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• Introduction B: Fundamentals of Cellular Mechanics B.1: Fluid-structure Interactions and Flagellar Actuation B.2: Mathematical Models for Swimming Bacteria B.3: Tetrahymena pyriformis in Motion
• C: Theoretical Microbiorobotics C.1: Piezoelectric Cellular Actuators with Nested Rhombus Strain Amplification
• C.2: Stochastic Models and Control of Bacterial Bioactuators and Biomicrorobots C.3: Stochastic Model and Control in Microbiorobotics D: Experimental Microbiorobotics D.1: Bacteria-Inspired Microrobots D.2: Magnetotactic Bacteria for Microrobotics D.3: Flexible magnetic microswimmers D.4: Bacteria-Powered Microrobots D.5: Control of Tetrahymena pyriformis as a Microrobot E: Perspectives and Outlook.
• (source: Nielsen Book Data)

Microbiorobotics is a new engineering discipline that inherently involves a multidisciplinary approach (mechanical engineering, cellular biology, mathematical modeling, control systems, synthetic biology, etc). Building robotics system in the micro scale is an engineering task that has resulted in many important applications, ranging from micromanufacturing techniques to cellular manipulation. However, it is also a very challenging engineering task. One of the reasons is because many engineering ideas and principles that are used in larger scales do not scale well to the micro-scale. For example, locomotion principles in a fluid do not function in the same way, and the use of rotational motors is impractical because of the difficulty of building of the required components.
(source: Nielsen Book Data)

• NOMENCLATURE ix INTRODUCTION xi
• CHAPTER 1. OBSERVER-BASED REALIZATION OF A GIVEN CONTROLLER 1 1.1. Introduction 1 1.2. Principle 3 1.3. A first illustration 9 1.4. Augmented-order controllers 12 1.5. Discussion 16 1.6. In brief 19 1.7. Reduced-order controllers case 20 1.8. Illustrations 23 1.8.1. Illustration 1: plant state monitoring 24 1.8.2. Illustration 2: controller switching 26 1.8.3. Illustration 3: smooth gain scheduling 29 1.9. Reference inputs in observer-based realizations 31 1.9.1. General results 31 1.9.2. Illustration 33 1.10. Disturbance monitoring and rejection 36 1.10.1. General results 36 1.10.2. Illustration 40 1.11. Minimal parametric description of a linear system 44 1.12. Selection of the observer-based realization 47 1.12.1. Luenberger observer dynamics assignment 47 1.12.2. State-estimator dynamics assignment 48 1.13. Conclusions 49 1.14. Bibliography 49
• CHAPTER 2. CROSS STANDARD FORM AND REVERSE ENGINEERING 53 2.1. Introduction 53 2.2. Definitions 55 2.3. Low-order controller case (nK n) 56 2.3.1. Uniqueness condition 58 2.3.2. Existence of a CSF 59 2.4. Augmented-order controller case (nK > n) 61 2.5. Illustration 61 2.5.1. Solving the inverse H -optimal control problem 61 2.5.2. Improving K0 with frequency-domain specification 64 2.5.3. Improving K0 with phase lead 66 2.6. Pseudo-cross standard form 69 2.6.1. A reference model tracking problem 69 2.6.2. Illustration 70 2.6.3. Comment 72 2.7. Conclusions 72 2.8. Bibliography 73
• CHAPTER 3. REVERSE ENGINEERING FOR MECHANICAL SYSTEMS 77 3.1. Introduction 77 3.2. Context 78 3.3. Model, specifications and initial controller 79 3.4. H design based on the acceleration sensitivity function 81 3.4.1. General results 81 3.4.2. Illustration 84 3.4.3. Analysis on an augmented model 88 3.4.4. Illustration 88 3.4.5. Synthesis on an augmented model 89 3.4.6. Illustration 91 3.4.7. Taking into account a roll-off specification 94 3.4.8. Illustration 96 3.4.9. Taking into account an integral term 98 3.4.10. Illustration 100 3.5. Mixed H2/H design based on the acceleration sensitivity function 102 3.5.1. The one degree of freedom case 103 3.5.2. First-order optimality conditions 106 3.5.3. Numerical solution using Matlab(R) 118 3.5.4. Multi-variable case 120 3.6. Aircraft lateral flight control design 121 3.6.1. Model and specifications 121 3.6.2. Basic H2/H control problem 123 3.6.3. Augmented H control problem 126 3.7. Conclusions 130 3.8. Bibliography 131 CONCLUSIONS AND PERSPECTIVES 135 APPENDICES 139
• Appendix 1. A Preliminary Methodological Example 141
• Appendix 2. Discrete-time Case 149
• Appendix 3. Nominal State-feedback for Mechanical Systems 153
• Appendix 4. Help of Matlab(R) Functions 159 LIST OF FIGURES 169 INDEX 175.
• (source: Nielsen Book Data)

Reverse Engineering in Control Design proposes practical approaches to building a standard H-infinity problem taking into account an initial controller. Such approaches allow us to mix various control objectives and to initialize procedures for a fixed-structure controller design. They are based on the Observer-Based Realization (OBR) of controllers. The interest of OBR from the controller implementation point of view is detailed and highlighted in this book through academic examples. An open-source toolbox is available to implement these approaches in Matlab(R). Throughout the book academic applications are proposed to illustrate the various basic principles. These applications have been chosen by the author for their pedagogic contents and demo files and embedded Matlab(R) functions can be downloaded so readers can run these illustrations on their personal computers. Contents 1. Observer-based Realization of a Given Controller. 2. Cross Standard Form and Reverse Engineering. 3. Reverse Engineering for Mechanical Systems. Appendix 1. A Preliminary Methodological Example. Appendix 2. Discrete-time Case. Appendix 3. Nominal State-feedback for Mechanical Systems. Appendix 4. Help of Matlab(R) Functions. About the Authors Daniel Alazard is Professor in System Dynamics and Control at Institut Superieur de l'Aeronautique et de l'Espace (ISAE), Toulouse, France -- SUPAERO Graduate Program. His main research interests concern robust control, flexible structure control and their applications to various aerospace systems.
(source: Nielsen Book Data)

• PREFACE xi
• 1 STATIC OPTIMIZATION 1 1.1 Optimization without Constraints / 1 1.2 Optimization with Equality Constraints / 4 1.3 Numerical Solution Methods / 15 Problems / 15
• 2 OPTIMAL CONTROL OF DISCRETE-TIME SYSTEMS 19 2.1 Solution of the General Discrete-Time Optimization Problem / 19 2.2 Discrete-Time Linear Quadratic Regulator / 32 2.3 Digital Control of Continuous-Time Systems / 53 2.4 Steady-State Closed-Loop Control and Suboptimal Feedback / 65 2.5 Frequency-Domain Results / 96 Problems / 102
• 3 OPTIMAL CONTROL OF CONTINUOUS-TIME SYSTEMS 110 3.1 The Calculus of Variations / 110 3.2 Solution of the General Continuous-Time Optimization Problem / 112 3.3 Continuous-Time Linear Quadratic Regulator / 135 3.4 Steady-State Closed-Loop Control and Suboptimal Feedback / 154 3.5 Frequency-Domain Results / 164 Problems / 167
• 4 THE TRACKING PROBLEM AND OTHER LQR EXTENSIONS 177 4.1 The Tracking Problem / 177 4.2 Regulator with Function of Final State Fixed / 183 4.3 Second-Order Variations in the Performance Index / 185 4.4 The Discrete-Time Tracking Problem / 190 4.5 Discrete Regulator with Function of Final State Fixed / 199 4.6 Discrete Second-Order Variations in the Performance Index / 206 Problems / 211
• 5 FINAL-TIME-FREE AND CONSTRAINED INPUT CONTROL 213 5.1 Final-Time-Free Problems / 213 5.2 Constrained Input Problems / 232 Problems / 257
• 6 DYNAMIC PROGRAMMING 260 6.1 Bellman's Principle of Optimality / 260 6.2 Discrete-Time Systems / 263 6.3 Continuous-Time Systems / 271 Problems / 283
• 7 OPTIMAL CONTROL FOR POLYNOMIAL SYSTEMS 287 7.1 Discrete Linear Quadratic Regulator / 287 7.2 Digital Control of Continuous-Time Systems / 292 Problems / 295
• 8 OUTPUT FEEDBACK AND STRUCTURED CONTROL 297 8.1 Linear Quadratic Regulator with Output Feedback / 297 8.2 Tracking a Reference Input / 313 8.3 Tracking by Regulator Redesign / 327 8.4 Command-Generator Tracker / 331 8.5 Explicit Model-Following Design / 338 8.6 Output Feedback in Game Theory and Decentralized Control / 343 Problems / 351
• 9 ROBUSTNESS AND MULTIVARIABLE FREQUENCY-DOMAIN TECHNIQUES 355 9.1 Introduction / 355 9.2 Multivariable Frequency-Domain Analysis / 357 9.3 Robust Output-Feedback Design / 380 9.4 Observers and the Kalman Filter / 383 9.5 LQG/Loop-Transfer Recovery / 408 9.6 H DESIGN / 430 Problems / 435
• 10 DIFFERENTIAL GAMES 438 10.1 Optimal Control Derived Using Pontryagin's Minimum Principle and the Bellman Equation / 439 10.2 Two-player Zero-sum Games / 444 10.3 Application of Zero-sum Games to H Control / 450 10.4 Multiplayer Non-zero-sum Games / 453
• 11 REINFORCEMENT LEARNING AND OPTIMAL ADAPTIVE CONTROL 461 11.1 Reinforcement Learning / 462 11.2 Markov Decision Processes / 464 11.3 Policy Evaluation and Policy Improvement / 474 11.4 Temporal Difference Learning and Optimal Adaptive Control / 489 11.5 Optimal Adaptive Control for Discrete-time Systems / 490 11.6 Integral Reinforcement Learning for Optimal Adaptive Control of Continuous-time Systems / 503 11.7 Synchronous Optimal Adaptive Control for Continuous-time Systems / 513 APPENDIX A REVIEW OF MATRIX ALGEBRA 518 A.1 Basic Definitions and Facts / 518 A.2 Partitioned Matrices / 519 A.3 Quadratic Forms and Definiteness / 521 A.4 Matrix Calculus / 523 A.5 The Generalized Eigenvalue Problem / 525 REFERENCES 527 INDEX 535.
• (source: Nielsen Book Data)

This new, updated edition reflects major changes that have occurred in the field in recent years and presents, in a clear and direct way, the fundamentals of optimal control theory. It covers the major topics involving measurement, principles of optimality, dynamic programming, variational methods, Kalman filtering, and other solution techniques. To give the reader a sense of the problems that can arise in a hands-on project, the authors have updated material on optimal output feedback control, a technique used in the aerospace industry. Additional new chapters cover adaptive control and adaptive optical control.
(source: Nielsen Book Data)

• 1. Introduction
• 2. The Parts of a Robot
• 3. Building an Inexpensive Robot by Modifying a Radio-Controlled Car
• 4. Beginning Electronics
• 5. Computer Arithmetic
• 6. Computer Logic
• 7. Introducing the MSP430 Microcontroller
• 8. Starting to Program - An Introduction to MSP430 Assembler
• 9. Building MSP430 Assembler Programs
• 10. Introducing C for the MSP430
• 11. More C and Mixing C With Assembler
• 12. MSP430 Parallel and Serial Ports
• 13. MSP430 Timing, Counters, and Interrupts
• 14. MSP430 Data Acquisition
• 15. Inexpensive Ways to Perform Circuit Simulation
• 16. Prototyping Circuits
• 17. Collision Avoidance
• 18. Adding a Tachometer
• 19. Controlling Things With a Remote
• 20. Troubleshooting
• 21. The Working, High-Performance Robot.
• (source: Nielsen Book Data)

This book provides a careful explanation of the basic areas of electronics and computer architecture, along with lots of examples, to demonstrate the interface, sensor design, programming and microcontroller peripheral setup necessary for embedded systems development. With no need for mechanical knowledge of robots, the book starts by demonstrating how to modify a simple radio-controlled car to create a basic robot. The fundamental electronics of the MSP430 are described, along with programming details in both C and assembly language, and full explanations of ports, timing, and data acquisition. Further chapters cover inexpensive ways to perform circuit simulation and prototyping. Key features include: Thorough treatment of the MSP430's architecture and functionality along with detailed application-specific guidance Programming and the use of sensor technology to build an embedded system A learn-by-doing experience With this book you will learn: The basic theory for electronics design - Analog circuits - Digital logic - Computer arithmetic - Microcontroller programming How to design and build a working robot Assembly language and C programming How to develop your own high-performance embedded systems application using an on-going robotics application.
(source: Nielsen Book Data)

Cellular actuators: modularity and variability in muscle-inspired actuation describes the roles actuators play in robotics and their insufficiency in emerging new robotic applications, such as wearable devices and human co-working robots where compactness and compliance are important. Piezoelectric actuators, the topic of this book, provide advantages like displacement scale, force, reliability, and compactness, and rely on material properties to provide displacement and force as reactions to electric stimulation. The authors, renowned researchers in the area, present the fundamentals of muscle-like movement and a system-wide study that includes the design, analysis, and control of biologically inspired actuators.

This proceedings volume brings together peer-reviewed papers presented at the International Conference on Information Technology and Computer Application Engineering, held 10-11 December 2014, in Hong Kong, China. Specific topics under consideration include Computational Intelligence, Computer Science and its Applications, Intelligent Information Processing and Knowledge Engineering, Intelligent Networks and Instruments, Multimedia Signal Processing and Analysis, Intelligent Computer-Aided Design Systems and other related topics. This book provides readers a state-of-the-art survey of recent innovations and research worldwide in Information Technology and Computer Application Engineering, in so-doing furthering the development and growth of these research fields, strengthening international academic cooperation and communication, and promoting the fruitful exchange of research ideas. This volume will be of interest to professionals and academics alike, serving as a broad overview of the latest advances in the dynamic field of Information Technology and Computer Application Engineering

• Front Matter
• 1 Problem of Accuracy
• 2 Accuracy-associated Models
• 3 Form-shaping Function: Mathematical Formulation
• 4 FSF of Serial-kinematics Systems
• 5 FSF of Parallel- and Hybrid-kinematics Machines
• 6 Machine Setup
• 7 Kinematic Setup of Robots
• 8 Error Budget
• 9 Accuracy of an Axis
• 10 Infinitesimal Kinematics of Serial-kinematics Machines
• 11 Infinitesimal Mechanics of the PKMs
• 12 Non-linear Problems of Machine Accuracy
• Back Matter

Accuracy is one of the fundamental characteristics and one of the most important indexes of the quality of machines and robots. It significantly defines their structure and applications and, in turn, depends on their structure and applications. Accuracy provision, maintenance, and enhancement are permanently hot problems in modern manufacturing and manufacturing science. The book is being published in two volumes and comprises the Introduction and four parts including 18 chapters with consecutive numbering, and three appendices. Each part combines a theoretical chapter with applications to machines and robots with different kinematic types. The formalized consideration is accompanied with application examples, which, as a rule, are explained with numerical solutions using realistic initial values. Volume 1 includes Introduction, Parts I and II (Chapters 1-12), and hence, embraces the general accuracy definitions, nominal machine functioning models, and geometrical accuracy problems. Volume 2 includes Parts III and IV and is available separately. The book is intended for machine and robot designers and researchers, university graduate and senior undergraduate students, and may be also useful for instructors in the mechanical engineering field for teaching processes.
(source: Nielsen Book Data)

"What jobs should be automated? How should our legal systems handle autonomous systems? How likely is the emergence of suprahuman intelligence? A.I. is the future of science, technology, and business--and there is no person better qualified or situated to explore that future than Max Tegmark. What has A.I. brought us? Where will it lead us? The story of A.I. is the story of intelligence--of life processes as they evolve from bacteria (1.0) to humans (2.0), where life processes define their own software, to technology (3.0), where life processes design both their hardware and software. We know that A.I. is transforming work, laws, and weapons, as well as the dark side of computing (hacking and viral sabotage), raising questions that we all need to address: What jobs should be automated? How should our legal systems handle autonomous systems? How likely is the emergence of suprahuman intelligence? Is it possible to control suprahuman intelligence? How do we ensure that the uses of A.I. remain beneficial? These are the issues at the heart of this book and its unique perspective, which seeks a ground apart from techno-skepticism and digital utopia"-- Provided by publisher.

"What jobs should be automated? How should our legal systems handle autonomous systems? How likely is the emergence of suprahuman intelligence? A.I. is the future of science, technology, and business--and there is no person better qualified or situated to explore that future than Max Tegmark. What has A.I. brought us? Where will it lead us? The story of A.I. is the story of intelligence--of life processes as they evolve from bacteria (1.0) to humans (2.0), where life processes define their own software, to technology (3.0), where life processes design both their hardware and software. We know that A.I. is transforming work, laws, and weapons, as well as the dark side of computing (hacking and viral sabotage), raising questions that we all need to address: What jobs should be automated? How should our legal systems handle autonomous systems? How likely is the emergence of suprahuman intelligence? Is it possible to control suprahuman intelligence? How do we ensure that the uses of A.I. remain beneficial? These are the issues at the heart of this book and its unique perspective, which seeks a ground apart from techno-skepticism and digital utopia"-- Provided by publisher.

• Foreword xiii Preface to the Second Edition xv Preface to the First Edition xvii 1Introduction 1 1.1 What Is Fuzzy Control? 1 1.2 Why Fuzzy Control? 2 1.3 Controller Design 3 1.4 Introductory Example: Stopping a Car 3 1.5 Nonlinear Control Systems 9 1.6 Summary 11 1.7 The Autopilot Simulator* 12 1.8 Notes and References* 13 2 Fuzzy Reasoning 17 2.1 Fuzzy Sets 17 2.1.1 Classical Sets 18 2.1.2 Fuzzy Sets 19 2.1.3 Universe 21 2.1.4 Membership Function 22 2.1.5 Possibility 24 2.2 Fuzzy Set Operations 25 2.2.1 Union, Intersection, and Complement 25 2.2.2 Linguistic Variables 28 2.2.3 Relations 30 2.3 Fuzzy If--Then Rules 33 2.3.1 Several Rules 35 2.4 Fuzzy Logic 36 2.4.1 Truth-Values 36 2.4.2 Classical Connectives 36 2.4.3 Fuzzy Connectives 39 2.4.4 Triangular Norms 41 2.5 Summary 43 2.6 Theoretical Fuzzy Logic* 43 2.6.1 Tautologies 43 2.6.2 Fuzzy Implication 45 2.6.3 Rules of Inference 48 2.6.4 Generalized Modus Ponens 51 2.7 Notes and References* 53 3 Fuzzy Control 55 3.1 The Rule Based Controller 56 3.1.1 Rule Base Block 56 3.1.2 Inference Engine Block 58 3.2 The Sugeno Controller 61 3.3 Autopilot Example: Four Rules 64 3.4 Table Based Controller 65 3.5 Linear Fuzzy Controller 68 3.6 Summary 70 3.7 Other Controller Components* 70 3.7.1 Controller Components 70 3.8 Other Rule Based Controllers* 77 3.8.1 The Mamdani Controller 77 3.8.2 The FLS Controller 79 3.9 Analytical Simplification of the Inference* 80 3.9.1 Four Rules 81 3.9.2 Nine Rules 82 3.10 Notes and References* 84 4 Linear Fuzzy PID Control 85 4.1 Fuzzy P Controller 87 4.2 Fuzzy PD Controller 89 4.3 Fuzzy PD+I Controller 90 4.4 Fuzzy Incremental Controller 92 4.5 Tuning 94 4.5.1 Ziegler--Nichols Tuning 94 4.5.2 Hand-Tuning 96 4.5.3 Scaling 99 4.6 Simulation Example: Third-Order Process 99 4.7 Autopilot Example: Stable Equilibrium 101 4.7.1 Result 102 4.8 Summary 103 4.9 Derivative Spikes and Integrator Windup* 104 4.9.1 Setpoint Weighting 104 4.9.2 Filtered Derivative 105 4.9.3 Anti-Windup 106 4.10 PID Loop Shaping* 106 4.11 Notes and References* 109 5 Nonlinear Fuzzy PID Control 111 5.1 Nonlinear Components 111 5.2 Phase Plot 113 5.3 Four Standard Control Surfaces 115 5.4 Fine-Tuning 118 5.4.1 Saturation in the Universes 119 5.4.2 Limit Cycle 119 5.4.3 Quantization 120 5.4.4 Noise 120 5.5 Example: Unstable Frictionless Vehicle 121 5.6 Example: Nonlinear Valve Compensator 124 5.7 Example: Motor Actuator with Limits 127 5.8 Autopilot Example: Regulating a Mass Load 127 5.9 Summary 130 5.10 Phase Plane Analysis* 130 5.10.1 Trajectory in the Phase Plane 131 5.10.2 Equilibrium Point 132 5.10.3 Stability 132 5.11 Geometric Interpretation of the PD Controller* 134 5.11.1 The Switching Line 137 5.11.2 A Rule Base for Switching 140 5.12 Notes and References* 143 6 The Self-Organizing Controller 145 6.1 Model Reference Adaptive Systems 145 6.2 The Original SOC 147 6.2.1 Adaptation Law 148 6.3 A Modified SOC 150 6.4 Example with a Long Deadtime 151 6.4.1 Tuning 151 6.4.2 Adaptation 153 6.4.3 Performance 153 6.5 Tuning and Time Lock 155 6.5.1 Tuning of the SOC Parameters 155 6.5.2 Time Lock 156 6.6 Summary 157 6.7 Example: Adaptive Control of a First-Order Process* 157 6.7.1 The MIT Rule 158 6.7.2 Choice of Control Law 159 6.7.3 Choice of Adaptation Law 159 6.7.4 Convergence 160 6.8 Analytical Derivation of the SOC Adaptation Law* 161 6.8.1 Reference Model 162 6.8.2 Adjustment Mechanism 162 6.8.3 The Fuzzy Controller 165 6.9 Notes and References* 169 7 Performance and Relative Stability 171 7.1 Reference Model 172 7.2 Performance Measures 177 7.3 PID Tuning from Performance Specifications 180 7.4 Gain Margin and Delay Margin 185 7.5 Test of Four Difficult Processes 186 7.5.1 Higher-Order Process 186 7.5.2 Double Integrator Process 187 7.5.3 Process with a Long Time Delay 188 7.5.4 Process with Oscillatory Modes 188 7.6 The Nyquist Criterion for Stability 188 7.6.1 Absolute Stability 189 7.6.2 Relative Stability 190 7.7 Relative Stability of the Standard Control Surfaces 191 7.8 Summary 193 7.9 Describing Functions* 193 7.9.1 Static Nonlinearity 195 7.9.2 Limit Cycle 197 7.10 Frequency Responses of the FPD and FPD+I Controllers* 198 7.10.1 FPD Frequency Response with a Linear Control Surface 200 7.10.2 FPD Frequency Response with Nonlinear Control Surfaces 201 7.10.3 The Fuzzy PD+I Controller 203 7.10.4 Limit Cycle 204 7.11 Analytical Derivation of Describing Functions for the Standard Surfaces* 206 7.11.1 Saturation Surface 206 7.11.2 Deadzone Surface 209 7.11.3 Quantizer Surface 213 7.12 Notes and References* 216 8 Fuzzy Gain Scheduling Control 217 8.1 Point Designs and Interpolation 218 8.2 Fuzzy Gain Scheduling 219 8.3 Fuzzy Compensator Design 221 8.4 Autopilot Example: Stopping on a Hilltop 226 8.5 Summary 228 8.6 Case Study: the FLS Controller* 229 8.6.1 Cement Kiln Control 229 8.6.2 High-Level Fuzzy Control 231 8.6.3 The FLS Design Procedure 233 8.7 Notes and References* 235 9 Fuzzy Models 237 9.1 Basis Function Architecture 238 9.2 Handmade Models 240 9.2.1 Approximating a Curve 240 9.2.2 Approximating a Surface 244 9.3 Machine-Made Models 249 9.3.1 Least-Squares Line Fit 249 9.3.2 Least-Squares Basis Function Fit 250 9.4 Cluster Analysis 253 9.4.1 Mahalanobis Distance 253 9.4.2 Hard Clusters, HCM Algorithm 257 9.4.3 Fuzzy Clusters, FCM Algorithm 260 9.5 Training and Testing 263 9.6 Summary 266 9.7 Neuro-Fuzzy Models* 267 9.7.1 Neural Networks 267 9.7.2 Gradient Descent Algorithm 268 9.7.3 Adaptive Neuro-Fuzzy Inference System (ANFIS) 273 9.8 Notes and References* 275 10 Demonstration Examples 277 10.1 Hot Water Heater 277 10.1.1 Installing a Timer Switch 278 10.1.2 Fuzzy P Controller 280 10.2 Temperature Control of a Tank Reactor 282 10.2.1 CSTR Model 283 10.2.2 Results and Discussion 285 10.3 Idle Speed Control of a Car Engine 287 10.3.1 Engine Model 287 10.3.2 Results and Discussion 288 10.4 Balancing a Ball on a Cart 292 10.4.1 Mathematical Model 293 10.4.2 Step
• 1: Design a Crisp PD Controller 297 10.4.3 Step
• 2: Replace it with a Linear Fuzzy 300 10.4.4 Step
• 3: Make it Nonlinear 300 10.4.5 Step
• 4: Fine-Tune it 301 10.5 Dynamic Model of a First-Order Process with a Nonlinearity 301 10.5.1 Supervised Model 302 10.5.2 Semi-Automatic Identification by a Modified HCM 304 10.6 Summary 307 10.7 Further State-Space Analysis of the Cart-Ball System* 307 10.7.1 Nonlinear Equations 313 10.8 Notes and References* 314 References 315 Index 319.
• (source: Nielsen Book Data)

• Foreword xiii Preface to the Second Edition xv Preface to the First Edition xvii 1Introduction 1 1.1 What Is Fuzzy Control? 1 1.2 Why Fuzzy Control? 2 1.3 Controller Design 3 1.4 Introductory Example: Stopping a Car 3 1.5 Nonlinear Control Systems 9 1.6 Summary 11 1.7 The Autopilot Simulator* 12 1.8 Notes and References* 13
• 2 Fuzzy Reasoning 17 2.1 Fuzzy Sets 17 2.2 Fuzzy Set Operations 25 2.3 Fuzzy If--Then Rules 33 2.4 Fuzzy Logic 36 2.5 Summary 43 2.6 Theoretical Fuzzy Logic* 43 2.7 Notes and References* 53
• 3 Fuzzy Control 55 3.1 The Rule Based Controller 56 3.2 The Sugeno Controller 61 3.3 Autopilot Example: Four Rules 64 3.4 Table Based Controller 65 3.5 Linear Fuzzy Controller 68 3.6 Summary 70 3.7 Other Controller Components* 70 3.8 Other Rule Based Controllers* 77 3.9 Analytical Simplification of the Inference* 80 3.10 Notes and References* 84
• 4 Linear Fuzzy PID Control 85 4.1 Fuzzy P Controller 87 4.2 Fuzzy PD Controller 89 4.3 Fuzzy PD+I Controller 90 4.4 Fuzzy Incremental Controller 92 4.5 Tuning 94 4.6 Simulation Example: Third-Order Process 99 4.7 Autopilot Example: Stable Equilibrium 101 4.8 Summary 103 4.9 Derivative Spikes and Integrator Windup* 104 4.10 PID Loop Shaping* 106 4.11 Notes and References* 109
• 5 Nonlinear Fuzzy PID Control 111 5.1 Nonlinear Components 111 5.2 Phase Plot 113 5.3 Four Standard Control Surfaces 115 5.4 Fine-Tuning 118 5.5 Example: Unstable Frictionless Vehicle 121 5.6 Example: Nonlinear Valve Compensator 124 5.7 Example: Motor Actuator with Limits 127 5.8 Autopilot Example: Regulating a Mass Load 127 5.9 Summary 130 5.10 Phase Plane Analysis* 130 5.11 Geometric Interpretation of the PD Controller* 134 5.12 Notes and References* 143
• 6 The Self-Organizing Controller 145 6.1 Model Reference Adaptive Systems 145 6.2 The Original SOC 147 6.3 A Modified SOC 150 6.4 Example with a Long Deadtime 151 6.5 Tuning and Time Lock 155 6.6 Summary 157 6.7 Example: Adaptive Control of a First-Order Process* 157 6.8 Analytical Derivation of the SOC Adaptation Law* 161 6.9 Notes and References* 169
• 7 Performance and Relative Stability 171 7.1 Reference Model 172 7.2 Performance Measures 177 7.3 PID Tuning from Performance Specifications 180 7.4 Gain Margin and Delay Margin 185 7.5 Test of Four Difficult Processes 186 7.6 The Nyquist Criterion for Stability 188 7.7 Relative Stability of the Standard Control Surfaces 191 7.8 Summary 193 7.9 Describing Functions* 193 7.10 Frequency Responses of the FPD and FPD+I Controllers* 198 7.11 Analytical Derivation of Describing Functions for the Standard Surfaces* 206 7.12 Notes and References* 216
• 8 Fuzzy Gain Scheduling Control 217 8.1 Point Designs and Interpolation 218 8.2 Fuzzy Gain Scheduling 219 8.3 Fuzzy Compensator Design 221 8.4 Autopilot Example: Stopping on a Hilltop 226 8.5 Summary 228 8.6 Case Study: the FLS Controller* 229 8.7 Notes and References* 235
• 9 Fuzzy Models 237 9.1 Basis Function Architecture 238 9.2 Handmade Models 240 9.3 Machine-Made Models 249 9.4 Cluster Analysis 253 9.5 Training and Testing 263 9.6 Summary 266 9.7 Neuro-Fuzzy Models* 267 9.8 Notes and References* 275
• 10 Demonstration Examples 277 10.1 Hot Water Heater 277 10.2 Temperature Control of a Tank Reactor 282 10.3 Idle Speed Control of a Car Engine 287 10.4 Balancing a Ball on a Cart 292 10.5 Dynamic Model of a First-Order Process with a Nonlinearity 301 10.6 Summary 307 10.7 Further State-Space Analysis of the Cart-Ball System* 307 10.8 Notes and References* 314 References 315 Index 319.
• (source: Nielsen Book Data)

Foundations of Fuzzy Control 2nd Edition: A Practical Approach has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. Foundations of Fuzzy Control 2nd Edition: A Practical Approach sets out practical worked-through problems, examples and case studies to illustrate each type of control system; and is accompanied by a website that hosts downloadable MATLAB programs and a web based course on Fuzzy Control which is taught by the author.
(source: Nielsen Book Data)
Foundations of Fuzzy Control: A Practical Approach, 2nd Edition has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. This new edition covers the basics of fuzzy control and builds a solid foundation for the design of fuzzy controllers, by creating links to established linear and nonlinear control theory. Advanced topics are also introduced and in particular, common sense geometry is emphasised. Key features Sets out practical worked through problems, examples and case studies to illustrate each type of control system Accompanied by a website hosting downloadable MATLAB programs Accompanied by an online course on Fuzzy Control which is taught by the author. Students can access further material and enrol at the companion website Foundations of Fuzzy Control: A Practical Approach, 2nd Edition is an invaluable resource for researchers, practitioners, and students in engineering. It is especially relevant for engineers working with automatic control of mechanical, electrical, or chemical systems.
(source: Nielsen Book Data)

• Robotics
• mechatronics
• manufacturing systems
• new technology.
• (source: Nielsen Book Data)

One of the most important problems in the field of engineering and technology is the development of the so-called "intelligent systems", which can perform various intellectual tasks. This work is dedicated to the current progress of research in this vast field and specifically explores the topics of robotics, mechatronics and manufacturing systems.
(source: Nielsen Book Data)

• 1. Introduction to Controls
• 2. The Frequency Domain
• 3. Tuning a Control System
• 4. Delay in Digital Controllers
• 5. The z-Domain
• 6. Six Types of Controllers
• 7. Disturbance Response
• 8. Feed-Forward
• 9. Filters in Control Systems
• 10. Introduction to Observers in Control Systems
• 11. Introduction to Modeling
• 12. Nonlinear Behavior and Time Variation
• 13. Model Development and Verification
• 14. Encoders and Resolvers
• 15. Basics of the Electric Servomotor and Drive
• 16. Compliance and Resonance
• 17. Position-Control Loops
• 18. Using the Luenberger Observer in Motion Control
• 19. Rapid Control Prototyping (RCP) for a Motion System.
• (source: Nielsen Book Data)

Control Systems Design Guide has helped thousands of engineers to improve machine performance. This fourth edition of the practical guide has been updated with cutting-edge control design scenarios, models and simulations enabling apps from battlebots to solar collectors. This useful reference enhances coverage of practical applications via the inclusion of new control system models, troubleshooting tips, and expanded coverage of complex systems requirements, such as increased speed, precision and remote capabilities, bridging the gap between the complex, math-heavy control theory taught in formal courses, and the efficient implementation required in real industry settings. George Ellis is Director of Technology Planning and Chief Engineer of Servo Systems at Kollmorgen Corporation, a leading provider of motion systems and components for original equipment manufacturers (OEMs) around the globe. He has designed an applied motion control systems professionally for over 30 years He has written two well-respected books with Academic Press, Observers in Control Systems and Control System Design Guide, now in its fourth edition. He has contributed articles on the application of controls to numerous magazines, including Machine Design, Control Engineering, Motion Systems Design, Power Control and Intelligent Motion, and Electronic Design News.
(source: Nielsen Book Data)

• Preface xi
• Series Preface xv
• Symbols and Acronyms xvii
• 1 Introduction 1
• 2 Fractional Calculus and Fractional-Order Systems 9
• 2.1 Fractional Calculus 9
• 2.1.1 Several Important Functions of Fractional Calculus 9
• 2.1.2 Fractional Integral and Derivatives 11
• 2.1.3 Some Important Lemmas 12
• 2.2 Some Typical Fractional-Order Systems 16
• 2.2.1 Fractional-Order Lorenz System 16
• 2.2.2 Fractional-Order Van Der Pol Oscillator 18
• 2.2.3 Fractional-Order Genesio-Tesi System 18
• 2.2.4 Fractional-Order Arneodo System 20
• 2.2.5 Fractional-Order Lotka-Volterra System 21
• 2.2.6 Fractional-Order Financial System 23
• 2.2.7 Fractional-Order Newton-Leipnik System 25
• 2.2.8 Fractional-Order Duffing System 27
• 2.2.9 Fractional-Order Lu System 29
• 2.2.10 Fractional-Order Three-Dimensional System 33
• 2.2.11 Fractional-Order Hyperchaotic Oscillator 35
• 2.2.12 Fractional-Order Four-Dimensional Hyperchaotic System 37
• 2.2.13 Fractional-Order Hyperchaotic Cellular Neural Network 39
• 2.3 Conclusion 41
• 3 Fractional-Order PID Controller and Fractional-Order Disturbance Observer 43
• 3.1 Problem Statement 43
• 3.2 Fractional-Order PID Controller 44
• 3.2.1 Integer-Order PID Controller 44
• 3.2.2 Fractional-Order PI?D? Controller 44
• 3.2.3 Control Based on Fractional-Order PI?D? Controller 45
• 3.3 Frequency-Domain Fractional-Order Disturbance Observer 48
• 3.3.1 Classical Integer-Order Disturbance Observer 48
• 3.3.2 Fractional-Order Disturbance Observer 49
• 3.3.3 Estimation Performance of Fractional-Order Disturbance Observer 51
• 3.3.4 Control Based on Fractional-Order Disturbance Observer 52
• 3.4 Conclusion 53
• 4 Design of Fractional-Order Controllers for Nonlinear Chaotic Systems and Some Applications 55
• 4.1 Fractional-Order Control for a Novel Chaotic SystemWithout Equilibrium 55
• 4.1.1 Problem Statement 55
• 4.1.2 Design of Chaotic System and Circuit Implementation 56
• 4.1.2.1 A Novel Chaotic System 56
• 4.1.2.2 Circuit Implementation 58
• 4.1.3 Design of Fractional-Order Controller and Stability Analysis 59
• 4.1.4 Numerical Simulation 62
• 4.1.4.1 Novel Chaotic System 62
• 4.1.4.2 Chaotic Systems with Equilibrium 63
• 4.2 Application of Chaotic System without Equilibrium in Image Encryption 68
• 4.2.1 Image Encryption Scheme 69
• 4.2.2 Histogram Analysis 69
• 4.2.3 Correlation of Two Adjacent Pixels 71
• 4.2.4 Anti-Attack Ability of Image Encryption Scheme 71
• 4.2.5 Sensitivity Analysis of Key 71
• 4.3 Synchronization Control for Fractional-Order Nonlinear Chaotic Systems 73
• 4.3.1 Problem Description 73
• 4.3.2 Design of Synchronization Controller 73
• 4.3.3 Simulation Examples 75
• 4.3.3.1 Fractional-Order Chen System 76
• 4.3.3.2 Fractional-Order Lorenz System 79
• 4.3.4 Application of Synchronization Control Scheme in Secure Communication 82
• 4.4 Conclusion 83
• 5 Sliding-Mode Control for Fractional-Order Nonlinear Systems Based on Disturbance Observer 85
• 5.1 Problem Statement 85
• 5.2 Adaptive Control Design Based on Fractional-Order Sliding-Mode Disturbance Observer 86
• 5.2.1 Design of Fractional-Order Sliding-Mode Disturbance Observer 86
• 5.2.2 Controller Design and Stability Analysis 87
• 5.3 Simulation Examples 89
• 5.3.1 Example 1 89
• 5.3.2 Example 2 91
• 5.4 Conclusion 94
• 6 Disturbance-Observer-Based Neural Control for Uncertain Fractional-Order Rotational Mechanical System 95
• 6.1 Problem Statement 95
• 6.2 Adaptive Neural Control Design 96
• 6.2.1 Design of Fractional-Order Disturbance Observer 96
• 6.2.2 Controller Design and Stability Analysis 97
• 6.3 Simulation Example 101
• 6.4 Conclusion 105
• 7 Adaptive Neural Tracking Control for Uncertain Fractional-Order Chaotic Systems Subject to Input Saturation and Disturbance 107
• 7.1 Problem Statement 107
• 7.2 Adaptive Neural Control Design Based on Fractional-Order Disturbance Observer 108
• 7.3 Simulation Examples 115
• 7.3.1 Fractional-Order Chaotic Electronic Oscillator 116
• 7.3.2 Fractional-OrderModified Jerk System 118
• 7.4 Conclusion 121
• 8 Stabilization Control of Continuous-Time Fractional Positive Systems Based on Disturbance Observer 123
• 8.1 Problem Statement 123
• 8.1.1 Notation and Definitions 123
• 8.1.2 Preliminaries 123
• 8.2 Main Results 126
• 8.2.1 Fractional Disturbance Observer 126
• 8.2.2 Stabilization Control of Fractional Positive System 128
• 8.2.3 Simulation of Fractional Positive System 130
• 8.2.4 Stabilization Control of Fractional Bounded Positive System 131
• 8.2.5 Simulation of Fractional Bounded Positive System 133
• 8.3 Conclusion 137
• 9 Sliding-Mode Synchronization Control for Fractional-Order Chaotic Systems with Disturbance 139
• 9.1 Problem Statement 139
• 9.2 Design of Fractional-Order Disturbance Observer 139
• 9.3 Disturbance-Observer-Based Synchronization Control of Fractional-Order Chaotic Systems 141
• 9.4 Simulation Examples 144
• 9.4.1 Synchronization Control of Modified Fractional-Order Jerk System 144
• 9.4.2 Synchronization Control of Fractional-Order Liu System 148
• 9.5 Conclusion 152
• 10 Anti-Synchronization Control for Fractional-Order Nonlinear Systems Using Disturbance Observer and Neural Networks 153
• 10.1 Problem Statement 153
• 10.2 Design of Disturbance Observer 153
• 10.3 Anti-Synchronization Control of Fractional-Order Nonlinear Systems 155
• 10.4 Simulation Examples 158
• 10.4.1 Anti-Synchronization Control of Fractional-Order Lorenz System 159
• 10.4.2 Anti-Synchronization Control of Fractional-Order Lu System 161
• 10.5 Conclusion 167
• 11 Synchronization Control for Fractional-Order Systems Subjected to Input Saturation 169
• 11.1 Problem Statement 169
• 11.2 Synchronization Control Design of Fractional-Order Systems with Input Saturation 170
• 11.3 Simulation Examples 172
• 11.3.1 Fractional-OrderModified Chua's Circuit with Sine Function 172
• 11.3.2 Fractional-Order Four-Dimensional Modified Chua's Circuit 174
• 11.4 Conclusion 179
• 12 Synchronization Control for Fractional-Order Chaotic Systems with Input Saturation and Disturbance 181
• 12.1 Problem Statement 181
• 12.2 Design of Fractional-Order Disturbance Observer 181
• 12.3 Design of Synchronization Control 183
• 12.4 Simulation Examples 185
• 12.4.1 Fractional-Order Chua's Circuit 185
• 12.4.2 Fractional-Order Hyperchaos Chua's Circuit 189
• 12.5 Conclusion 197
• Appendix A Fractional Derivatives of Some Functions 199
• A.1 Fractional Derivative of Constant 199
• A.2 Fractional Derivative of the Power Function 199
• A.3 Fractional Derivative of the Exponential Function 200
• A.4 Fractional Derivatives of Sine and Cosine Functions 201
• Appendix B Table of Caputo Derivatives 203
• Appendix C Laplace Transforms Involving Fractional Operations 205
• C.1 Laplace Transforms 205
• C.2 Special Functions for Laplace Transforms 205
• C.3 Laplace Transform Tables 205
• References 211
• Index 227.
• (source: Nielsen Book Data)

A treatise on investigating tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems, with Disturbance and Saturation provides the reader with a good understanding on how to achieve tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation. Although some texts have touched upon control of fractional-order systems, the issues of input saturation and disturbances have rarely been considered together. This book offers chapter coverage of fractional calculus and fractional-order systems; fractional-order PID controller and fractional-order disturbance observer; design of fractional-order controllers for nonlinear chaotic systems and some applications; sliding mode control for fractional-order nonlinear systems based on disturbance observer; disturbance observer based neural control for an uncertain fractional-order rotational mechanical system; adaptive neural tracking control for uncertain fractional-order chaotic systems subject to input saturation and disturbance; stabilization control of continuous-time fractional positive systems based on disturbance observer; sliding mode synchronization control for fractional-order chaotic systems with disturbance; and more. Based on the approximation ability of the neural network (NN), the adaptive neural control schemes are reported for uncertain fractional-order nonlinear systems Covers the disturbance estimation techniques that have been developed to alleviate the restriction faced by traditional feedforward control and reject the effect of external disturbances for uncertain fractional-order nonlinear systems By combining the NN with the disturbance observer, the disturbance observer based adaptive neural control schemes have been studied for uncertain fractional-order nonlinear systems with unknown disturbances Considers, together, the issue of input saturation and the disturbance for the control of fractional-order nonlinear systems in the present of system uncertainty, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems, with Disturbance and Saturation can be used as a reference for the academic research on fractional-order nonlinear systems or used in Ph.D. study of control theory and engineering.
(source: Nielsen Book Data)

• Chapter 1. The Automation Waves
• Chapter 2. Is This Time Different?
• Chapter 3. Information Technology: An Unprecedented Force for Disruption
• Chapter 4. White-Collar Jobs at Risk
• Chapter 5. Transforming Higher Education
• Chapter 6. The Health Care Challenge
• Chapter 7. Technologies and Industries of the Future
• Chapter 8. Consumers, Limits to Growth ... and Crisis?
• Chapter 9. Super-Intelligence and the Singularity
• Chapter 10. Toward a New Economic Paradigm.
• (source: Nielsen Book Data)

What are the jobs of the future? How many will there be? And who will have them? We might imagine and hope that today's industrial revolution will unfold like the last: even as some jobs are eliminated, more will be created to deal with the new innovations of a new era. In Rise of the Robots, Silicon Valley entrepreneur Martin Ford argues that this is absolutely not the case. As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence is already well on its way to making  good jobs" obsolete: many paralegals, journalists, office workers, and even computer programmers are poised to be replaced by robots and smart software. As progress continues, blue and white collar jobs alike will evaporate, squeezing working- and middle-class families ever further. At the same time, households are under assault from exploding costs, especially from the two major industries education and health care that, so far, have not been transformed by information technology. The result could well be massive unemployment and inequality as well as the implosion of the consumer economy itself.In Rise of the Robots, Ford details what machine intelligence and robotics can accomplish, and implores employers, scholars, and policy makers alike to face the implications. The past solutions to technological disruption, especially more training and education, aren't going to work, and we must decide, now, whether the future will see broad-based prosperity or catastrophic levels of inequality and economic insecurity. Rise of the Robots is essential reading for anyone who wants to understand what accelerating technology means for their own economic prospects not to mention those of their children as well as for society as a whole.
(source: Nielsen Book Data)

• Preface; Acknowledgements; List of abbreviations and notations; 1 Introduction; 1.1 Terminology; 1.1 Links, joints and kinematic chains; 1.2 Serial, parallel and hybrid robots; 1.2 Methodology of structural synthesis; 1.2.1 New formulae for mobility, connectivity, redundancy and overconstraint of parallel robots; 1.2.2 Evolutionary morphology approach; 1.2.3 Types of parallel robots with respect to motion coupling; 2 Parallel mechanisms with cylindrical motion of the moving platform; 2.1 T1R1-type parallel mechanisms with coupled cylindrical motion; 2.1.1 Overconstrained solutions.

• 2.1.2 Non overconstrained solutions2.2 T1R1-type parallel mechanisms with decoupled cylindrical motion; 2.2.1 Overconstrained solutions; 2.2.2 Non overconstrained solutions; 2.3 T1R1-type parallel mechanisms with uncoupled cylindrical motion; 2.3.1 Overconstrained solutions; 2.3.2 Non overconstrained solutions; 2.4 Maximally regular parallel mechanisms with cylindrical motion; 2.4.1 Overconstrained solutions; 2.4.2 Non overconstrained solutions; 3 Other T1R1-type parallel mechanisms; 3.1 T1R1-type parallel mechanisms with coupled motions; 3.1.1 Overconstrained solutions.

• 3.1.2 Non overconstrained solutions3.2 T1R1-type parallel mechanisms with decoupled motions; 3.2.1 Overconstrained solutions; 3.2.2 Non overconstrained solutions; 3.3 T1R1-type parallel mechanisms with uncoupled motions; 3.3.1 Overconstrained solutions; 3.3.2 Non overconstrained solutions; 3.4 Maximally regular T1R1-type parallel mechanisms; 3.4.1 Overconstrained solutions; 3.4.2 Non overconstrained solutions; 4 Parallel wrists with two degrees of freedom; 4.1 R2-type parallel wrists with coupled motions; 4.1.1 Overconstrained solutions; 4.1.2 Non overconstrained solutions.

• 4.2 R2-type parallel wrists with decoupled motions4.2.1 Overconstrained solutions; 4.2.2 Non overconstrained solutions; 4.3 R2-type parallel wrists with uncoupled motions; 4.3.1 Overconstrained solutions; 4.3.2 Non overconstrained solutions; 4.4 Maximally regular R2-type parallel wrists; 4.4.1 Overconstrained solutions; 4.4.2 Non overconstrained solutions; 5 T2R1-type overconstrained spatial parallel manipulators; 5.1 Overconstrained solutions with coupled motions; 5.1.1 Fully-parallel solutions; 5.1.2 Non fully-parallel solutions; 5.2 Overconstrained solutions with decoupled motions.

• 5.2.1 Fully-parallel solutions5.2.2 Non fully-parallel solutions; 5.3 Overconstrained solutions with uncoupled motions; 5.3.1 Fully-parallel solutions; 5.3.2 Non fully-parallel solutions; 5.4 Overconstrained maximally regular solutions; 5.4.1 Fully-parallel solutions; 5.4.2 Non fully-parallel solutions; 6 Non overconstrained T2R1-type spatial parallel manipulators; 6.1 Non overconstrained solutions with coupled motions; 6.1.1 Fully-parallel solutions; 6.1.2 Non fully-parallel solutions; 6.2 Non overconstrained solutions with decoupled motions; 6.2.1 Fully-parallel solutions.

This book represents the fourth part of a larger work dedicated to the structural synthesis of parallel robots. Part 1 (Gogu 2008a) presented the methodology of structural synthesis and the systematisation of structural solutions of simple and complex limbs with two to six degrees of connectivity systematically generated by the structural synthesis approach. Part 2 (Gogu 2009a) presented structural solutions of translational parallel robotic manipulators with two and three degrees of mobility. Part 3 (Gogu 2010a) focussed on structural solutions of parallel robotic manipulators with planar mot.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
• (source: Nielsen Book Data)

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Introduction / Neustein, Amy
• Contents
• List of authors
• Part I. The evolution and design of service robots in health care: evaluating the role of speech and other modalities in human-robot interaction
• 1. A critical analysis of speech- based interaction in healthcare robots: making a case for the increased use of speech in medical and assistive robots / Teixeira, António
• 2. Speech- based interaction with service robots: a survey of methods and approaches / Kulyukin, Vladimir
• 3. Improving patient-robot interaction in health care: service robot feature effects on patient acceptance and emotional responses / Swangnetr, Manida / Kaber, David B. / Zhu, Biwen / Zhang, Tao
• 4. Designing embodied and virtual agents for the operating room: taking a closer look at multimodal medical-service robots and other cyber-physical systems / Wachs, Juan P.
• Part II. Design and usability of medical and assistive robots in elder care: reporting on case studies and pilot test results
• 5. The emerging role of robotics for personal health management in the older-adult population / Tulu, Bengisu / Padir, Taskin / Linton, R. J. / Malehorn, Kevin / Liu, Tammy / Bzura, Conrad / Im, Hosung
• 6. Enabling older adults to interact with robots: why input methods are critical for usability / Beer, Jenay M. / Rogers, Wendy A.
• 7. Human-robot interaction for assistance with activities of daily living: a case study of the socially and cognitively engaging Brian 2.1 in the long-term care setting / McColl, Derek / Nejat, Goldie
• Part III. Speech-driven companion robots for children with medical and neurodevelopmental disorders: presenting empirical findings of EU-sponsored projects and prototypes
• 8. Voice-enabled assistive robots for handling autism spectrum conditions: an examination of the role of prosody / Marchi, Erik / Ringeval, Fabien / Schuller, Björn
• 9. ASR and TTS for voice controlled child-robot interactions in italian: empirical study findings on the Aliz-e project for treating children with metabolic disorders in the hospital setting / Sommavilla, Giacomo / Tesser, Fabio / Paci, Giulio / Cosi, Piero
• Editor's biography

Examines various speech technologies deployed in healthcare service robots to maximize the robot's ability to interpret user input. Demonstrates how robot anthropomorphic features and etiquette in behavior promotes user-positive emotions, acceptance of robots, and compliance with robot requests. Analyzes how multimodal medical-service robots and other cyber-physical systems can reduce mistakes and mishaps in the operating room. Evaluates various input methods for improving acceptance of robots in the older adult population. Presents case studies of cognitively and socially engaging robots in the long-term care setting for helping older adults with activities of daily living and in the pediatric setting for helping children with autism spectrum conditions and metabolic disorders. Speech and Automata in Health Care forges new ground by closely analyzing how three separate disciplines - speech technology, robotics, and medical/surgical/assistive care - intersect with one another, resulting in an innovative way of diagnosing and treating both juvenile and adult illnesses and conditions. This includes the use of speech-enabled robotics to help the elderly population cope with common problems associated with aging caused by the diminution in their sensory, auditory and motor capabilities. By examining the emerging nexus of speech, automata, and health care, the authors demonstrate the exciting potential of automata, both speech-driven and multimodal, to affect the healthcare delivery system so that it better meets the needs of the populations it serves. This book provides both empirical research findings and incisive literature reviews that demonstrate some of the more novel uses of speech-enabled and multimodal automata in the operating room, hospital ward, long-term care facility, and in the home. Studies backed by major universities, research institutes, and by EU-funded collaborative projects are debuted in this volume. This volume provides a wealth of timely material for industrial engineers, speech scientists, computational linguists, and for signal processing and intelligent systems design experts. Topics include: Spoken Interaction with Healthcare Robots Service Robot Feature Effects on Patient Acceptance/Emotional Response Designing Embodied and Virtual Agents for the Operating Room The Emerging Role of Robotics for Personal Health Management in the Older-Adult Population Why Input Methods for Robots that Serve the Older Adult Are Critical for Usability Socially and Cognitively Engaging Robots in the Long-Term Care Setting Voice-Enabled Assistive Robots for Managing Autism Spectrum Conditions ASR and TTS for Voice-Controlled Robot Interactions in Treating Children with Metabolic Disorders

• Fundamentals of extremum seeking
• Gradient extremum seeking for scalar static map with delay
• Newton-based ES for scalar static map with delay
• Inverse optimal ES under delay
• Stochastic ES for higher-derivative and dynamic maps with delay
• Robustness to delay mismatch in ES
• Multivariable ES for distinct input delays
• ES under time-varying and state-dependent delays
• ES for distributed delays
• ES for heat PDE
• ES for reaction-advection-diffusion PDEs
• ES for wave PDEs
• Multivariable ES for distinct families of PDEs
• ES for PDE-PDE cascades
• Nash equilibrium seeking with arbitrarily delayed player actions
• Nash equilibrium seeking with players acting through heat PDE dynamics
• Heterogeneous duopoly games with delays and heat PDEs
• Appendix A. Averaging theorem for functional differential equations
• Appendix B. Averaging theorem for general infinite-dimensional systems
• Appendix C. Small-gain theorems
• Appendix D. Auxiliary proofs and derivations
• Appendix E. Important inequalities

Extremum Seeking through Delays and PDEs, the first book on the topic, expands the scope of applicability of the extremum seeking method, from static and finite-dimensional systems to infinite-dimensional systems. In this book, numerous algorithms for model-free real-time optimization are developed and their convergence guaranteed, extensions from single-player optimization to noncooperative games, under delays and PDEs, are provided, the delays and PDEs are compensated in the control designs using the PDE backstepping approach, and stability is ensured using infinite-dimensional versions of averaging theory, and accessible and powerful tools are provided for analysis

• Introduction
• Microcontrollers, Single-board computers and development tools
• Building Linux-based overmonitoring systems
• Creating a custom slim Linux distribution
• Example Sensor and Control Applications
• Designing for System Robustness
• Tying it all together--.
• (source: Nielsen Book Data)

In this practical reference, popular author Lewin Edwards shows how to develop robust, dependable real-time systems for robotics and other control applications, using open-source tools. It demonstrates efficient and low-cost embedded hardware and software design techniques, based on Linux as the development platform and operating system and the Atmel AVR as the primary microcontroller. The book provides comprehensive examples of sensor, actuator and control applications and circuits, along with source code for a number of projects. It walks the reader through the process of setting up the Linux-based controller, from creating a custom kernel to customizing the BIOS, to implementing graphical control interfaces. Including detailed design information on: * ESBUS PC-host interface * Host-module communications protocol * A speed-controlled DC motor with tach feedback and thermal cut-off * A stepper motor controller * A two-axis attitude sensor using a MEMS accelerometer * Infrared remote control in Linux using LIRC * Machine vision using Video4Linux.
(source: Nielsen Book Data)