Practical AI Control Methods

Neural, Fuzzy, and Reinforcement Learning Approaches
1. Auflage 2026
ISBN: 978-1-041-37047-5
Verlag: Taylor & Francis Ltd

Buch, Englisch, 480 Seiten, Format (B × H): 156 mm x 234 mm

This comprehensive guide bridges classical control theory and modern AI-driven control systems, demonstrating how neural networks, fuzzy logic, and reinforcement learning enable adaptive controllers that learn from data and handle complex nonlinearities. Moving beyond theoretical foundations, the book emphasizes practical implementation through detailed Python and Simulink examples, covering neural network architectures, deep reinforcement learning, transformer-based control, and hybrid fuzzy-AI systems.

Designed for graduate students, advanced undergraduates, and practicing engineers in control systems and AI, the text assumes familiarity with classical control, Python programming, and machine learning fundamentals. Readers gain hands-on experience building intelligent controllers through project-driven tutorials that address real-world deployment challenges, validation strategies, and the practical realities of learning-based control, including the absence of classical stability guarantees and the need for empirical validation.

With coverage spanning system identification, vision-based perception, model predictive control, and deployment on embedded platforms, this book serves as both a practical manual and technical reference for designing, implementing, and deploying AI-enabled control architectures.

- Emphasizes hands-on controller construction, data preparation, training workflows, and simulation setup rather than pure theory.

- Presents AI-control algorithms as implementation tutorials using Python and Simulink examples.

- Includes complete project walkthroughs for neural network controllers, reinforcement learning navigation, and hybrid fuzzy-AI systems.

Zielgruppe

General

Autoren/Hrsg.

Pasko, Robert

Fachgebiete

• Technische Wissenschaften Bauingenieurwesen Haustechnik, Gebäudeautomatisierung
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Elektrikerhandwerk
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise

Inhaltsverzeichnis

Preface

Acknowledgments

About the Author

1 Introduction to AI Control

2 AI Techniques for Control

3 Neural Computation and Optimization

4 Neural Network Control

5 Reinforcement Learning for Control

6 Fuzzy Logic for Control

7 System Identification

8 CNN

9 Simulation and Deployment Tools

10 Advanced AI Control

11 Transformers for Control and ID

12 Hybrid Fuzzy-AI Control

13 Best Practices

14 Conclusion and Future Directions 433

Appendix

Index

Über Autor(innen)

Robert Pasko, Jr., MS, holds a Master of Science in Engineering with a concentration in Control Systems, with a focus on classical control theory, AI-based control strategies, intelligent simulation environments, and neural network architectures. He also holds a Master of Science in Microbiology, during which he conducted original research and published a thesis on molecular plant-microbe interactions, with particular emphasis on symbiotic signaling pathways.

Mr. Pasko brings an unusually diverse and interdisciplinary background to his work, integrating deep experience from scientific, technical, and applied domains. This broad foundation supports his current focus on intelligent control systems and neural adaptive algorithms, particularly where real-time decision-making intersects with safety-critical environments.

His research and engineering projects explore the interface between machine learning, simulation, and control, bridging theoretical methods with hands-on system development. He has also contributed to educational materials and academic publishing efforts in the field of AI control.

His research interests include learning-augmented predictive control within structure-constrained feedback architectures; adaptive modeling and perception modules that support closed-loop regulation of nonlinear dynamical systems; and bounded hybrid adaptation methods. Across these areas, methods are evaluated with respect to state constraints, actuator limits, and failure-mode analysis.