Amazon cover image
Image from Amazon.com

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

Contributor(s): Material type: TextTextSeries: Mechanical engineering theory and applications | Engineering tools, techniques and tablesPublisher: Hauppauge, N.Y. : Nova Science Publishers, [2011]Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781620816073
  • 1620816075
Subject(s): Genre/Form: Additional physical formats: Print version:: Industrial control systemsDDC classification:
  • 629.8 22
LOC classification:
  • TJ217
Online resources:
Contents:
INDUSTRIAL CONTROL SYSTEMS ; INDUSTRIAL CONTROL SYSTEMS ; CONTENTS ; PREFACE ; 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 ; DYNAMIC MATRIX CONTROL WITH INTERNAL MODEL BASED ON ANN OF A CONTINUOUS EXTRACTIVE PROCESS FOR BIOETHANOL PRODUCTION ; ABSTRACT ; NOMENCLATURE ; 1. INTRODUCTION ; 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 Selection3.3. 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 ; CONCLUSION ; REFERENCES ; NONDESTRUCTIVE DYNAMIC MONITORING OF ACCELERATED ION BEAMS; ABSTRACT ; 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 ; CONCLUSION ; REFERENCES ; ADAPTIVE JACOBIAN TRAJECTORY TRACKING FOR SERIAL ROBOT MANIPULATOR PASSING THROUGH SINGULARITIES ; ABSTRACT ; 1. INTRODUCTION ; 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 ; REFERENCES.
INTELLIGENT CONTROL SYSTEM FOR AN INDUSTRIAL MANIPULATORAbstract; 1Introduction; 2AdaptiveLearningTechniqueforLarge-ScaleTeachingSignals; 2.1Background; 2.2Model-basedroboticservosystem; 2.2.1Computedtorquecontrolmethod; 2.2.2TeachingsignalforRNN; 2.3Independentrecurrentneuralnetworksforanindustrialrobotwithsixjoints; 2.3.1AdaptivelearningofRNNs; 2.3.2LearningresultsofRNNs; 2.4AdvancedservosystemusingintegratedRNNs; 3FineGainTuningforModel-BasedRoboticServoControllersUsingGeneticAlgorithms; 3.1Background; 3.2RoboticServoController; 3.2.1ResolvedAccelerationControl.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Technology Available
Total holds: 0

Includes bibliographical references and index.

Description based on print version record.

English.

INDUSTRIAL CONTROL SYSTEMS ; INDUSTRIAL CONTROL SYSTEMS ; CONTENTS ; PREFACE ; 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 ; DYNAMIC MATRIX CONTROL WITH INTERNAL MODEL BASED ON ANN OF A CONTINUOUS EXTRACTIVE PROCESS FOR BIOETHANOL PRODUCTION ; ABSTRACT ; NOMENCLATURE ; 1. INTRODUCTION ; 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 Selection3.3. 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 ; CONCLUSION ; REFERENCES ; NONDESTRUCTIVE DYNAMIC MONITORING OF ACCELERATED ION BEAMS; ABSTRACT ; 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 ; CONCLUSION ; REFERENCES ; ADAPTIVE JACOBIAN TRAJECTORY TRACKING FOR SERIAL ROBOT MANIPULATOR PASSING THROUGH SINGULARITIES ; ABSTRACT ; 1. INTRODUCTION ; 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 ; REFERENCES.

INTELLIGENT CONTROL SYSTEM FOR AN INDUSTRIAL MANIPULATORAbstract; 1Introduction; 2AdaptiveLearningTechniqueforLarge-ScaleTeachingSignals; 2.1Background; 2.2Model-basedroboticservosystem; 2.2.1Computedtorquecontrolmethod; 2.2.2TeachingsignalforRNN; 2.3Independentrecurrentneuralnetworksforanindustrialrobotwithsixjoints; 2.3.1AdaptivelearningofRNNs; 2.3.2LearningresultsofRNNs; 2.4AdvancedservosystemusingintegratedRNNs; 3FineGainTuningforModel-BasedRoboticServoControllersUsingGeneticAlgorithms; 3.1Background; 3.2RoboticServoController; 3.2.1ResolvedAccelerationControl.

Master record variable field(s) change: 072 - OCLC control number change

Powered by Koha