The book offers in-depth coverage of advanced topics essential for modern applications: digital and discrete-time controllers, nonlinear and adaptive strategies, and robust techniques for managing model uncertainty. Readers will find clear expositions of Lyapunov-based methods, optimal control, H-infinity synthesis, and the design of networked and distributed systems. Special attention is given to data-driven and learning-augmented approaches, showing how AI and machine learning integrate with control theory to produce cooperative, adaptive, and resilient solutions for complex, interconnected systems.
Practical deployment is woven throughout the narrative, with hands-on guidance for simulation, rapid prototyping, embedded implementation, certification, and formal verification in safety-critical domains. Hardware considerations, real-world constraints, and industry case studies-from aerospace to advanced manufacturing-ensure the material remains grounded in engineering practice. By bridging theoretical rigor with pragmatic strategies, this book serves as an indispensable reference for those designing robust feedback systems in today's dynamic technological landscape.
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