Cover image for Nonlinear and optimal control theory : lectures given at the C.I.M.E. summer school held in Cetraro, Italy, June 19-29, 2004
Title:
Nonlinear and optimal control theory : lectures given at the C.I.M.E. summer school held in Cetraro, Italy, June 19-29, 2004
Series:
Lecture notes in mathematics, 1932
Publication Information:
New York, NY. : Springer, 2008
Physical Description:
xiii, 351 p. : ill. ; 24 cm.
ISBN:
9783540776444

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30000010178992 QA402.35 N64 2008 Open Access Book Book
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Summary

Summary

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.


Table of Contents

A.A. AgrachevA.S. MorseE.D. SontagH.J. SussmannV.I. Utkin
Geometry of Optimal Control Problems and Hamiltonian Systemsp. 1
1 Lagrange Multipliers' Geometryp. 1
1.1 Smooth Optimal Control Problemsp. 1
1.2 Lagrange Multipliersp. 4
1.3 Extremalsp. 6
1.4 Hamiltonian Systemp. 7
1.5 Second Order Informationp. 10
1.6 Maslov Indexp. 14
1.7 Regular Extremalsp. 22
2 Geometry of Jacobi Curvesp. 25
2.1 Jacobi Curvesp. 25
2.2 The Cross-Ratiop. 26
2.3 Coordinate Settingp. 28
2.4 Curves in the Grassmannianp. 29
2.5 The Curvaturep. 30
2.6 Structural Equationsp. 33
2.7 Canonical Connectionp. 35
2.8 Coordinate Presentationp. 38
2.9 Affine Foliationsp. 39
2.10 Symplectic Settingp. 41
2.11 Monotonicityp. 44
2.12 Comparison Theoremp. 49
2.13 Reductionp. 51
2.14 Hyperbolicityp. 53
Referencesp. 58
Lecture Notes on Logically Switched Dynamical Systemsp. 61
1 The Quintessential Switched Dynamical System Problemp. 62
1.1 Dwell-Time Switchingp. 62
1.2 Switching Between Stabilizing Controllersp. 65
1.3 Switching Between Graphsp. 66
2 Switching Controls with Memoryless Logicsp. 67
2.1 Introductionp. 67
2.2 The Problemp. 67
2.3 The Solutionp. 67
2.4 Analysisp. 68
3 Collaborationsp. 68
4 The Curse of the Continuump. 69
4.1 Process Model Classp. 69
4.2 Controller Covering Problemp. 73
4.3 A Natural Approachp. 74
4.4 A Different Approachp. 75
4.5 Which Metric?p. 75
4.6 Construction of a Control Coverp. 76
5 Supervisory Controlp. 76
5.1 The Systemp. 77
5.2 Slow Switchingp. 86
5.3 Analysisp. 87
5.4 Analysis of the Dwell Time Switching Logicp. 102
6 Flockingp. 110
6.1 Leaderless Coordinationp. 111
6.2 Symmetric Neighbor Relationsp. 142
6.3 Measurement Delaysp. 148
6.4 Asynchronous Flockingp. 155
6.5 Leader Followingp. 158
Referencesp. 159
Input to State Stability: Basic Concepts and Resultsp. 163
1 Introductionp. 163
2 ISS as a Notion of Stability of Nonlinear I/O Systemsp. 163
2.1 Desirable Propertiesp. 164
2.2 Merging Two Different Views of Stabilityp. 165
2.3 Technical Assumptionsp. 166
2.4 Comparison Function Formalismp. 166
2.5 Global Asymptotic Stabilityp. 167
2.6 0-GAS Does Not Guarantee Good Behavior with Respect to Inputsp. 168
2.7 Gains for Linear Systemsp. 168
2.8 Nonlinear Coordinate Changesp. 169
2.9 Input-to-State Stabilityp. 171
2.10 Linear Case, for Comparisonp. 172
2.11 Feedback Redesignp. 173
2.12 A Feedback Redesign Theorem for Actuator Disturbancesp. 174
3 Equivalences for ISSp. 176
3.1 Nonlinear Superposition Principlep. 176
3.2 Robust Stabilityp. 177
3.3 Dissipationp. 178
3.4 Using "Energy" Estimates Instead of Amplitudesp. 180
4 Cascade Interconnectionsp. 180
4.1 An Example of Stabilization Using the ISS Cascade Approachp. 182
5 Integral Input-to-State Stabilityp. 183
5.1 Other Mixed Notionsp. 183
5.2 Dissipation Characterization of iISSp. 184
5.3 Superposition Principles for iISSp. 185
5.4 Cascades Involving iISS Systemsp. 186
5.5 An iISS Examplep. 188
6 Input to State Stability with Respect to Input Derivativesp. 190
6.1 Cascades Involving the D[superscript k]ISS Propertyp. 190
6.2 Dissipation Characterization of D[superscript k]ISSp. 191
6.3 Superposition Principle for D[superscript k]ISSp. 191
6.4 A Counter-Example Showing that D[superscript 1]ISS [not equal] ISSp. 192
7 Input-to-Output Stabilityp. 192
8 Detectability and Observability Notionsp. 194
8.1 Detectabilityp. 195
8.2 Dualizing ISS to OSS and IOSSp. 196
8.3 Lyapunov-Like Characterization of IOSSp. 196
8.4 Superposition Principles for IOSSp. 197
8.5 Norm-Estimatorsp. 197
8.6 A Remark on Observers and Incremental IOSSp. 198
8.7 Variations of IOSSp. 199
8.8 Norm-Observabilityp. 200
9 The Fundamental Relationship Among ISS, IOS, and IOSSp. 201
10 Systems with Separate Error and Measurement Outputsp. 202
10.1 Input-Measurement-to-Error Stabilityp. 202
10.2 Review: Viscosity Subdifferentialsp. 203
10.3 RES-Lyapunov Functionsp. 204
11 Output to Input Stability and Minimum-Phasep. 205
12 Response to Constant and Periodic Inputsp. 205
13 A Remark Concerning ISS and H[subscript infinity] Gainsp. 206
14 Two Sample Applicationsp. 207
15 Additional Discussion and Referencesp. 209
Referencesp. 213
Generalized Differentials, Variational Generators, and the Maximum Principle with State Constraintsp. 221
1 Introductionp. 221
2 Preliminaries and Backgroundp. 222
2.1 Review of Some Notational Conventions and Definitionsp. 222
2.2 Generalized Jacobians, Derivate Containers, and Michel-Penot Subdifferentialsp. 228
2.3 Finitely Additive Measuresp. 229
3 Cellina Continuously Approximable Mapsp. 230
3.1 Definition and Elementary Propertiesp. 231
3.2 Fixed Point Theorems for CCA Mapsp. 234
4 GDQs and AGDQsp. 243
4.1 The Basic Definitionsp. 244
4.2 Properties of GDQs and AGDQsp. 246
4.3 The Directional Open Mapping and Transversality Propertiesp. 255
5 Variational Generatorsp. 267
5.1 Linearization Error and Weak GDQsp. 267
5.2 GDQ Variational Generatorsp. 269
5.3 Examples of Variational Generatorsp. 270
6 Discontinuous Vector Fieldsp. 277
6.1 Co-Integrably Bounded Integrally Continuous Mapsp. 277
6.2 Points of Approximate Continuityp. 280
7 The Maximum Principlep. 281
Referencesp. 285
Sliding Mode Control: Mathematical Tools, Design and Applicationsp. 289
1 Introductionp. 289
2 Examples of Dynamic Systems with Sliding Modesp. 289
3 VSS in Canonical Spacep. 296
3.1 Control of Free Motionp. 298
3.2 Disturbance Rejectionp. 300
3.3 Comments for VSS in Canonical Spacep. 301
3.4 Preliminary Mathematical Remarkp. 302
4 Sliding Modes in Arbitrary State Spaces: Problem Statementsp. 303
5 Sliding Mode Equations: Equivalent Control Methodp. 305
5.1 Problem Statementp. 305
5.2 Regularizationp. 306
5.3 Boundary Layer Regularizationp. 311
6 Sliding Mode Existence Conditionsp. 313
7 Design Principlesp. 316
7.1 Decoupling and Invariancep. 316
7.2 Regular Formp. 318
7.3 Block Control Principlep. 320
7.4 Enforcing Sliding Modesp. 322
7.5 Unit Controlp. 325
8 The Chattering Problemp. 327
9 Discrete-Time Systemsp. 330
9.1 Discrete-Time Sliding Mode Conceptp. 331
9.2 Linear Discrete-Time Systems with Known Parametersp. 333
9.3 Linear Discrete-Time Systems with Unknown Parametersp. 335
10 Infinite-Dimensional Systemsp. 336
10.1 Distributed Control of Heat Processp. 337
10.2 Flexible Mechanical Systemp. 338
11 Control of Induction Motorp. 340
Referencesp. 344
List of Participantsp. 349