Design, simulate, and deploy robust feedback control loops, PID controllers, and automated systems for real-world engineering hardware
Modern mechanical and manufacturing systems rely on control systems that must perform reliably under constant change — from shifting loads to uncertainty and increasing automation complexity. Yet in practice, many engineers work with PID loops, robotics, and automated systems without a clear, practical framework for understanding instability, tuning, or when classical control approaches start to break down.
This session delivers a grounded, application-focused introduction to control systems engineering for real industrial environments. Moving beyond textbook theory, it explains how closed-loop systems behave in practice, how PID controllers are tuned and optimized, and how engineers manage nonlinearities, uncertainty, and performance limits. It also introduces modern developments such as adaptive and AI-based control in robotics and manufacturing. Participants will leave with a clearer, more intuitive understanding of how to design, diagnose, and improve real-world control systems with confidence.
Key Topics Discussed:
Mechanical Engineer and Product Manager
Glen Merritt is a PhD-level engineer and researcher specializing in advanced control systems, robotics, and automation engineering. His work focuses on applying machine learning, neural networks, and model-based control techniques to complex physical systems, particularly in industrial automation and additive manufacturing processes. He has contributed to peer-reviewed research in areas such as nonlinear system control, hybrid robotic systems, and real-time adaptive control methods, including applications in additive friction stir deposition and rehabilitation robotics. His background combines strong theoretical expertise in dynamical systems with practical engineering implementation in high-performance industrial environments.
