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 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 -- 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 MANIPULATOR -- Abstract -- 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 -- 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 -- MODELING AND CONTROL OF INDUSTRIAL SYSTEMS USING GLOBAL AUTOMATA -- ABSTRACT -- 1. INTRODUCTION -- 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 -- CONCLUSIONS -- REFERENCES -- INDEX.