Embedded Control Systems

Catalog description

This course provides a study of embedded control systems. Students will analyze, design, and implement discrete feedback control systems. Sampling, quantization, and filtering for digital control systems are introduced. Modern state-space analysis and design techniques for discrete systems utilizing state space controllers and observers are examined. Optimal controls for digital systems are introduced and explored through regulation, system identification, Kalman estimation, and a comprehensive Model Predictive Control approach. Introduction to data driven Machine Learning Controls is explored. Embedded control implementation will be conducted with evaluation in the lab setting using discrete simulation, code generation tools, and hardware platforms such as multi-core microcontrollers. Applied AI Integration: This course meets or exceeds MSOE’s Applied AI Integration guidelines for substantial coverage of Foundational Knowledge and Practical Application.