccelerationControl

3.2.2BasicGainTuningConsideringCriticallyDampedCondition

3.2.3DesiredTrajectory

3.3Finegaintuningbyusinggeneticalgorithms

3.4TuningResults

3.4.1RoboticDynamicswithoutFrictionTorqueTerm

(1)Incaseofresolvedaccelerationcontrollaw

(2)Incaseofcomputedtorquecontrollaw

3.4.2RoboticDynamicswithFrictionTorqueTerm

(1)Incaseoftheresolvedaccelerationcontrollaw

(2)Incaseofthecomputedtorquecontrollaw

4Conclusions

References

Chapter 6 MODELING AND CONTROL OF INDUSTRIAL SYSTEMS USING GLOBAL AUTOMATA

1.1. Modeling Industrial Systems - A Literature Review

1.2. Automatic PLC Code Generation - A Literature Review.

2. AUTOMATA THEORY

2.1. Finite Automata

Definition 2.1. Finite Automata

Definition 2.2. Run - Computation

2.2. Hybrid Automata

Definition 2.3. Hybrid Automata

2.3. Timed Automata

Definition 2.4. Timed Automata

2.4. PLC Automata

Definition 2.5. PLC Automata

2.5. Software for Modeling Discrete Event Systems

2.5.1. JGrafchart

2.5.2. UPPAAL

2.5.3. Diagen

2.5.4. Kronos

2.5.5. Hytech

2.5.6. Moby/PLC

2.5.7. Shift

2.5.8. Supremica

3. GLOBAL AUTOMATA

3.1. Global Automata Definition

Definition 3.1. Global Automata: A Global Automaton is Defined by the Tuple

3.2. Global Automata Structural Properties.

Definition 3.2. A Global Automaton GA is Linear if

Definition 3.3. A Global Automaton GA is Time Invariant if

Definition 3.4.

Definition 3.5.

Definition 3.6.

Definition 3.7.

Definition 3.8.

Definition 3.9.

Definition 3.10.

3.3. Comparison with Existing Methods

4. MODELING TOOLS ON GLOBAL AUTOMATA

4.1. State Aggregation

4.1.1. The "3-Machines Stop" Problem

4.2. Automata Composition

Definition 4.1. Global Automata Composition:

4.2.1. A Three Tank System

4.3. Hierarchy on Global Automata

5. IMPLEMENTATION TOOLS

5.1. Guide for Building Simulation Models Based on Global Automata

5.1.1. Guide for Code Generation

5.1.2. Examples of Building Simulation Models by Using The Guide

5.1.2. A House Thermostat

5.1.5. Token Passing Bus Network

5.1.6. The "3-Machines Stop" Problem

5.2. Synthesis Tool for Implementing Global Automata in PLCs

5.2.1. Guide For Programming PLCs In IL, LAD and FBD

5.2.2. Guide for Programming PLCs in Structured Text

5.2.3. Guide for Programming PLCs in Sequential Function Chart

5.2.4. In Case Of Multiple Automata Model.

5.3. Applications of Guide Use in Real Problems.

6. EXAMPLES OF USING GLOBAL AUTOMATA

6.1. Reciprocating Internal Combustion Engine

6.2. Concrete Batching and Mixing Plant

6.2.1. Modeling and Control Using Global Automata

6.2.2. Supervisory Control and Data Acquisition Station

6.3. A Three Tank System

INDEX

Blank Page.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-62081-607-5

OCLC:

793207157