Learning-Based Control Systems Techniques in Neural, Fuzzy, and Adaptive Control

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

Biography

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.