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Nonlinear Model Predictive Control - Theory and Algorithms
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Nonlinear Model Predictive Control - Theory and Algorithms
von: Lars Grüne, Jürgen Pannek
Springer-Verlag, 2016
ISBN: 9783319460246
463 Seiten, Download: 9841 KB
 
Format: PDF
geeignet für: PC, MAC, Laptop Online-Lesen Apple iPad, Android Tablet PC's

Typ: B (paralleler Zugriff)

 

 
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Inhaltsverzeichnis

  Preface to the Second Edition 7  
  Preface to the First Edition 9  
  Contents 11  
  1 Introduction 15  
     1.1 What Is Nonlinear Model Predictive Control? 15  
     1.2 Where Did NMPC Come From? 17  
     1.3 How Is This Book Organized? 19  
     1.4 What Is Not Covered in This Book? 23  
     References 24  
  2 Discrete Time and Sampled Data Systems 26  
     2.1 Discrete Time Systems 26  
     2.2 Sampled Data Systems 29  
     2.3 Stability of Discrete Time Systems 42  
     2.4 Stability of Sampled Data Systems 50  
     2.5 Notes and Extensions 53  
     References 56  
  3 Nonlinear Model Predictive Control 57  
     3.1 The Basic NMPC Algorithm 57  
     3.2 Constraints 60  
     3.3 Variants of the Basic NMPC Algorithms 64  
     3.4 The Dynamic Programming Principle 70  
     3.5 Notes and Extensions 76  
     References 80  
  4 Infinite Horizon Optimal Control 82  
     4.1 Definition and Well Posedness of the Problem 82  
     4.2 The Dynamic Programming Principle 85  
     4.3 Relaxed Dynamic Programming 91  
     4.4 Notes and Extensions 97  
     References 100  
  5 Stability and Suboptimality Using Stabilizing Terminal Conditions 102  
     5.1 The Relaxed Dynamic Programming Approach 102  
     5.2 Equilibrium Endpoint Constraint 103  
     5.3 Lyapunov Function Terminal Cost 110  
     5.4 Suboptimality and Inverse Optimality 118  
     5.5 Notes and Extensions 126  
     References 129  
  6 Stability and Suboptimality Without Stabilizing Terminal Conditions 131  
     6.1 Setting and Preliminaries 131  
     6.2 Bounds on VN and Asymptotic Controllability with Respect to ell 135  
     6.3 Implications of the Bound on VN 139  
     6.4 Computation of ? 140  
     6.5 Main Stability and Performance Results 145  
     6.6 Design of Good Stage Costs ell 154  
     6.7 Semiglobal and Practical Asymptotic Stability 164  
     6.8 Proof of Proposition 6.18 173  
     6.9 Notes and Extensions 182  
     References 186  
  7 Feasibility and Robustness 187  
     7.1 The Feasibility Problem 187  
     7.2 Feasibility of Unconstrained NMPC Using Exit Sets 190  
     7.3 Feasibility of Unconstrained NMPC Using Stability 194  
     7.4 Comparing NMPC with and Without Terminal Conditions 198  
     7.5 Robustness: Basic Definition and Concepts 202  
     7.6 Robustness Without State Constraints 204  
     7.7 Examples for Nonrobustness Under State Constraints 209  
     7.8 Robustness with State Constraints via Robust-Optimal Feasibility 214  
     7.9 Robustness with State Constraints via Continuity of VN 219  
     7.10 Notes and Extensions 225  
     References 228  
  8 Economic NMPC 230  
     8.1 Setting 230  
     8.2 Averaged Performance with Terminal Conditions 232  
     8.3 Asymptotic Stability with Terminal Conditions 236  
     8.4 Non-averaged and Transient Performance with Terminal Conditions 240  
     8.5 Averaged Optimality Without Terminal Conditions 248  
     8.6 Practical Asymptotic Stability Without Terminal Conditions 252  
     8.7 Non-averaged and Transient Performance Without Terminal Conditions 257  
     8.8 Notes and Extensions 264  
     References 266  
  9 Distributed NMPC 268  
     9.1 Background and Problem Formulation 268  
     9.2 Classification of Connectedness 270  
     9.3 Problem Classes for Different Levels of Connectedness 281  
     9.4 Asymptotic Stability and Convergence 285  
     9.5 Communication and Coordination Schemes 290  
     9.6 Notes and Extensions 301  
     References 303  
  10 Variants and Extensions 305  
     10.1 Schemes with Mixed Terminal Conditions 305  
     10.2 Unconstrained NMPC with Terminal Weights 309  
     10.3 Nonpositive Definite Stage Cost 310  
     10.4 Multistep NMPC-Feedback Laws 314  
     10.5 Fast Sampling 316  
     10.6 Compensation of Computation Times 320  
     10.7 Online Measurement of ? 324  
     10.8 Adaptive Optimization Horizon 333  
     10.9 Nonoptimal NMPC 340  
     References 349  
  11 Numerical Discretization 351  
     11.1 Basic Solution Methods 351  
     11.2 Convergence Theory 356  
     11.3 Adaptive Step Size Control 361  
     11.4 Using the Methods Within the NMPC Algorithms 365  
     11.5 Numerical Approximation Errors and Stability 367  
     11.6 Notes and Extensions 371  
     References 374  
  12 Numerical Optimal Control of Nonlinear Systems 375  
     12.1 Discretization of the NMPC Problem 375  
     12.2 Unconstrained Optimization 388  
     12.3 Constrained Optimization 393  
     12.4 Implementation Issues in NMPC 416  
     12.5 Warm Start of the NMPC Optimization 426  
     12.6 Nonoptimal NMPC 434  
     12.7 Notes and Extensions 438  
     References 440  
  Appendix A NMPC Software Supporting This Book 443  
  A.1 The MATLAB NMPC Routine 443  
  A.2 Additional MATLAB and MAPLE Routines 445  
  A.3 The C++ NMPC Software 447  
  Appendix B Glossary 449  
  Index 456  


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