Classical control assumes you already have a clean model of your plant; this course is about what to do when you don't, and instead have to let measured data do the modeling for you. You'll work through the full pipeline on real signals — cleaning noisy measurements, fitting ARX and subspace models, pulling out reduced dynamics with DMD, then closing the loop with predictive and reinforcement-learning controllers, mostly in Python and Matlab across six homeworks and a project. It sits naturally after a first controls course and points toward where industry actually lives now, since most modern plants are tuned from logged data rather than first-principles derivations.
→ STARS müfredatı (resmi syllabus)
İlk dosyayı sen atarsan — not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa — defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.
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