There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material. The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the engineering properties of the regulator. Topics include degree of stability, phase and gain margin, tolerance of time delay, effect of nonlinearities, asymptotic properties, and various sensitivity problems. The third section explores state estimation and robust controller design using state-estimate feedback. Numerous examples emphasize the issues related to consistent and accurate system design. Key topics include loop-recovery techniques, frequency shaping, and controller reduction, for both scalar and multivariable systems. Self-contained appendixes cover matrix theory, linear systems, the Pontryagin minimum principle, Lyapunov stability, and the Riccati equation. Newly added to this Dover edition is a complete solutions manual for the problems appearing at the conclusion of each section.

Optimal Control: Linear Quadratic Methods

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

Linear Robust Control

A New Approach to the H ∞ Design of Optimal Digital Linear Filters

Introduction to optimal robust control scalar LQG optimal control problem and solution scalar H infinity optimal control problem and solution multivariable LQG optimal control problems and solution multivariable H infinity optimal control problem and solution robust control design procedures H2 optimal filtering smoothing and prediction problems H infinity optimal filtering smoothing and prediction problems industrial applications of H infinity optimal control.

Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems

The solution of l/sub 2/ (minimum variance) and H/sub infinity / estimation problems is considered using a polynomial systems approach. The results for the l/sub 2/ filtering problem, which corresponds with Wiener or Kalman filtering/prediction, are first presented in polynomial matrix form. Attention then turns to the solution of the H/sub infinity / estimation problem for scalar systems. Numerous examples are presented to illustrate the computational procedures. The two types of estimator are appropriate to very different estimation problems and the new H/sub infinity / devices should be valuable in certain application areas. >

Solution of the H/sub infinity / optimal linear filtering problem for discrete-time systems

Brief Controller performance benchmarking and tuning using generalised minimum variance control

A novel adaptive filtering technique is described for a class of systems with unknown disturbances. The estimator includes both a self-tuning filter and a Kalman filter. The state estimates are employed in a closed-loop feedback control scheme which is designed via the usual linear quadratic approach. The approach was developed for application to the dynamic ship positioning control problem and has the advantage that existing nonadaptive Kalman filtering systems may be easily modified to include the self-tuning feature.

Michael J. Grimble

Papers

A New Approach to the H ∞ Design of Optimal Digital Linear Filters

Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems

Solution of the H/sub infinity / optimal linear filtering problem for discrete-time systems

Brief Controller performance benchmarking and tuning using generalised minimum variance control

Dynamic ship positioning using a self-tuning Kalman filter