Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Trends in Microgrid Control

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

This paper describes a robust control experiment, currently in progress at Harris Corporation A detailed physical description of the experiment's physical apparatus is presented In addition, the paper gives a brief description of the approaches to be used for robust control system design and analysis

A Robust Control Experiment using an Optical Structure Prototype

Feedback control of aerodynamic ow separation is a formidable challenge due to the nonlinear system dynamics and the lack of a closed-form model that describes these dynamics. Recently, adaptive control approaches have been employed to meet this challenge. However, the performance and robustness achieved using these algorithms is limited due to the fact that the sensors in ow control systems are not used to estimate lift and drag, the quantities of interest. This paper derives a physics-based performance function that is based on estimating the ratio lift/drag and correlates closely with simulated and experimental aerodynamic performance. The developments are based on a NACA 0025 airfoil at a range of near-transitional Reynolds numbers and a range of pre-stall and post-stall angles of attack. The simulation and experimental results consider the use of four pressure measurements. Both the simulated and experimental results indicate that the resulting performance function has properties favorable to closed-loop control. The performance function developed here may be applied to any feedback control framework.

Development of an Improved Performance Function for the Control of Flow Separation

Constraints on the variances of the system inputs and selected system outputs have been shown to have practical applications. One way of designing control laws that achieve these objectives is by designing an LQG (or optimal /spl Hscr//sub 2/) controller with an appropriate choice of the input and output weighting matrices. However, one of the well known limitations of practical implementation of an LQG controller is that it is of the same order as the plant model. The two primary methods of designing reduced order controllers are the truncation (or indirect) methods and parameter optimization (or direct methods). These two methods are often used simultaneously since the truncated controllers are often used to initialize the direct methods. This paper considers the design of /spl Hscr//sub 2/ optimal reduced order controllers to meet a set of variance constraints. As with full order control, this problem involves the proper choice of the weighting matrices in the cost function. Although the behavior of the variances with weight variations in reduced order control design are experimentally seen to be more erratic than in optimal full order design, a fuzzy algorithm previously developed for the full order variance constrained problem is shown to be applicable to the reduced order variance constrained problem. Three reduced order schemes are developed and compared. The first two schemes involve direct reduced order design while the third scheme involves reduced order design using modified balanced truncation. The first two schemes differ only in how they are initialized with the first approach using the weights from the full order variance constrained problem and the second approach using unity weights for initialization. The three schemes are compared using numerical experiments. The results clearly demonstrate the feasibility of reduced order variance constrained control design.

A fuzzy approach to reduced order variance constrained control design

In real world motion planning tasks, autonomous vehicles can easily deviate away from their planned trajectories due to external disturbances, uncertain wheel/leg-terrain interaction, and other errors in the model used for planning. A possible solution to this problem consists in the continuous usage of replanning strategies. However, replanning is in general computationally intensive and its use should be minimized when possible. In this paper, a new methodology for robust trajectory tracking is proposed. The method generates, via sampling, correcting control inputs to drive the vehicle back to the desired trajectory. Due to the use of sampling, the methodology easily incorporates nonlinear planning models and integrates seamlessly with sampling-based motion planners. The paper presents simulation and preliminary experimental results showing the efficacy of the proposed approach and thus its potential application to motion planning tasks with real-time constraints.

Robust sampling-based trajectory tracking for autonomous vehicles

An important part of feedback control involves analyzing uncertain systems for robust stability and performance. Many robustness theories consider only stability issues and ignore performance. Most of the performance robustness results that do exist will not always yield finite performance bounds for the case of closed-loop systems consisting of uncertain positive real plants controlled by strictly positive real compensators. These results are obviously conservative since this class of systems is unconditionally stable. This paper uses majorant analysis to develop tests that yield finite performance bounds for the above case. The results are specialized to the case of static, decentralized colocated rate feedback and dynamic colocated rate feedback.

Emmanuel G. Collins

Papers

A Robust Control Experiment using an Optical Structure Prototype

Development of an Improved Performance Function for the Control of Flow Separation

A fuzzy approach to reduced order variance constrained control design

Robust sampling-based trajectory tracking for autonomous vehicles

Frequency domain performance bounds for uncertain positive real plants controlled by strictly positive real compensators