Assessment and Future Directions of Nonlinear Model Predictive Control 2007th Edition by Rolf Findeisen, Frank Allgower, Lorenz Biegler ISBN 3540726985 9783540726982 by Rolf Findeisen 9783540726982, 3540726985 instant download after payment.

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ISBN 10: 3540726985 
ISBN 13: 9783540726982
Author: Rolf Findeisen, Frank Allgower, Lorenz Biegler

Assessment and Future Directions of Nonlinear Model Predictive Control 2007th Table of contents:

Part I: Foundations and Background

• Chapter 1: Introduction to Model Predictive Control (MPC)
  • Basic Principles of MPC: Prediction, Optimization, Receding Horizon
  • Linear Model Predictive Control (LMPC) Revisited
  • Advantages and Limitations of LMPC
  • Why Nonlinearity Matters: Necessity of NMPC
• Chapter 2: Fundamentals of Nonlinear Systems
  • Definition and Characteristics of Nonlinear Systems
  • Stability Concepts for Nonlinear Systems (Lyapunov, Input-to-State Stability)
  • Optimization Basics for Nonlinear Problems (Unconstrained and Constrained)
  • Numerical Methods for Solving Nonlinear Equations and Optimization

Part II: The State of the Art in NMPC (Assessment)

• Chapter 3: Core NMPC Algorithms and Formulations
  • Direct vs. Indirect Optimization Methods
  • Single Shooting, Multiple Shooting, and Collocation Methods
  • Real-Time Iteration Schemes
  • Explicit NMPC and Parametric Solutions
• Chapter 4: Stability and Robustness in NMPC
  • Guaranteed Stability Properties of NMPC
  • Terminal Constraints and Cost Functions
  • Robust NMPC Approaches (Min-Max, Tube-based, Probabilistic)
  • Handling Disturbances and Model Mismatches
• Chapter 5: Computational Challenges and Solutions
  • The Online Optimization Burden
  • Efficient Numerical Solvers for NMPC (SQP, Interior Point, Gauss-Newton)
  • Parallel Computing and Hardware Acceleration for NMPC
  • Approximation Techniques for Real-Time Implementation

Part III: Future Directions and Emerging Trends

• Chapter 6: NMPC and Machine Learning
  • Data-Driven Modeling for NMPC (System Identification, Neural Networks)
  • Reinforcement Learning (RL) and NMPC Hybrid Approaches
  • Learning-Based Robustness and Adaptability
  • Safe Learning and Verification in Control
• Chapter 7: Advanced NMPC Formulations
  • Hybrid NMPC for Switched and Discontinuous Systems
  • Distributed and Decentralized NMPC
  • Stochastic NMPC for Uncertain Systems
  • Multi-Objective NMPC
• Chapter 8: NMPC for Complex and Large-Scale Systems
  • Decomposition Techniques for High-Dimensional Problems
  • Reduced-Order Models in NMPC
  • NMPC for Cyber-Physical Systems
  • Integration with Other Control Paradigms

Part IV: Practical Considerations and Applications

• Chapter 9: Software and Implementation Aspects
  • Available NMPC Software Tools and Libraries (ACADO, CasADi, FORCESPRO, etc.)
  • Considerations for Embedded Systems and Real-Time Operating Systems
  • Model Simplification and Validation in Practice
• Chapter 10: Case Studies and Industrial Applications
  • Autonomous Driving and Robotics
  • Chemical Process Control (Reactors, Distillation Columns)
  • Aerospace and Space Systems
  • Renewable Energy Systems (Wind Turbines, Smart Grids)
  • Biomedical Systems and Personalized Medicine
• Chapter 11: Challenges for Future Deployment
  • Certification and Safety Critical Applications
  • Operator Acceptance and Human-Machine Interaction
  • Scalability for Industrial-Scale Problems
  • Closing the Gap Between Theory and Practice

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