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

Electrical energy storage technologies for stationary applications are reviewed. Particular attention is paid to pumped hydroelectric storage, compressed air energy storage, battery, flow battery, fuel cell, solar fuel, superconducting magnetic energy storage, flywheel, capacitor/supercapacitor, and thermal energy storage. Comparison is made among these technologies in terms of technical characteristics, applications and deployment status.

http://promexico.me/Rubenius/WhitePapers/sdarticle%20(1).pdf

Progress in electrical energy storage system: A critical review

Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The controller updates considered here are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation is considered first and then its distributed counterpart, in which agents require knowledge only of their neighbors' states for the controller implementation. The results are then extended to a self-triggered setup, where each agent computes its next update time at the previous one, without having to keep track of the state error that triggers the actuation between two consecutive update instants. The results are illustrated through simulation examples.

/pdf/distributed-event-triggered-control-for-multi-agent-systems-4p46fy7wt3.pdf

Distributed Event-Triggered Control for Multi-Agent Systems

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

Most research in control engineering considers periodic or time-triggered control systems with equidistant sample intervals. However, practical cases abound in which it is of interest to consider event-driven control in which the sampling is event-triggered. Although there are various benefits of using event-driven control like reducing resource utilization (e.g., processor and communication load), their application in practice is hampered by the lack of a system theory for event-driven control systems. To provide a first step in developing an event-driven system theory, this paper considers an event-driven control scheme for perturbed linear systems. The event-driven control scheme triggers the control update only when the (tracking or stabilization) error is large. In this manner, the average processor and/or communication load can be reduced significantly. The analysis in this paper is aimed at the control performance in terms of practical stability (ultimate boundedness). Several examples illustrate t...

/pdf/analysis-of-event-driven-controllers-for-linear-systems-2vtpdrc2r9.pdf

Analysis of event-driven controllers for linear systems

In the near future, a significant increase in electric power consumption in vehicles is expected. To limit the associated increase in fuel consumption and exhaust emissions, smart strategies for the generation, storage/retrieval, distribution, and consumption of electric power will be used. Inspired by the research on energy management for hybrid electric vehicles (HEVs), this paper presents an extensive study on controlling the vehicular electric power system to reduce the fuel use and emissions, by generating and storing electrical energy only at the most suitable moments. For this purpose, both off-line optimization methods using knowledge of the driving pattern and on-line implementable ones are developed and tested in a simulation environment. Results show a reduction in fuel use of 2%, even without a prediction of the driving cycle being used. Simultaneously, even larger reductions of the emissions are obtained. The strategies can also be applied to a mild HEV with an integrated starter alternator (ISA), without modifications, or to other types of HEVs with slight changes in the formulation.

http://www.mate.tue.nl/mate/pdfs/5064.pdf

Energy management strategies for vehicular electric power systems

Hybrid electric vehicles (HEVs) are equipped with multiple power sources for improving the efficiency and performance of their power supply system. An energy management (EM) strategy is needed to optimize the internal power flows and satisfy the driver's power demand. To achieve maximum fuel profits from EM, many solution methods have been presented. Optimal solution methods are typically not feasible in an online application due to their computational demand and their need to have a priori knowledge about future vehicle power demand. In this paper, an online EM strategy is presented with the ability to mimic the optimal solution but without using a priori road information. Rather than solving a mathematical optimization problem, the methodology concentrates on a physical explanation about when to produce, consume, and store electric power. This immediately reveals the vehicle characteristics that are important for EM. It is shown that this concept applies to many existing HEVs as well as possible future vehicle configurations. Since the method only focuses on typical vehicle characteristics, the underlying algorithm requires minor computational effort and can be executed in real time. Clear directions for online implementation are given in this paper. A parallel HEV with a 5-kW integrated starter/generator (ISG) is selected to demonstrate the performance of the EM strategy. Simulation results indicate that the proposed EM strategy exhibits similar behavior as an optimal solution obtained from dynamic programming. Profits in fuel economy primarily arise from engine stop/start and energy obtained during regenerative braking. This latter energy is preferably used for pure electric propulsion where the internal combustion engine is switched off.

Online Energy Management for Hybrid Electric Vehicles

http://www.dct.tue.nl/New/Heemels/HeeGor_99_CEP.pdf

Asynchronous measurement and control: a case study on motor synchronization

Introduction Modeling Introduction Model Building Approaches Mathematical Models Index of a DAE Examples Bond Graphs Comparison Circuits, Bond Graphs, and Block Diagrams Examples Summary References Problems Black-Box Model Representations Introduction Discrete-Time Systems Impulse Response and Transfer Function Disturbances Time-Domain Representations Frequency-Domain Representations Examples: Calculation of Stochastic Quantities Summary References Problems Nonparametric Identification Time-Domain Identification by Correlation Analysis Frequency Response Analysis Frequency Response Analysis by the Correlation Method Fourier Analysis: The Empirical Transfer Function Estimate Persistence of Excitation Summary References Problems Parametric Identification Prediction Error Methods Prediction Models Least Squares Method Analysis of the Linear LS Estimate Convergence and Consistency Approximate Identification Summary References Problems Identification in Practice Experiment Design Model Structure Selection Model Validation Summary References Problems Simulation Introduction Simulation Tools Numerical Solution of Differential Equations Conversion from Parallel to Series Solving Stiff ODEs and DAEs Applications Summary References Problems Optimization Introduction Convergence Determination of Step Size si Determination of Search Direction di Least Squares Problem Optimization with Constraints Summary References Problems Literature Index

P.P.J. van den Bosch

Papers

Analysis of event-driven controllers for linear systems

Energy management strategies for vehicular electric power systems

Online Energy Management for Hybrid Electric Vehicles

Asynchronous measurement and control: a case study on motor synchronization

Modeling, identification, and simulation of dynamical systems