Preparing your Path…
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Path Catalog
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7801 Paths · page 273 / 781
This learning path equips senior or graduate students with the knowledge and skills to apply control engineering principles to automotive systems, covering engine control, ABS, suspension, and ESC. It starts with foundational control theory and vehicle dynamics, then progresses through modeling, actuator and sensor considerations, and concludes with application-specific control strategies and system integration.
This advanced learning path equips senior and graduate students with the knowledge to apply control theory to aerospace systems. It covers modeling, analysis, and design of autopilots and spacecraft control, building from core principles to advanced applications.
This advanced learning path equips senior engineering students with the knowledge and skills to apply feedback, cascade, and model-based control strategies to chemical and industrial processes. It covers process modeling, dynamic analysis, controller design, and practical implementation considerations.
This advanced learning path equips senior and graduate students with the skills to simulate control systems using software. It covers mathematical modeling, numerical simulation, controller design, validation, and hardware-in-the-loop testing, ensuring a comprehensive understanding of the entire simulation workflow.
This learning path guides junior engineering students through the essential MATLAB programming skills and Control System Toolbox workflows needed to model, analyze, and design control systems. Starting with MATLAB fundamentals, the path progresses through system modeling, time and frequency domain analysis, and controller design via root locus and frequency response methods, culminating in simulation and validation. The path emphasizes practical, career-relevant skills for control engineering.
A graduate-level learning path covering the modeling, analysis, and control of distributed parameter systems (DPS), focusing on PDEs, transfer functions, and boundary control. Learners will build from functional analysis and linear systems theory to advanced control design for infinite-dimensional systems.
A comprehensive graduate-level path to apply stochastic methods in control engineering. It builds from probability and stochastic processes through optimal control and estimation to culminate in LQG control with noise.
A graduate-level learning path building rigorous linear systems theory from state-space foundations through advanced matrix methods and system decompositions. It emphasizes mathematical depth, structural analysis, and practical applications in control engineering.
This learning path provides a systematic study of adaptive control, focusing on self-tuning regulators and model reference adaptive control. It covers the necessary prerequisite knowledge in linear systems, parameter estimation, and stability theory, then explores the design, analysis, and implementation of adaptive controllers. The path is designed for senior university students with an advanced understanding of control engineering.
This advanced learning path equips senior engineering students with the knowledge and skills to design controllers that perform reliably despite model uncertainties. It covers singular value analysis, uncertainty modeling, robust stability and performance, and H-infinity control synthesis, culminating in a comprehensive design project.