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New Methods of Parameter and State Estimation: A Comprehensive Guide

Eric Nelson

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Published in Dynamic Systems Models: New Methods Of Parameter And State Estimation

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Dynamic Systems Models: New Methods of Parameter and State Estimation

5 out of 5

Language	:	English
File size	:	10651 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	371 pages

In the realm of engineering, accurate estimation of parameters and states is crucial for optimal system performance and control. This book, "New Methods of Parameter and State Estimation," serves as an authoritative guide, providing a comprehensive overview of cutting-edge techniques and their applications in various domains.

Master the Art of System Identification

The first section of the book delves into the fundamentals of system identification, a critical step in model building for control and estimation. You will learn about:

Dynamic models and their representation
Data acquisition and preprocessing techniques
Parameter estimation methods, including least squares, maximum likelihood, and Bayesian approaches

With a solid foundation in system identification, you will be equipped to accurately capture the behavior of complex systems and predict their future states.

Adaptive Control: Unleash the Power of Self-Tuning

The second section explores the principles of adaptive control, where controllers adjust their parameters in real-time based on system behavior. Key topics covered include:

Model reference adaptive control (MRAC)
Self-tuning regulators (STRs)
Gain-scheduled control

By mastering adaptive control techniques, you will empower systems with the ability to handle uncertainties, nonlinearities, and time-varying conditions, ensuring optimal performance even in challenging environments.

Kalman Filtering: A Cornerstone of Modern Estimation

The final section of the book focuses on the renowned Kalman filter, a powerful estimation technique widely used in navigation, robotics, and signal processing. You will delve into:

State space models and their properties
Recursive estimation using the Kalman filter
Extended Kalman filter (EKF) for nonlinear systems

With a deep understanding of Kalman filtering, you will be able to accurately estimate the states of dynamic systems even in the presence of noise and uncertainties.

Real-World Applications: Bridging Theory and Practice

Throughout the book, numerous real-world examples and case studies illustrate the practical applications of parameter and state estimation methods. These include:

Control of robotic manipulators
Navigation systems for autonomous vehicles
Monitoring and fault detection in industrial processes

By connecting theoretical concepts to real-world challenges, you will gain a practical understanding of how these methods enhance system performance and safety.

Key Features:

Comprehensive coverage of parameter and state estimation techniques
Clear and concise explanations with mathematical rigor
Up-to-date information on the latest advancements
Abundant examples and case studies for practical insights
Suitable for graduate students, researchers, and practicing engineers

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"New Methods of Parameter and State Estimation" is an indispensable resource for anyone seeking to enhance their expertise in this critical field. Whether you are a graduate student, researcher, or practicing engineer, this book will provide you with the knowledge and tools necessary to excel in the design and implementation of state-of-the-art estimation systems.

Dynamic Systems Models: New Methods of Parameter and State Estimation

5 out of 5