Learning-Based Control Systems

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.