[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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

/pdf/stability-of-time-delay-systems-5d2qjgdknf.pdf

Stability of Time-Delay Systems

关于Semigroups of Linear Operators and Applications to Partial Differential Equations的两个注解

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

Bibliographical review on reconfigurable fault-tolerant control systems

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Preface. 1. Introduction. 2. Systems Theory. 3. Adaptive Parameter Estimation. 4. Adaptive State Feedback Control. 5. Continuous-Time Model Reference Adaptive Control. 6. Discrete-Time Model Reference Adaptive Control. 7. Indirect Adaptive Control. 8. A Comparison Study. 9. Multivariable Adaptive Control. 10. Adaptive Control of Systems with Nonlinearities. Bibliography. Index.

Adaptive Control Design and Analysis

We develop Feedback Control real-time Scheduling (FCS) as a unified framework to provide Quality of Service (QoS) guarantees in unpredictable environments (such as e-business servers on the Internet). FCS includes four major components. First, novel scheduling architectures provide performance control to a new category of QoS critical systems that cannot be addressed by traditional open loop scheduling paradigms. Second, we derive dynamic models for computing systems for the purpose of performance control. These models provide a theoretical foundation for adaptive performance control. Third, we apply established control methodology to design scheduling algorithms with proven performance guarantees, which is in contrast with existing heuristics-based solutions relying on laborious design/tuning/testing iterations. Fourth, a set of control-based performance specifications characterizes the efficiency, accuracy, and robustness of QoS guarantees. 
The generality and strength of FCS are demonstrated by its instantiations in three important applications with significantly different characteristics. First, we develop real-time CPU scheduling algorithms that guarantees low deadline miss ratios in systems where task execution times may deviate from estimations at run-time. We solve the saturation problems of real-time CPU scheduling systems with a novel integrated control structure. Second, we develop an adaptive web server architecture to provide relative and absolute delay guarantees to different service classes with unpredictable workloads. The adaptive architecture has been implemented by modifying an Apache web server. Evaluation experiments on a testbed of networked Linux PC's demonstrate that our server provides robust relative/absolute delay guarantees despite of instantaneous changes in the user population. Third, we develop a data migration executor for networked storage systems that migrate data on-line while guaranteeing specified I/O throughput of concurrent applications.

https://www.cse.wustl.edu/~lu/papers/thesis.pdf

Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms*

For a system with hysteresis, the authors present a parameterized hysteresis model and develop a hysteresis inverse. The authors then design adaptive controllers with an adaptive hysteresis inverse for plants with unknown hysteresis. A new adaptive controller structure is introduced which is capable of achieving a linear parameterization and a linear error model in the presence of a hysteresis nonlinearity. A robust adaptive law is used to update the controller parameters and hysteresis inverse parameters, which ensures the global boundedness of the closed-loop signals for a wide class of of hysteresis models. Simulations show that the use of the adaptive hysteresis inverse leads to major improvements of system performance. >

Adaptive control of plants with unknown hystereses

From the Publisher:
An in-depth examination of intelligent approaches to increasing the accuracy of a variety of system components. Utilizing a unified, adaptive, inverse approach, the book offers electrical, mechanical, chemical, aeronautical and computer engineers methods for controlling many of the "hard" nonlinearities of frequently-employed control systems such as dead-zone, backlash and hysteresis. Discusses such nonlinearities at both the input and output points of a linear part and within both continuous time designs and discrete time designs.

Adaptive Control of Systems with Actuator and Sensor Nonlinearities

Direct adaptive-state feedback control schemes are developed for linear time-invariant plants with actuator failures with characterizations that some of the plant inputs are stuck at some fixed or varying values which cannot be influenced by control action. Conditions and controller structures for achieving plant-model state matching in the presence of actuator failures are derived. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator-failure parameters are unknown. Closed-loop stability and asymptotic-state tracking are ensured. Simulation results show that desired system performance is achieved with the developed adaptive actuator failure compensation control designs.

Gang Tao

Papers

Adaptive Control Design and Analysis

Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms*

Adaptive control of plants with unknown hystereses

Adaptive Control of Systems with Actuator and Sensor Nonlinearities

Adaptive state feedback and tracking control of systems with actuator failures