Systems Theory in Data and Optimization

Part I. Data-Driven and Learning-Based Control.- Chapter 1. PACSBO: Probably Approximately Correct Safe Bayesian Optimization.- Chapter 2. Value of Communication: Data-Driven Topology Optimization for Distributed Linear Cyber-Physical Systems.- Chapter 3. Variance-Informed Model Reference Gaussian Process Regression: Utilizing Variance Information for Control in Nonlinear Systems.- Chapter 4. Data-Driven Dynamic Model and Model Reference Control of Inverter Based Resources.- Chapter 5. Adaptive Tracking MPC for Nonlinear Systems via Online Linear System Identification.- Part II: Machine Learning: Theory and Applications.- Chapter 6. Investigation of the Influence of Training Data and Methods on the Control Performance of MPC Utilizing Gaussian Processes.- Chapter 7. Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty.- Chapter 8. A Universal Reproducing Kernel Hilbert Space for Learning Nonlinear Systems Operators.- Chapter 9. On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks.- Chapter 10. Solving Partial Differential Equations with Equivariant Extreme Learning Machines.- Chapter 11. Adaptive Robust L2 Loss Function using Fractional Calculus.- Chapter 12. Sparse Reconstruction of Forces, Torques and Velocity Signals for a Swimmer in a Wake.- Chapter 13. Control Theoretic Approach to Fine-Tuning and Transfer Learning.- Part III. Model Predictive Control.- Chapter 14. Accelerating Multi-Objective Model Predictive Control Using High-Order Sensitivity Information.- Chapter 15. On Discount Functions for Economic Model Predictive Control without Terminal Conditions.- Chapter 16. Multi-Parametric Programming with Constraint Telaxation for the Optimal Operation of Micro-Grids Integrating Renewables.- Chapter 17. Multi-Objective Learning Model Predictive Control.- Chapter 18. Terminal Set of Nonlinear Model Predictive Control with Koopman Operators.- Part IV: Optimization.- Chapter 19. Optimal Dynamic Pricing in Energy Markets: A Stackelberg Game Approach.- Chapter 20. Distributed Newton Optimization with ADMM-Based Consensus.- Chapter 21. Inexactness in Bilevel Nonlinear Optimization: A Gradient-free Newton’s Method Approach.

• Erscheinungsdatum: 26.09.2025
• Autor/Autorin: Julian Berberich
• Reihe: Springer Nature Proceedings excluding Computer Science
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 18.9 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 500 Seiten
• ISBN: 9783031831911
• Lieferung: Sofort per Download
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