Classical control starts from a known model of the plant; this course is about what to do when you don't have one and have to let measured data fill that gap. You'll spend the term identifying input-output and state-space models from noisy experiments, compressing them with DMD/DMDc, and wrapping the result in MPC or reinforcement-learning controllers, typically in Python and MATLAB, building toward a project. It sits at the intersection of signals, optimization, and ML, and is increasingly how robotics, process, and aerospace groups close the loop when first-principles modeling runs out.
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Course Learning Outcomes: Course Learning Outcome Assessment Acquire and preprocess experimental data for control design Midterm Final Term project HW - evaluated via quizzes Construct and validate linear input output and state space models using system identification techniques Midterm Final Term project HW - evaluated via quizzes Extract reduced order predictive models with Dynamic Mode Decomposition (DMD/DMDc). Midterm Final Term project HW - evaluated via quizzes Formulate and implement Mode