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Path Catalog
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7801 Paths · page 271 / 781
This graduate-level learning path equips learners with the knowledge to design and analyze control systems for solar, wind, and grid-integrated renewable energy systems. It covers fundamental control theory, renewable-specific modeling, and advanced control strategies for grid integration.
This graduate-level path equips learners to design, analyze, and implement control systems for cyber-physical systems (CPS), with emphasis on integration, security, and real-time control, and a specialization in networked control. It progresses from classical and state-space control foundations through discrete-time and real-time systems, to networked control, CPS security, robust control, and a capstone project that integrates all concepts.
This advanced graduate path explores the control of systems over communication networks, focusing on the challenges of delays, packet loss, and protocol constraints. It covers the necessary foundations in control theory and network fundamentals before diving into stability analysis and design for networked control systems.
This advanced graduate-level path provides a rigorous foundation in model predictive control (MPC), covering the underlying optimization theory, core formulation principles, and practical implementation strategies. Learners will progress from essential mathematical prerequisites to advanced topics like nonlinear MPC and real-time implementation, culminating in a capstone project that applies MPC to a realistic system.
This advanced graduate-level path equips learners with the knowledge to apply machine learning techniques, particularly reinforcement learning and data-driven methods, to control systems. It covers essential control theory, system identification, and machine learning foundations, culminating in the design and evaluation of ML-based controllers.
This learning path equips engineers and managers with the skills to plan, execute, and deliver control engineering projects. It bridges core control engineering concepts with project management practices, covering risk, coordination, and delivery in a technical context.
This professional learning path equips instrumentation engineers with the knowledge to select and apply sensors, transmitters, and signal conditioning for process control. It covers control fundamentals, measurement principles, and selection criteria, culminating in a practical application project.
This learning path equips process engineers with the skills to model and simulate process control systems. It covers dynamic modeling fundamentals, control theory, simulation tools, and model validation, culminating in a capstone simulation project.
This professional learning path equips mechatronics engineers with the knowledge and skills to design and implement high-performance motion control systems for positioning and servo applications. It covers modeling, control design, implementation, and practical considerations for real-world mechatronic systems.
This learning path equips field engineers with the knowledge and skills to safely and effectively tune and commission industrial control systems. Starting with foundational control theory and process dynamics, it progresses through loop checking, controller tuning methods, and advanced troubleshooting techniques. The path emphasizes practical application, safety, and systematic commissioning procedures.