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E-raamat: Nonlinear and Optimal Control Theory: Lectures given at the C.I.M.E. Summer School held in Cetraro, Italy, June 19-29, 2004

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  • Sari: C.I.M.E. Foundation Subseries 1932
  • Ilmumisaeg: 24-Jun-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783540776536
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  • Formaat: PDF+DRM
  • Sari: C.I.M.E. Foundation Subseries 1932
  • Ilmumisaeg: 24-Jun-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783540776536
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Mathematical Control Theory is a branch of Mathematics having as one of its main aims the establishment of a sound mathematical foundation for the c- trol techniques employed in several di erent ?elds of applications, including engineering,economy,biologyandsoforth. Thesystemsarisingfromthese- plied Sciences are modeled using di erent types of mathematical formalism, primarily involving Ordinary Di erential Equations, or Partial Di erential Equations or Functional Di erential Equations. These equations depend on oneormoreparameters thatcanbevaried,andthusconstitute thecontrol - pect of the problem. The parameters are to be chosen soas to obtain a desired behavior for the system. From the many di erent problems arising in Control Theory, the C. I. M. E. school focused on some aspects of the control and op- mization ofnonlinear, notnecessarilysmooth, dynamical systems. Two points of view were presented: Geometric Control Theory and Nonlinear Control Theory. The C. I. M. E. session was arranged in ?ve six-hours courses delivered by Professors A. A. Agrachev (SISSA-ISAS, Trieste and Steklov Mathematical Institute, Moscow), A. S. Morse (Yale University, USA), E. D. Sontag (Rutgers University, NJ, USA), H. J. Sussmann (Rutgers University, NJ, USA) and V. I. Utkin (Ohio State University Columbus, OH, USA). We now brie y describe the presentations. Agrachev's contribution began with the investigation of second order - formation in smooth optimal control problems as a means of explaining the variational and dynamical nature of powerful concepts and results such as Jacobi ?elds, Morse's index formula, Levi-Civita connection, Riemannian c- vature.
Geometry of Optimal Control Problems and Hamiltonian Systems
1(61)
A. A. Agrachev
Lagrange Multipliers' Geometry
1(24)
Smooth Optimal Control Problems
1(3)
Lagrange Multipliers
4(2)
Extremals
6(1)
Hamiltonian System
7(3)
Second Order Information
10(4)
Maslov Index
14(8)
Regular Extremals
22(3)
Geometry of Jacobi Curves
25(36)
Jacobi Curves
25(1)
The Cross-Ratio
26(2)
Coordinate Setting
28(1)
Curves in the Grassmannian
29(1)
The Curvature
30(3)
Structural Equations
33(2)
Canonical Connection
35(3)
Coordinate Presentation
38(1)
Affine Foliations
39(2)
Symplectic Setting
41(3)
Monotonicity
44(5)
Comparison Theorem
49(2)
Reduction
51(2)
Hyperbolicity
53(5)
References
58(3)
Lecture Notes on Logically Switched Dynamical Systems
61(102)
A. S. Morse
The Quintessential Switched Dynamical System Problem
62(5)
Dwell-Time Switching
62(3)
Switching Between Stabilizing Controllers
65(1)
Switching Between Graphs
66(1)
Switching Controls with Memoryless Logics
67(1)
Introduction
67(1)
The Problem
67(1)
The Solution
67(1)
Analysis
68(1)
Collaborations
68(1)
The Curse of the Continuum
69(7)
Process Model Class
69(4)
Controller Covering Problem
73(1)
A Natural Approach
74(1)
A Different Approach
75(1)
Which Metric?
75(1)
Construction of a Control Cover
76(1)
Supervisory Control
76(34)
The System
77(9)
Slow Switching
86(1)
Analysis
87(15)
Analysis of the Dwell Time Switching Logic
102(8)
Flocking
110(53)
Leaderless Coordination
111(31)
Symmetric Neighbor Relations
142(6)
Measurement Delays
148(7)
Asynchronous Flocking
155(3)
Leader Following
158(1)
References
159(4)
Input to State Stability: Basic Concepts and Results
163(58)
E. D. Sontag
Introduction
163(1)
ISS as a Notion of Stability of Nonlinear I/O Systems
163(13)
Desirable Properties
164(1)
Merging Two Different Views of Stability
165(1)
Technical Assumptions
166(1)
Comparison Function Formalism
166(1)
Global Asymptotic Stability
167(1)
O-GAS Does Not Guarantee Good Behavior with Respect to Inputs
168(1)
Gains for Linear Systems
168(1)
Nonlinear Coordinate Changes
169(2)
Input-to-State Stability
171(1)
Linear Case, for Comparison
172(1)
Feedback Redesign
173(1)
A Feedback Redesign Theorem for Actuator Disturbances
174(2)
Equivalences for ISS
176(4)
Nonlinear Superposition Principle
176(1)
Robust Stability
177(1)
Dissipation
178(2)
Using ``Energy'' Estimates Instead of Amplitudes
180(1)
Cascade Interconnections
180(3)
An Example of Stabilization Using the ISS Cascade Approach
182(1)
Integral Input-to-State Stability
183(7)
Other Mixed Notions
183(1)
Dissipation Characterization of iISS
184(1)
Superposition Principles for iISS
185(1)
Cascades Involving iISS Systems
186(2)
An iISS Example
188(2)
Input to State Stability with Respect to Input Derivatives
190(2)
Cascades Involving the DkISS Property
190(1)
Dissipation Characterization of DkISS
191(1)
Superposition Principle for DkISS
191(1)
A Counter-Example Showing that D1ISS ≠ ISS
192(1)
Input-to-Output Stability
192(2)
Detectability and Observability Notions
194(7)
Detectability
195(1)
Dualizing ISS to OSS and IOSS
196(1)
Lyapunov-Like Characterization of IOSS
196(1)
Superposition Principles for IOSS
197(1)
Norm-Estimators
197(1)
A Remark on Observers and Incremental IOSS
198(1)
Variations of IOSS
199(1)
Norm-Observability
200(1)
The Fundamental Relationship Among ISS, IOS, and IOSS
201(1)
Systems with Separate Error and Measurement Outputs
202(3)
Input-Measurement-to-Error Stability
202(1)
Review: Viscosity Subdifferentials
203(1)
RES-Lyapunov Functions
204(1)
Output to Input Stability and Minimum-Phase
205(1)
Response to Constant and Periodic Inputs
205(1)
A Remark Concerning ISS and H∞ Gains
206(1)
Two Sample Applications
207(2)
Additional Discussion and References
209(12)
References
213(8)
Generalized Differentials, Variational Generators, and the Maximum Principle with State Constraints
221(68)
H. J. Sussmann
Introduction
221(1)
Preliminaries and Background
222(8)
Review of Some Notational Conventions and Definitions
222(6)
Generalized Jacobians, Derivate Containers, and Michel-Penot Subdifferentials
228(1)
Finitely Additive Measures
229(1)
Cellina Continuously Approximable Maps
230(13)
Definition and Elementary Properties
231(3)
Fixed Point Theorems for CCA Maps
234(9)
GDQs and AGDQs
243(24)
The Basic Definitions
244(2)
Properties of GDQs and AGDQs
246(9)
The Directional Open Mapping and Transversality Properties
255(12)
Variational Generators
267(10)
Linearization Error and Weak GDQs
267(2)
GDQ Variational Generators
269(1)
Examples of Variational Generators
270(7)
Discontinuous Vector Fields
277(4)
Co-Integrably Bounded Integrally Continuous Maps
277(3)
Points of Approximate Continuity
280(1)
The Maximum Principle
281(8)
References
285(4)
Sliding Mode Control: Mathematical Tools, Design and Applications
289(60)
V. I. Utkin
Introduction
289(1)
Examples of Dynamic Systems with Sliding Modes
289(7)
VSS in Canonical Space
296(7)
Control of Free Motion
298(2)
Disturbance Rejection
300(1)
Comments for VSS in Canonical Space
301(1)
Preliminary Mathematical Remark
302(1)
Sliding Modes in Arbitrary State Spaces: Problem Statements
303(2)
Sliding Mode Equations: Equivalent Control Method
305(8)
Problem Statement
305(1)
Regularization
306(5)
Boundary Layer Regularization
311(2)
Sliding Mode Existence Conditions
313(3)
Design Principles
316(11)
Decoupling and Invariance
316(2)
Regular Form
318(2)
Block Control Principle
320(2)
Enforcing Sliding Modes
322(3)
Unit Control
325(2)
The Chattering Problem
327(3)
Discrete-Time Systems
330(6)
Discrete-Time Sliding Mode Concept
331(2)
Linear Discrete-Time Systems with Known Parameters
333(2)
Linear Discrete-Time Systems with Unknown Parameters
335(1)
Infinite-Dimensional Systems
336(4)
Distributed Control of Heat Process
337(1)
Flexible mechanical System
338(2)
Control of Induction Motor
340(9)
References
344(5)
List of Participants 349