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• Preface xiii List of Acronyms xv
• 1 Introduction 1 1.1 Control Engineering and Control Theory 2 1.2 Classical and Modern Control 5 1.3 The Evolution of the NMSS Model Form 8 1.4 True Digital Control 11 1.5 Book Outline 12 1.6 Concluding Remarks 13 References 14
• 2 Discrete-Time Transfer Functions 17 2.1 Discrete-Time TF Models 18 2.1.1 The Backward Shift Operator 18 2.1.2 General Discrete-Time TF Model 22 2.1.3 Steady-State Gain 23 2.2 Stability and the Unit Circle 24 2.3 Block Diagram Analysis 26 2.4 Discrete-Time Control 28 2.5 Continuous to Discrete-Time TF Model Conversion 36 2.6 Concluding Remarks 38 References 38
• 3 Minimal State Variable Feedback 41 3.1 Controllable Canonical Form 44 3.1.1 State Variable Feedback for the General TF Model 49 3.2 Observable Canonical Form 50 3.3 General State Space Form 53 3.3.1 Transfer Function Form of a State Space Model 53 3.3.2 The Characteristic Equation, Eigenvalues and Eigenvectors 55 3.3.3 The Diagonal Form of a State Space Model 57 3.4 Controllability and Observability 58 3.4.1 Definition of Controllability (or Reachability) 58 3.4.2 Rank Test for Controllability 59 3.4.3 Definition of Observability 59 3.4.4 Rank Test for Observability 59 3.5 Concluding Remarks 61 References 62
• 4 Non-Minimal State Variable Feedback 63 4.1 The NMSS Form 64 4.1.1 The NMSS (Regulator) Representation 64 4.1.2 The Characteristic Polynomial of the NMSS Model 67 4.2 Controllability of the NMSS Model 68 4.3 The Unity Gain NMSS Regulator 69 4.3.1 The General Unity Gain NMSS Regulator 74 4.4 Constrained NMSS Control and Transformations 77 4.4.1 Non-Minimal State Space Design Constrained to yield a Minimal SVF Controller 79 4.5 Worked Example with Model Mismatch 81 4.6 Concluding Remarks 85 References 86
• 5 True Digital Control for Univariate Systems 89 5.1 The NMSS Servomechanism Representation 93 5.1.1 Characteristic Polynomial of the NMSS Servomechanism Model 95 5.2 Proportional-Integral-Plus Control 98 5.2.1 The Closed-Loop Transfer Function 99 5.3 Pole Assignment for PIP Control 101 5.3.1 State Space Derivation 101 5.4 Optimal Design for PIP Control 110 5.4.1 Linear Quadratic Weighting Matrices 111 5.4.2 The LQ Closed-loop System and Solution of the Riccati Equation 112 5.4.3 Recursive Solution of the Discrete-Time Matrix Riccati Equation 114 5.5 Case Studies 116 5.6 Concluding Remarks 119 References 120
• 6 Control Structures and Interpretations 123 6.1 Feedback and Forward Path PIP Control Structures 123 6.1.1 Proportional-Integral-Plus Control in Forward Path Form 125 6.1.2 Closed-loop TF for Forward Path PIP Control 126 6.1.3 Closed-loop Behaviour and Robustness 127 6.2 Incremental Forms for Practical Implementation 131 6.2.1 Incremental Form for Feedback PIP Control 131 6.2.2 Incremental Form for Forward Path PIP Control 134 6.3 The Smith Predictor and its Relationship with PIP Design 137 6.3.1 Theorem 6.1 Relationship between PIP and SP-PIP Control Gains 139 6.3.2 Complete Equivalence of the SP-PIP and Forward Path PIP Controllers 140 6.4 Stochastic Optimal PIP Design 142 6.4.1 Stochastic NMSS Equations and the Kalman Filter 142 6.4.2 Polynomial Implementation of the Kalman Filter 144 6.4.3 Stochastic Closed-loop System 149 6.4.4 Other Stochastic Control Structures 150 6.4.5 Modified Kalman Filter for Non-Stationary Disturbances 151 6.4.6 Stochastic PIP Control using a Risk Sensitive Criterion 152 6.5 Generalised NMSS Design 153 6.5.1 Feed-forward PIP Control based on an Extended Servomechanism NMSS Model 153 6.5.2 Command Anticipation based on the Servomechanism NMSS Model 154 6.6 Model Predictive Control 157 6.6.1 Model Predictive Control based on NMSS Models 158 6.6.2 Generalised Predictive Control 158 6.6.3 Structural Equivalence Between GPC and PIP Control 159 6.6.4 Theorem 6.2 Equivalence Between GPC and (Constrained) PIP-LQ 160 6.6.5 Observer Filters 162 6.7 Concluding Remarks 163 References 164
• 7 True Digital Control for Multivariable Systems 167 7.1 The Multivariable NMSS (Servomechanism) Representation 168 7.1.1 The General Multivariable System Description 170 7.1.2 Multivariable NMSS Form 171 7.1.3 The Characteristic Polynomial of the Multivariate NMSS Model 173 7.2 Multivariable PIP Control 175 7.3 Optimal Design for Multivariable PIP Control 177 7.4 Multi-Objective Optimisation for PIP Control 186 7.4.1 Goal Attainment 187 7.5 Proportional-Integral-Plus Decoupling Control by Algebraic Pole Assignment 192 7.5.1 Decoupling Algorithm I 193 7.5.2 Implementation Form 194 7.5.3 Decoupling Algorithm II 195 7.6 Concluding Remarks 195 References 196
• 8 Data-Based Identification and Estimation of Transfer Function Models 199 8.1 Linear Least Squares, ARX and Finite Impulse Response Models 200 8.1.1 En bloc LLS Estimation 202 8.1.2 Recursive LLS Estimation 203 8.1.3 Statistical Properties of the RLS Algorithm 205 8.1.4 The FIR Model 210 8.2 General TF Models 211 8.2.1 The Box--Jenkins and ARMAX Models 212 8.2.2 A Brief Review of TF Estimation Algorithms 213 8.2.3 Standard IV Estimation 215 8.3 Optimal RIV Estimation 218 8.3.1 Initial Motivation for RIV Estimation 218 8.3.2 The RIV Algorithm in the Context of ML 220 8.3.3 Simple AR Noise Model Estimation 222 8.3.4 RIVAR Estimation: RIV with Simple AR Noise Model Estimation 223 8.3.5 Additional RIV Algorithms 226 8.3.6 RIVAR and IV4 Estimation Algorithms 227 8.4 Model Structure Identification and Statistical Diagnosis 231 8.4.1 Identification Criteria 232 8.4.2 Model Structure Identification Procedure 234 8.5 Multivariable Models 243 8.5.1 The Common Denominator Polynomial MISO Model 243 8.5.2 The MISO Model with Different Denominator Polynomials 246 8.6 Continuous-Time Models 248 8.6.1 The SRIV and RIVBJ Algorithms for Continuous-Time Models 249 8.6.2 Estimation of delta-Operator Models 253 8.7 Identification and Estimation in the Closed-Loop 253 8.7.1 The Generalised Box-Jenkins Model in a Closed-Loop Context 254 8.7.2 Two-Stage Closed-Loop Estimation 255 8.7.3 Three-Stage Closed-Loop Estimation 256 8.7.4 Unstable Systems 260 8.8 Concluding Remarks 260 References 261
• 9 Additional Topics 265 9.1 The delta-Operator Model and PIP Control 266 9.1.1 The delta-operator NMSS Representation and its Characteristic Polynomial 267 9.1.2 Theorem 9.1 Controllability of the delta-operator NMSS Model 269 9.1.3 The delta-Operator PIP Control Law 269 9.1.4 Implementation Structures for delta-Operator PIP Control 270 9.1.5 Pole Assignment delta-Operator PIP Design 271 9.1.6 Linear Quadratic Optimal delta-Operator PIP Design 272 9.2 Time Variable Parameter Estimation 279 9.2.1 Simple Limited Memory Algorithms 281 9.2.2 Modelling the Parameter Variations 282 9.2.3 State Space Model for DTF Estimation 284 9.2.4 Optimisation of the Hyper-parameters 287 9.3 State-Dependent Parameter Modelling and PIP Control 290 9.3.1 The SDP-TF Model 290 9.3.2 State-Dependent Parameter Model Identification and Estimation 292 9.3.3 Proportional-Integral-Plus Control of SDP Modelled Systems 293 9.4 Concluding Remarks 298 References 298 A Matrices and Matrix Algebra 301 A.1 Matrices 301 A.2 Vectors 302 A.3 Matrix Addition (or Subtraction) 302 A.4 Matrix or Vector Transpose 302 A.5 Matrix Multiplication 303 A.6 Determinant of a Matrix 304 A.7 Partitioned Matrices 305 A.8 Inverse of a Matrix 306 A.9 Quadratic Forms 307 A.10 Positive Definite or Semi-Definite Matrices 308 A.11 The Rank of a Matrix 308 A.12 Differentiation of Vectors and Matrices 308 References 310 B The Time Constant 311 Reference 311 C Proof of Theorem 4.1 313 References 314 D Derivative Action Form of the Controller 315 E Block Diagram Derivation of PIP Pole Placement Algorithm 317 F Proof of Theorem 6.1 321 Reference 322 G The CAPTAIN Toolbox 323 G.1 Transfer Functions and Control System Design 323 G.2 Other Routines 324 References 325 H The Theorem of D.A. Pierce (1972) 327 References 328 Index.
• (source: Nielsen Book Data)

True Digital Control: Statistical Modelling and Non--Minimal State Space Design develops a true digital control design philosophy that encompasses data--based model identification, through to control algorithm design, robustness evaluation and implementation. With a heritage from both classical and modern control system synthesis, this book is supported by detailed practical examples based on the authors' research into environmental, mechatronic and robotic systems. Treatment of both statistical modelling and control design under one cover is unusual and highlights the important connections between these disciplines. Starting from the ubiquitous proportional--integral controller, and with essential concepts such as pole assignment introduced using straightforward algebra and block diagrams, this book addresses the needs of those students, researchers and engineers, who would like to advance their knowledge of control theory and practice into the state space domain; and academics who are interested to learn more about non--minimal state variable feedback control systems. Such non--minimal state feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner. Key features: Covers both system identification and control system design in a unified manner Includes practical design case studies and simulation examples Considers recent research into time--variable and state--dependent parameter modelling and control, essential elements of adaptive and nonlinear control system design, and the delta--operator (the discrete--time equivalent of the differential operator) systems Accompanied by a website hosting MATLAB examples True Digital Control: Statistical Modelling and Non--Minimal State Space Design is a comprehensive and practical guide for students and professionals who wish to further their knowledge in the areas of modern control and system identification.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Contents
• 1. Introduction / Zentner, L. / Linß, S.
• 2. Definition and classification of compliant systems / Zentner, L. / Linß, S.
• 3. Modeling compliant systems as rigid-body systems / Zentner, L.
• 4. Modeling large deflections of curved rodlike structures / Zentner, L.
• 5. Examples of modeling large deflections of curved rod-like structures / Zentner, L.
• 6. Synthesis of compliant mechanisms and design of flexure hinges / Linß, S.
• References
• Index

Compliant mechanisms and actuators are growing in importance due to their benefits in robotics, medical technology, sensor applications, or in handling compressible objects. This book helps to understand the mechanical behavior of compliant systems. Suggested classifications and different modeling methods are shown that allow for the description of compliant systems.
(source: Nielsen Book Data)

• 1. Introduction
• 1.1. The history of ships
• 1.2. Review of the development of ships
• 1.3. Future trends

• 2. Background of artificial intelligence (AI)
• 2.1. Review of AI
• 2.2. The fundamentals and implication areas of AI
• 2.3. The change of maritime in AI
• 2.4. Future trends

• 3. Evolution of maritime autonomous surface ships
• 3.1. Definitions of MASS
• 3.2. The development of autonomy in the world
• 3.3. Change of MASS systems
• 3.4. Elements of autonomous technologies
• 3.5. Technical and operational constraints
• 3.6. Future trends

• 4. Impact, development and potential for MASS
• 4.1. Crew careers status
• 4.2. Maritime industry shocks
• 4.3. The influence of port transportation
• 4.4. Safety and security in MASS
• 4.5. Legal implications of MASS
• 4.6. The post-COVID-19 pandemic era
• 4.7. Future trends

• 5. Concerns and challenges
• 5.1. Technological environment
• 5.2. Social responsibility
• 6. Conclusion and future trends.

Autonomous ships, like autonomous cars, can guide themselves without human conduction. AI and the future of computers taking over the art of driving is now our reality. The autonomous ship is a vessel that can operate fully on its own, without a crew, and is equipped with software to manage its movements. The interest in this field is growing rapidly with conferences dedicated to this specific technology. Forbes has stated that autonomous ships increase efficiency, improve safety, and relieve humans from unsafe and repetitive tasks.

• 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)

• Introduction
• Methods
• The first major AV safety study: Schoettle and Sivak (2015)
• Google AV crash data versus naturalistic crash data: Blanco et al. (2016)
• Crash selection bias and site selection bias: Dixit et al. (2016)
• Crash selection biases and site selection biases: Teoh and Kidd (2017)
• Heterogeneity and crash selection bias: Favarò et al. (2017)
• Crash selection bias and site selection bias: Banerjee et al. (2018)
• Overall summary of AV IRR estimates and issues
• Overall discussion
• Overall conclusions
• Specific recommendations
• Appendix A: AV hopes and modeling studies
• Appendix B: Abbreviations and definitions
• Appendix C: Transition crashes: descriptions and comments
• Appendix D: Should crash reports by AV companies be accepted at face value?
• Appendix E: Why unreported and unrecorded CV crashes?
• Appendix F: Are AVs or human drivers at fault in a crash?
• Appendix G: Crash severity definitions in naturalistic driving studies
• Appendix H: Standardization method
• Appendix I: Replication of Teoh and Kidd (2017) phase 1 CV crash results (Table 12, Note f)
• Appendix J: Crash reduction: AVs versus crash avoidance technologies
• Appendix K: Site selection bias: site differences in CV crash rates
• Appendix L: Summary of issues
• Appendix M: Do ADS neural networks have negative learning?
• Appendix N: PR crash selection bias
• Appendix: Bibliography
• Epilogue
• About the Author
• Index.

Will Automated Vehicles be Safer than Conventional Vehicles? One of the critically important questions that has emerged about advanced technologies in transportation is how to test the actual effects of these advanced systems on safety, particularly how to evaluate the safety of highly automated driving systems. Richard Young's Critical Analysis of Prototype Autonomous Vehicle Crash Rates does a deep dive into these questions by reviewing and then critically analyzing the first six scientific studies of AV crash rates.
(source: Nielsen Book Data)

• Preface xiii Acknowledgements xv 1 Introduction to Microcontrollers and Display Systems 1 1.1 Microcontrollers and Microprocessors 2 1.2 Evolution of the Microcontroller 3 1.3 Parts of a Microcontroller 4 1.3.1 Address 4 1.3.2 ALU 5 1.3.3 Analogue Comparator 5 1.3.4 Analogue-to-Digital Converter 5 1.3.5 Brown-out Detector 5 1.3.6 Bus 5 1.3.7 CAN 6 1.3.8 CISC 6 1.3.9 Clock 6 1.3.10 CPU 6 1.3.11 EEPROM 6 1.3.12 EPROM 6 1.3.13 Ethernet 7 1.3.14 Flash Memory 7 1.3.15 Harvard Architecture 7 1.3.16 Idle Mode 7 1.3.17 Interrupts 7 1.3.18 LCD Drivers 8 1.3.19 Pipelining 8 1.3.20 Power-on Reset 8 1.3.21 PROM 8 1.3.22 RAM 8 1.3.23 Real-time Clock 8 1.3.24 Register 9 1.3.25 Reset 9 1.3.26 RISC 9 1.3.27 ROM 9 1.3.28 Serial Input-Output 9 1.3.29 Sleep Mode 9 1.3.30 Supply Voltage 10 1.3.31 Timers 10 1.3.32 USB 10 1.3.33 Watchdog 10 1.4 Display Devices 10 1.4.1 LED 10 1.4.2 7-Segment LED 11 1.4.3 OLED 12 1.4.4 LCD 12 1.5 Summary 15 Exercises 15 2 PIC18F Microcontrollers 17 2.1 The PIC18F2410 Microcontroller 18 2.2 PIC18F2410 Architecture 19 2.2.1 The Program Memory 21 2.2.2 The Data Memory 21 2.2.3 Power Supply Requirements 22 2.2.4 Oscillator Configurations 24 2.2.5 The Reset 30 2.2.6 Parallel I/O Ports 31 2.2.7 Timer Modules 38 2.2.8 Analogue-to-Digital Converter Module 43 2.2.9 Special Features of the CPU 48 2.2.10 Interrupts 49 2.2.11 Pulse Width Modulator Module 53 2.3 Summary 56 Exercises 56 3 C Programming Language 59 3.1 C Languages for Microcontrollers 59 3.2 Your First mikroC Pro for PIC Program 61 3.2.1 Comments 61 3.2.2 Beginning and Ending a Program 62 3.2.3 White Spaces 63 3.2.4 Variable Names 63 3.2.5 Reserved Names 64 3.2.6 Variable Types 64 3.2.7 Constants 66 3.2.8 Escape Sequences 68 3.2.9 Volatile Variables 69 3.2.10 Accessing Bits of a Variable 69 3.2.11 sbit Type 70 3.2.12 bit Type 70 3.2.13 Arrays 70 3.2.14 Pointers 73 3.2.15 Structures 76 3.2.16 Unions 80 3.2.17 Operators in mikroC Pro for PIC 80 3.2.18 The Flow of Control 90 3.3 Functions in mikroC Pro for PIC 101 3.3.1 Function Prototypes 102 3.3.2 void Functions 103 3.3.3 Passing Parameters to Functions 104 3.3.4 Passing Arrays to Functions 106 3.3.5 Interrupt Processing 106 3.4 mikroC Pro for PIC Built-in Functions 108 3.5 mikroC Pro for PIC Libraries 109 3.5.1 ANSI C Library 109 3.5.2 Miscellaneous Library 111 3.6 Using the mikroC Pro for PIC Compiler 111 3.6.1 mikroC Pro for PIC IDE 112 3.6.2 Creating a New Source File 118 3.6.3 Compiling the Source File 122 3.7 Using the mikroC Pro for PIC Simulator 123 3.7.1 Setting a Break-Point 124 3.8 Other mikroC Pro for PIC Features 126 3.8.1 View Statistics 126 3.8.2 View Assembly 127 3.8.3 ASCII Chart 127 3.8.4 USART Terminal 127 3.8.5 Seven Segment Editor 127 3.8.6 Help 128 3.9 Summary 128 Exercises 129 4 PIC Microcontroller Development Tools
• Including Display Development Tools 131 4.1 PIC Hardware Development Boards 132 4.1.1 Super Bundle Development Kit 132 4.1.2 PIC18 Explorer Board 132 4.1.3 PIC18F4XK20 Starter Kit 134 4.1.4 PICDEM 4 135 4.1.5 PIC16F887 Development Kit 135 4.1.6 FUTURLEC PIC18F4550 Development Board 137 4.1.7 EasyPIC6 Development Board 137 4.1.8 EasyPIC7 Development Board 139 4.2 PIC Microcontroller Display Development Tools 140 4.2.1 Display Hardware Tools 140 4.2.2 Display Software Tools 143 4.3 Using the In-Circuit Debugger with the EasyPIC7 Development Board 145 4.4 Summary 149 Exercises 149 5 Light Emitting Diodes (LEDs) 151 5.1 ATypical LED 151 5.2 LED Colours 153 5.3 LED Sizes 154 5.4 Bi-Colour LEDs 154 5.5 Tri-Colour LEDs 155 5.6 Flashing LEDs 155 5.7 Other LED Shapes 155 5.8 7-Segment LEDs 156 5.8.1 Displaying Numbers 157 5.8.2 Multi-digit 7-Segment Displays 159 5.9 Alphanumeric LEDs 159 5.10 mikroC Pro for PIC 7-Segment LED Editor 163 5.11 Summary 163 Exercises 164 6 Liquid Crystal Displays (LCDs) and mikroC Pro for PIC LCD Functions 165 6.1 HD44780 Controller 165 6.2 Displaying User Defined Data 168 6.3 DDRAM Addresses 169 6.4 Display Timing and Control 171 6.4.1 Clear Display 172 6.4.2 Return Cursor to Home 172 6.4.3 Cursor Move Direction 172 6.4.4 Display ON/OFF 172 6.4.5 Cursor and Display Shift 173 6.4.6 Function Set 173 6.4.7 Set CGRAM Address 173 6.4.8 Set DDRAM Address 173 6.4.9 Read Busy Flag 174 6.4.10 Write Data to CGRAM or DDRAM 174 6.4.11 Read Data from CGRAM or DDRAM 174 6.5 LCD Initialisation 174 6.5.1 8-bit Mode Initialisation 175 6.5.2 4-bit Mode Initialisation 175 6.6 Example LCD Display Setup Program 177 6.7 mikroC Pro for PIC LCD Functions 180 6.7.1 Lcd-Init 180 6.7.2 Lcd-Out 181 6.7.3 Lcd-Out-Cp 181 6.7.4 Lcd-Chr 181 6.7.5 Lcd-Chr-Cp 181 6.7.6 Lcd-Cmd 182 6.8 Summary 182 Exercises 183 7 Graphics LCD Displays (GLCD) 185 7.1 The 128 x 64 Pixel GLCD 185 7.2 Operation of the GLCD Display 187 7.3 mikroC Pro for PIC GLCD Library Functions 189 7.3.1 Glcd-Init 189 7.3.2 Glcd-Set-Side 190 7.3.3 Glcd-Set-X 190 7.3.4 Glcd-Set-Page 190 7.3.5 Glcd-Write-Data 190 7.3.6 Glcd-Fill 190 7.3.7 Glcd-Dot 191 7.3.8 Glcd-Line 191 7.3.9 Glcd-V-Line 191 7.3.10 Glcd-H-Line 191 7.3.11 Glcd-Rectangle 192 7.3.12 Glcd-Rectangle-Round-Edges 192 7.3.13 Glcd-Rectangle-Round-Edges-Fill 192 7.3.14 Glcd-Box 193 7.3.15 Glcd-Circle 193 7.3.16 Glcd-Circle-Fill 194 7.3.17 Glcd-Set-Font 194 7.3.18 Glcd-Set-Font-Adv 194 7.3.19 Glcd-Write-Char 195 7.3.20 Glcd-Write-Char-Adv 195 7.3.21 Glcd-Write-Text 195 7.3.22 Glcd-Write-Text-Adv 195 7.3.23 Glcd-Write-Const-Text-Adv 196 7.3.24 Glcd-Image 196 7.4 Example GLCD Display 196 7.5 mikroC Pro for PIC Bitmap Editor 198 7.6 Adding Touch-screen to GLCDs 199 7.6.1 Types of Touch-screen Displays 200 7.6.2 Resistive Touch Screens 200 7.7 Summary 203 Exercises 204 8 Microcontroller Program Development 205 8.1 Using the Program Description Language and Flowcharts 205 8.1.1 BEGIN
• END 206 8.1.2 Sequencing 206 8.1.3 IF
• THEN
• ELSE
• ENDIF 206 8.1.4 DO
• ENDDO 207 8.1.5 REPEAT
• UNTIL 209 8.1.6 Calling Subprograms 209 8.1.7 Subprogram Structure 209 8.2 Examples 211 8.3 Representing for Loops in Flowcharts 216 8.4 Summary 218 Exercises 218 9 LED Based Projects 219 9.1 PROJECT 9.1
• Flashing LED 219 9.2 PROJECT 9.2
• Binary Counting Up LEDs 226 9.3 PROJECT 9.3
• Rotating LEDs 229 9.4 PROJECT 9.4
• Wheel of Lucky Day 231 9.5 PROJECT 9.5
• Random Flashing LEDs 239 9.6 PROJECT 9.6
• LED Dice 240 9.7 PROJECT 9.7
• Connecting more than one LED to a Port Pin 246 9.8 PROJECT 9.8
• Changing the Brightness of LEDs 250 9.9 PROJECT 9.9
• LED Candle 264 9.10 Summary 267 Exercises 267 10 7-Segment LED Display Based Projects 269 10.1 PROJECT 10.1
• Single Digit Up Counting 7-Segment LED Display 269 10.2 PROJECT 10.2
• Display a Number on 2-Digit 7-Segment LED Display 271 10.3 PROJECT 10.3
• Display Lottery Numbers on 2-Digit 7-Segment LED Display 278 10.4 PROJECT 10.4
• Event Counter Using 4-Digit 7-Segment LED Display 285 10.5 PROJECT 10.5
• External Interrupt Based Event Counter Using 4-Digit 7-Segment LED Display with Serial Driver 292 10.6 Summary 302 Exercises 303 11 Text Based LCD Projects 305 11.1 PROJECT 11.1
• Displaying Text on LCD 305 11.2 PROJECT 11.2
• Moving Text on LCD 307 11.3 PROJECT 11.3
• Counting with the LCD 310 11.4 PROJECT 11.4
• Creating Custom Fonts on the LCD 315 11.5 PROJECT 11.5
• LCD Dice 317 11.6 PROJECT 11.6
• Digital Voltmeter 325 11.7 PROJECT 11.7
• Temperature and Pressure Display 327 11.8 PROJECT 11.8
• The High/Low Game 333 11.9 Summary 344 Exercises 345 12 Graphics LCD Projects 347 12.1 PROJECT 12.1
• Creating and Displaying a Bitmap Image 347 12.2 PROJECT 12.2
• Moving Ball Animation 355 12.3 PROJECT 12.3
• GLCD Dice 357 12.4 PROJECT 12.4
• GLCD X-Y Plotting 372 12.5 PROJECT 12.5
• Plotting Temperature Variation on the GLCD 374 12.6 PROJECT 12.6
• Temperature and Relative Humidity Measurement 385 12.7 Operation of the SHT11 386 12.8 Acknowledgement 389 12.9 Summary 400 Exercises 400 13 Touch Screen Graphics LCD Projects 401 13.1 PROJECT 13.1
• Touch Screen LED ON-OFF 401 13.2 PROJECT 13.2
• LED Flashing with Variable Rate 410 13.3 Summary 418 Exercises 418 14 Using the Visual GLCD Software in GLCD Projects 419 14.1 PROJECT 14.1
• Toggle LED 420 14.2 PROJECT 14.2
• Toggle more than One LED 425 14.3 PROJECT 14.3
• Mini Electronic Organ 426 14.4 PROJECT 14.4
• Using the SmartGLCD 430 14.5 PROJECT 14.5
• Decimal to Hexadecimal Converter using the SmartGLCD 444 14.6 Summary 452 Exercises 452 15 Using the Visual TFT Software in Graphics Projects 453 15.1 PROJECT 15.1
• Countdown Timer 454 15.2 PROJECT 15.2
• Electronic Book 462 15.3 PROJECT 15.3
• Picture Show 467 15.4 Summary 472 Exercises 472 Bibliography 473 Index 475.
• (source: Nielsen Book Data)

Describing the use of displays in microcontroller based projects, the author makes extensive use of real-world, tested projects. The complete details of each project are given, including the full circuit diagram and source code. The author explains how to program microcontrollers (in C language) with LED, LCD and GLCD displays; and gives a brief theory about the operation, advantages and disadvantages of each type of display. Key features: Covers topics such as: displaying text on LCDs, scrolling text on LCDs, displaying graphics on GLCDs, simple GLCD based games, environmental monitoring using GLCDs (e.g. temperature displays)Uses C programming throughout the book - the basic principles of programming using C language and introductory information about PIC microcontroller architecture will also be providedIncludes the highly popular PIC series of microcontrollers using the medium range PIC18 family of microcontrollers in the book.Provides a detailed explanation of Visual GLCD and Visual TFT with examples.Companion website hosting program listings and data sheetsContains the extensive use of visual aids for designing LED, LCD and GLCD displays to help readers to understand the details of programming the displays: screen-shots, tables, illustrations, and figures, as well as end of chapter exercises"Using LEDs, LCDS, and GLCDs in Microcontroller Projects "is an application oriented book providing a number of design projects making it practical and accessible for electrical & electronic engineering and computer engineering senior undergraduates and postgraduates. Practising engineers designing microcontroller based devices with LED, LCD or GLCD displays will also find the book of great use.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Acknowledgments
• Contents
• Acronyms
• 1 Introduction and preview
• 2 Theory of system-level EMC
• 3 Engineering design of system-level EMC
• 4 Theory and method of system-level EM simulation
• 5 Analysis of system-level EMC prediction
• 6 Experiment and assessment of system-level EMC
• 7 Stage control of system-level EMC
• 8 Applications in engineering projects and progress in new technologies
• Index

This book introduces the state-of-the-art research progress of system-level EMC, including theories, design technologies, principles and applications in practice. The engineering design, simulation, prediction, analysis, test, stage control as well as effectiveness evaluation are discussed in detail with extensive project experiences, making the book an essential reference for researchers and industrial engineers.

• Foreword
• Introduction
• About this book
• Assumptions
• Acknowledgments
• Chapter 1: Introduction to advanced driver assistance systems and automated driving
• Chapter 2: Design approaches for automated driving systems
• Chapter 3: Different test approaches
• Chapter 4: Scenario-based testing
• Chapter 5: Simulation environment for ADAS and automated driving systems
• Chapter 6: Ground truth generation and testing neural network-based detection
• Chapter 7: Testing and qualification of perception software
• Chapter 8: Calibration of ADAS and automated driving features
• Chapter 9: Introduction to functional safety and cybersecurity testing
• Chapter 10: Verification and validation strategy Chapter 11: Acceptance criteria and maturity evaluation
• Chapter 12: Data flow and management in automated driving
• Chapter 13: Challenges and gaps in testing automated driving features
• Index
• About the author.

The day will soon come when you will be able to verbally communicate with a vehicle and instruct it to drive to a location. The car will navigate through street traffic and take you to your destination without additional instruction or effort on your part. Today, this scenario is still in the future, but the automotive industry is racing to toward the finish line to have automated driving vehicles deployed on our roads. ADAS and Automated Driving: A Practical Approach to Verification and Validation focuses on how automated driving systems (ADS) can be developed from concept to a product on the market for widescale public use. It covers practically viable approaches, methods, and techniques with examples from multiple production programs across different organizations. The author provides an overview of the various Advanced Driver Assistance Systems (ADAS) and ADS currently being developed and installed in vehicles. The technology needed for large-scale production and public use of fully autonomous vehicles is still under development, and the creation of such technology is a highly innovative area of the automotive industry. This text is a comprehensive reference for anyone interested in a career focused on the verification and validation of ADAS and ADS. The examples included in the volume provide the reader foundational knowledge and follow best and proven practices from the industry. Using the information in ADAS and Automated Driving, you can kick start your career in the field of ADAS and ADS.
(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 2.1.1 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)

• 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)

• Chapter 1. Introduction and history of connected and automated vehicles
• Chapter 2. Localization
• Chapter 3. Connectivity
• Chapter 4. Sensor and actuator hardware
• Chapter 5. Computer vision
• Chapter 6. Sensor fusion
• Chapter 7. Path planning and motion control
• Chapter 8. Verification and validation
• Chapter 9. Outlook.

The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.
(source: Nielsen Book Data)

• Chapter 1. Introduction and history of connected and automated vehicles
• Chapter 2. Localization
• Chapter 3. Connectivity
• Chapter 4. Sensor and actuator hardware
• Chapter 5. Computer vision
• Chapter 6. Sensor fusion
• Chapter 7. Path planning and motion control
• Chapter 8. Verification and validation
• Chapter 9. Outlook.

The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.

• Preface xi Introduction xiii
• 1 Kinematic Models for Mobile Robots 1 1.0 Introduction, 1 1.1 Vehicles with Front-Wheel Steering, 1 1.2 Vehicles with Differential-Drive Steering, 5 Exercises, 8 References, 9
• 2 Mobile Robot Control 11 2.0 Introduction, 11 2.1 Front-Wheel Steered Vehicle, Heading Control, 11 2.2 Front-Wheel Steered Vehicle, Speed Control, 22 2.3 Heading and Speed Control for the Differential-Drive Robot, 23 2.4 Reference Trajectory and Incremental Control, Front-Wheel Steered Robot, 26 2.5 Heading Control of Front-Wheel Steered Robot Using the Nonlinear Model, 32 2.6 Computed Control for Heading and Velocity, Front-Wheel Steered Robot, 36 2.7 Heading Control of Differential Drive Robot Using the Nonlinear Model, 38 2.8 Computed Control for Heading and Velocity, Differential-Drive Robot, 39 2.9 Steering Control Along a Path Using a Local Coordinate Frame, 41 2.10 Optimal Steering of Front-Wheel Steered Vehicle, 54 2.11 Optimal Steering of Front-Wheel Steered Vehicle, Free Final Heading Angle, 75 Exercises, 77 References, 78
• 3 Robot Attitude 79 3.0 Introduction, 79 3.1 Defi nition of Yaw, Pitch and Roll, 79 3.2 Rotation Matrix for Yaw, 80 3.3 Rotation Matrix for Pitch, 82 3.4 Rotation Matrix for Roll, 84 3.5 General Rotation Matrix, 86 3.6 Homogeneous Transformation, 88 3.7 Rotating a Vector, 92 Exercises, 93 References, 94
• 4 Robot Navigation 95 4.0 Introduction, 95 4.1 Coordinate Systems, 95 4.2 Earth-Centered Earth-Fixed Coordinate System, 96 4.3 Associated Coordinate Systems, 98 4.4 Universal Transverse Mercator (UTM) Coordinate System, 102 4.5 Global Positioning System, 104 4.6 Computing Receiver Location Using GPS, Numerical Methods, 108 4.6.1 Computing Receiver Location Using GPS via Newton's Method, 108 4.6.2 Computing Receiver Location Using GPS via Minimization of a Performance Index, 116 4.7 Array of GPS Antennas, 123 4.8 Gimbaled Inertial Navigation Systems, 126 4.9 Strap-Down Inertial Navigation Systems, 131 4.10 Dead Reckoning or Deduced Reckoning, 137 4.11 Inclinometer/Compass, 138 Exercises, 142 References, 147
• 5 Application of Kalman Filtering 149 5.0 Introduction, 149 5.1 Estimating a Fixed Quantity Using Batch Processing, 149 5.2 Estimating a Fixed Quantity Using Recursive Processing, 151 5.3 Estimating the State of a Dynamic System Recursively, 156 5.4 Estimating the State of a Nonlinear System via the Extended Kalman Filter, 169 Exercises, 185 References, 189
• 6 Remote Sensing 191 6.0 Introduction, 191 6.1 Camera Type Sensors, 191 6.2 Stereo Vision, 202 6.3 Radar Sensing: Synthetic Aperture Radar (SAR), 206 6.4 Pointing of Range Sensor at Detected Object, 212 6.5 Detection Sensor in Scanning Mode, 217 Exercises, 222 References, 223
• 7 Target Tracking Including Multiple Targets with Multiple Sensors 225 7.0 Introduction, 225 7.1 Regions of Confidence for Sensors, 225 7.2 Model of Target Location, 232 7.3 Inventory of Detected Targets, 239 Exercises, 244 References, 245
• 8 Obstacle Mapping and its Application to Robot Navigation 247 8.0 Introduction, 247 8.1 Sensors for Obstacle Detection and Geo-Registration, 248 8.2 Dead Reckoning Navigation, 249 8.3 Use of Previously Detected Obstacles for Navigation, 252 8.4 Simultaneous Corrections of Coordinates of Detected Obstacles and of the Robot, 258 Exercises, 262 References, 263
• 9 Operating a Robotic Manipulator 265 9.0 Introduction, 265 9.1 Forward Kinematic Equations, 265 9.2 Path Specifi cation in Joint Space, 269 9.3 Inverse Kinematic Equations, 271 9.4 Path Specifi cation in Cartesian Space, 276 9.5 Velocity Relationships, 284 9.6 Forces and Torques, 289 Exercises, 292 References, 293
• 10 Remote Sensing via UAVS 295 10.0 Introduction, 295 10.1 Mounting of Sensors, 295 10.2 Resolution of Sensors, 296 10.3 Precision of Vehicle Instrumentation, 297 10.4 Overall Geo-Registration Precision, 298 Exercises, 300 References, 300 Appendix A Demonstrations of Undergraduate Student Robotic Projects 301 A.0 Introduction, 301 A.1 Demonstration of the GEONAVOD Robot, 301 A.2 Demonstration of the Automatic Balancing Robotic Bicycle (ABRB), 302 See demonstration videos at http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470630213.ht ml Index 305.
• (source: Nielsen Book Data)

An important feature of this book is the particular combination of topics included. These are (1) control, (2) navigation and (3) remote sensing, all with application to mobile robots. Much of the material is readily extended to any type ground vehicle. In the controls area, robot steering is the issue. Both linear and nonlinear models are treated. Various control schemes are utilized, and through these applications the reader is introduced to methods such as: (1) Linearization and use of linear control design methods for control about a reference trajectory, (2) Use of Lyapunov stability theory for nonlinear control design, (3) Derivation of optimal control strategies via Pontryagin's maximum principle, (4) Derivation of a local coordinate system which is fundamental for the steering of vehicles along a path never before traversed. This local coordinate system has application regardless of the control design methods utilized. In the navigation area, various coordinate systems are introduced, and the transformations among them are derived. (1) The Global Positioning System (GPS) is introduced and described in significant detail. (2) Also introduced and discussed are inertial navigation systems (INS). These two methods are treated in terms of their ability to provide vehicle position as well as attitude. A preceding chapter is devoted to coordinate rotations and transformations since they play an important role in the understanding of this body of theory.
(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)

• The gathering storm
• The box
• Ships and other cyborgs
• Coming to America
• Parallel parking 1,200 feet of ship
• Longshoremen against the machine
• The largest robots on earth
• The little-known, rarely understood organizing principle of modern work
• How a management philosophy became our way of life
• Rime of the long-haul trucker
• 100 percent of everything
• How "Hitler's highway" became America's circulatory system
• The future of trucking
• What actually happens inside Amazon's warehouses
• The unbearable complexity of robotic warehousing
• Bezosism
• From Japan with love: origins of the Amazon way
• How warehouse work injures
• Amazon's employee-lite endgame
• The middle mile
• The future of delivery
• The last mile

Shortlisted for the 2021 Porchlight Business Book Awards, Current Events & Public Affairs The Wall Street Journal technology columnist reveals the fascinating story behind the misleadingly simple phrase shoppers take for granted-"Arriving Today"-in this eye-opening investigation into the new rules of online commerce, transportation, and supply chain management. We are at a tipping point in retail history. While consumers are profiting from the convenience of instant gratification, rapidly advancing technologies are transforming the way goods are transported and displacing workers in ways never before seen. In Arriving Today, Christopher Mims goes deep, far, and wide to uncover how a single product, from creation to delivery, weaves its way from a factory on the other side of the world to our doorstep. He analyzes the evolving technologies and management strategies necessary to keep the product moving to fulfill consumers' demand for "arriving today" gratification. Mims reveals a world where the only thing moving faster than goods in an Amazon warehouse is the rate at which an entire industry is being gutted and rebuilt by innovation and mass shifts in human labor practices. He goes behind the scenes to uncover the paradoxes in this shift-into the world's busiest port, the cabin of an 18-wheeler, and Amazon's automated warehouses-to explore how the promise of "arriving today" is fulfilled through a balletic dance between humans and machines. The scope of such large-scale innovation and expended energy is equal parts inspiring, enlightening, and horrifying. As he offers a glimpse of our future, Mims asks us to consider the system's vulnerability and its resilience, and who shoulders the burden, as we hurtle toward a fully automated system-and what it will mean when we are there.
(source: Nielsen Book Data)

• Introduction: RAISA in future travel-related industries
• Stanislav Ivanov and Craig Webster Section 1: Theoretical Issues of Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality 1. Conceptual Framework of the Use of Robots, Artificial Intelligence and Service Automation in Travel, Tourism, and Hospitality Companies
• Stanislav Ivanov and Craig Webster 2. Economic Fundamentals of the Use of Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality
• Stanislav Ivanov and Craig Webster 3. Self-Service Technologies in the Travel, Tourism and Hospitality Sectors - Principles and Practice
• Petranka Kelly, Jennifer Lawlor and Michael Mulvey 4. Customer Attitudes Towards Robots in Travel, Tourism and Hospitality - A Conceptual Framework
• Velina Kazandzhieva and Hristina Filipova 5. Making Sense of Robots - Consumer Discourse on Robots in Tourism and Hospitality Service Settings
• Ulrike Gretzel and Jamie Murphy 6. Chatbot Adoption in Tourism Services: A Conceptual Exploration
• Dandison C. Ukpabi, Bilal Aslam and Heikki Karjaluoto 7. The Impact of Robots, Artificial Intelligence, and Service Automation on Service Quality and Service Experience in Hospitality
• Nikola Naumov 8. Greggg - A Scalable High Performance, Low Cost Hospitality Robot
• Sam R. Thangiah, Michael Karavias, Ryan Caldwell, Matthew Wherry, Jessica Seibert, Abdullah Wahbeh, Zachariah Miller and Alexander Gessinger Section 2: Application of Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality 9. Robots, Artificial Intelligence and Service Automation in Hotels
• Georgina Lukanova and Galina Ilieva 10. Robots, Artificial Intelligence and Service Automation in Restaurants
• Katerina Berezina, Olena Cifci and Cihan Cobanoglu 11. Robots, Artificial Intelligence and Service Automation in Travel Agencies and Tourist Information Centres
• Maya Ivanova 12. Robots, Artificial Intelligence and Service Automation to the Core: Remastering Experiences at Museums
• Nuria Recuero Virto and Maria Francisca Blasco Lopez 13. The Role of Robots, Artificial Intelligence and Service Automation in Events
• Alfred Ogle and David Lamb.
• (source: Nielsen Book Data)

Using a combination of theoretical discussion and real-world case studies, this book analyses the use of robotics, artificial intelligence and services automation (RAISA) within the travel, tourism and hospitality industries. Divided into two sections, the book first concentrates on the theoretical aspects surrounding the use of RAISA in travel, tourism and hospitality. Themes explored include: economic fundamentals customer attitudes chatbot adoption service quality Following on from this, the second section concentrates on current and future use of RAISA technologies in specific subsectors of the tourism economy: hotels, restaurants, travel agencies, museums, and events. With an international scope of authorship and focus, the book is a useful reference source for scholars, students, and general readers interested in robotics, artificial intelligence, and automation technologies. Alongside this, the business insights and case studies examined in the book offer practitioners guidance on how these technologies can and will be incorporated into organizations, particularly those in the travel and tourism industry.
(source: Nielsen Book Data)

• Manifolds, tensors, and lie groups
• Group of invariance of observability
• Theory of nonlinear observability in the absence of unknown inputs
• Applications: observability analysis for systems in the absence of unknown inputs
• General concepts on nonlinear unknown input observability
• Unknown input observability for driftless systems with a single unknown input
• Unknown input observability for the general case

This book is about nonlinear observability. It provides a modern theory of observability based on a new paradigm borrowed from theoretical physics and the mathematical foundation of that paradigm. In the case of observability, this framework takes into account the group of invariance that is inherent to the concept of observability, allowing the reader to reach an intuitive derivation of significant results in the literature of control theory. The book provides a complete theory of observability and, consequently, the analytical solution of some open problems in control theory. Notably, it presents the first general analytic solution of the nonlinear unknown input observability (nonlinear UIO), a very complex open problem studied in the 1960s. Based on this solution, the book provides examples with important applications for neuroscience, including a deep study of the integration of multiple sensory cues from the visual and vestibular systems for self-motion perception. A New Theory Based on the Group of Invariance is the only book focused solely on observability. It provides readers with many applications, mostly in robotics and autonomous navigation, as well as complex examples in the framework of vision-aided inertial navigation for aerial vehicles. For these applications, it also includes all the derivations needed to separate the observable part of the system from the unobservable, an analysis with practical importance for obtaining the basic equations for implementing any estimation scheme or for achieving a closed-form solution to the problem.
(source: Nielsen Book Data)