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Observers are digital algorithms that combine sensor outputs with knowledge of the system to provide results superior to traditional structures, which rely wholly on sensors. Observers have been used in selected industries for years, but most books explain them with complex mathematics. Observers in Control Systems uses intuitive discussion, software experiments, and supporting analysis to explain the advantages and disadvantages of observers. If you are working in controls and want to improve your control systems, observers could be the technology you need and this book will give you a clear, thorough explanation of how they work and how to use them.
Control systems and devices have become the most essential part of nearly all mechanical systems, machines, devices and manufacturing systems throughout the world. Increasingly the efficiency of production, the reliability of output and increased energy savings are a direct result of the quality and deployment of the control system. A modern and essential tool within the engineer's kit is the Observer which helps improve the performance and reduce the cost of these systems.
George Ellis is the author of the highly successful Control System Design Guide (Second Edition). Unlike most controls books, which are written by control theorists and academics, Ellis is a leading engineer, designer, author and lecturer working in industry directly with the users of industrial motion control systems. Observers in Control Systems is written for all professional engineers and is designed to be utilized without an in-depth background in control theory. This is a "real-world" book which will demonstrate how observers work and how they can improve your control system. It also shows how observers operate when conditions are not ideal and teaches the reader how to quickly tune an observer in a working system.
Software Available online: A free updated and enhanced version of the author's popular Visual ModelQ allows the reader to practice the concepts with Visual ModelQ models on a PC. Based on a virtual laboratory, all key topics are demonstrated with more than twenty control system models. The models are written in Visual ModelQ ,and are available on the Internet to every reader with a PC.
- Teaches observers and Kalman filters from an intuitive perspective
- Explains how to reduce control system susceptibility to noise
- Shows how to design an adaptive controller based on estimating parameter variation using observers
- Shows how to improve a control system's ability to reject disturbances
- Key topics are demonstrated with PC-based models of control systems. The models are written in both MatLab® and ModelQ; models are available free of charge
Acknowledgements Safety Chapter 1 Control Systems and the Role of Observers 1.1 Overview 1.2 Preview of Observers 1.3 Summary of the Book Chapter 2 Control-System Background 2.1 Control-System Structures 2.2 Goals of Control Systems 2.3 Visual ModelQ Simulation Environment 2.4 Software Experiments: Introduction to Visual ModelQ 2.5 Exercises Chapter 3 Review of the Frequency Domain 3.1 Overview of the s-Domain 3.2 Overview of the z-Domain 3.3 The Open-Loop Method 3.4 A Zone-Based Tuning Procedure 3.5 Exercises Chapter 4 The Luenberger Observer: Correcting Sensor Problems 4.1 What Is a Luenberger Observer? 4.2 Experiments 4A-4C: Enhancing Stability with an Observer 4.3 Predictor-Corrector Form of the Luenberger Observer 4.4 Filer Form of the Luenberger Observer 4.5 Designing a Luenberger Observer 4.6 Introduction to Tuning an Observer Compensator 4.7 Exercises Chapter 5 The Luenberger Observer and Model Inaccuracy 5.1 Model Inaccuracy 5.2 Effects of Model Inaccuracy 5.3 Experimental Evaluation 5.4 Exercises Chapter 6 The Luenberger Observer and Disturbances 1 6.1 Disturbances 6.2 Disturbance Response 6.3 Disturbance Decoupling 6.4 Exercises Chapter 7 Noise in the Luenberger Observer 7.1 Noise in Control Systems 7.2 Sensor Noise and the Luenberger Observer 7.3 Noise Sensitivity when Using Disturbance Decoupling 7.4 Reducing Noise Susceptibility in Observer-Based Systems 7.5 Exercises Chapter 8 Using the Luenberger Observer in Motion Control 8.1 The Luenberger Observers in Motion Systems 8.2 Observing Velocity to Reduce Phase Lag 8.3 Using Observers to Improve Disturbance Response 8.4 Exercises References Appendix A Observer-Based Resolver Conversion in Industrial Servo Systems Introduction Resolvers and Traditional RDC Converting the Signal Observers Applying the Observer to RDC Advantages of Observer-Based Conversion Conclusion References Appendix B Cures for Mechanical Resonance in Industrial Servo Systems Introduction Two-Part Transfer Function Low-Frequency Resonance Velocity Control Law Methods of Correction Applied to Low-Frequency Resonance Conclusion Acknowledgements References Appendix C European Symbols for Block Diagrams Part I: Linear Functions Part II: Nonlinear Functions Appendix D Development of the Bilinear Transformation Bilinear Transformation Prewarping Factoring Polynomials Phase Advancing Appendix E Solutions of Exercises Index
- No. of pages:
- © Academic Press 2002
- 10th October 2002
- Academic Press
- Hardcover ISBN:
- Paperback ISBN:
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George Ellis has worked in product development for 35 years. He first experienced the concept of continuous improvement two decades ago through the Danaher Corporation, one of the world’s foremost lean thinking companies. Danaher transformed itself in the 1980s, modeling its Danaher Business System (DBS) on the Toyota Production System. Ellis has had numerous leadership roles at Danaher, including Vice President of Global Engineering for X-Rite from 2015 to 2018. In 2019, Ellis joined Envista Holdings Corporation, a new spin-off from Danaher for the dental industry, as Vice President of Innovation. There he spends every day immersed in lean knowledge work, deploying, improving, and sustaining new product development workflows in EBS, Envista’s brand of lean knowledge. He also wrote Project Management for Product Development, Control System Design Guide (4th edition), and Observers in Control Systems, all from Elsevier.
Vice President Innovation, Envista Business System Office Envista Holdings Corporation, Brea, CA, United States
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