There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Preface, Notations 1.Introduction to Time-Delay Systems I.Frequency-Domain Approach 2.Systems with Commensurate Delays 3.Systems withIncommensurate Delays 4.Robust Stability Analysis II.Time Domain Approach 5.Systems with Single Delay 6.Robust Stability Analysis 7.Systems with Multiple and Distributed Delays III.Input-Output Approach 8.Input-output stability A.Matrix Facts B.LMI and Quadratic Integral Inequalities Bibliography Index

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Stability of Time-Delay Systems

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

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies

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A Survey of Recent Results in Networked Control Systems

1. Introduction. 2. Linear Algebra. 3. Linear Systems. 4. H2 and Ha Spaces. 5. Internal Stability. 6. Performance Specifications and Limitations. 7. Balanced Model Reduction. 8. Uncertainty and Robustness. 9. Linear Fractional Transformation. 10. m and m- Synthesis. 11. Controller Parameterization. 12. Algebraic Riccati Equations. 13. H2 Optimal Control. 14. Ha Control. 15. Controller Reduction. 16. Ha Loop Shaping. 17. Gap Metric and ...u- Gap Metric. 18. Miscellaneous Topics. Bibliography. Index.

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Essentials of Robust Control

This paper considers the special classes of nonnegative and Metzlerian systems and provide procedures to compute real end complex stability radii. >

Real and complex stability radii for nonnegative and Metzlerian systems

This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.

A Level Set Based Method for Land Masking in Ship Detection Using SAR Images

The resolution of a synthetic aperture radar (SAR) image, in range and azimuth, is limited by the transmitted signal bandwidth and the synthetic aperture length, respectively. Bandwidth extrapolation algorithm is one of the various superresolution techniques for improving the resolution. The frequency AR model is used in the paper to expand the effective bandwidth of the signal based on the bandwidth extrapolation together with linear prediction algorithm. The performance estimation function, namely the prediction error as a scaling power-law function of the linear error prediction filter length, is derived through analyzing large numbers of computer simulation experiments. It is of considerable importance to determine the linear error prediction filter length for optimizing the bandwidth extrapolation parameters. It is demonstrated by the theoretical analysis and simulation results that the bandwidth extrapolation method is feasible and effective for expanding the bandwidth to obtain higher resolution SAR image by applying the method to the range and azimuth directions, respectively.

High resolution radar imaging using bandwidth extrapolation

The problem of computing a nominal model for a finite collection of linear plant models is considered. It is shown that, when the metric is derived from the l/sub 1/-norm, computing the nominal model reduces to a standard linear programming problem. An aircraft engine example is presented to illustrate the algorithm. >

Computing nominal models for uncertain plants

With the development and extensive utility of synthetic aperture radar(SAR), SAR systems are required not only to achieve the large-scale imaging but also to continuously stare at focus areas. Accordingly, SAR based on highly elliptical orbit provides a new way for solving these observation problems. This paper presents a method for spaceborne SAR echo simulation in a highly elliptical orbit based on the antenna beam steering control. Due to the effects of the high eccentricity and the apogee of the highly elliptical orbit, squint angle, looking-down angles of antenna beam and other factors are considered to design the beam control of the antenna, so that the satellite can keep illuminating on the earth surface to acquire echo for the whole cycle. In this paper, the proposed method of antenna beam steering implements the SAR echo simulation in highly elliptical orbit. The feasibility of the proposed method was analyzed by the steering control angles' variation characteristics and verified by simulating the lattice targets echo signal and imaging.

Jie Chen

Papers

Real and complex stability radii for nonnegative and Metzlerian systems

A Level Set Based Method for Land Masking in Ship Detection Using SAR Images

High resolution radar imaging using bandwidth extrapolation

Computing nominal models for uncertain plants

An Antenna Beam Steering Strategy for SAR Echo Simulation in Highly Elliptical Orbit