Before Memmo my notes were scattered across PDFs. Now a workspace pulls everything into one place — I see exactly what's still left to study.
The loop-shaping approach consists of obtaining a specification in relation to the open loop of the control from specifications regarding various closed loop transfers, because it is easier to work on a single transfer (in addition to the open loop) than on a multitude of transfers (various loopings such as set point/error, disturbance/error, disturbance/control, etc.). The simplicity and flexibility of the approach make it very well adapted to the industrial context.
This book presents the loop-shaping approach in its entirety, starting with the declension of high-level specifications into a loop-shaping specification. It then shows how it is possible to fully integrate this approach for the calculation of robust and efficient correctors with the help of existing techniques, which have already been industrially tried and tested, such as H-infinity synthesis. The concept of a gap metric (or distance between models) is also presented along with its connection with the prime factors of a set of systems shaping a ball of models, as well as its connections with robust synthesis by loop-shaping, in order to calculate efficient and robust correctors. As H-infinity loop-shaping is often demanding in terms of the order of correctors, the author also looks at loop-shaping synthesis under an ordering constraint. Two further promising lines of research are presented, one using stochastic optimization, and the other non-smooth optimization. Finally, the book introduces the concept of correction with two degrees of freedom via the formalism of prime factorization.
Avenues for future work are also opened up by the author as he discusses the main drawbacks to loop-shaping synthesis, and how these issues can be solved using modern optimization techniques in an increasingly competitive industrial context, in accordance with ever more complex sets of functional specifications, associated with increasingly broad conditions of usage.
Before Memmo my notes were scattered across PDFs. Now a workspace pulls everything into one place — I see exactly what's still left to study.
Memmo's summaries are gold before exams. I don't have to re-read 800 pages two weeks before — just the important parts.
The AI chat has saved me the night before an exam more than once. I just keep asking until I get it — no waiting on a study group to reply.
The quizzes hit exactly what I need to know. Memmo tracks what I get stuck on — so I only practice what's worth it.
Flashcards with spaced repetition are magic. Memmo knows when I'm about to forget something and brings it back.
The AI podcasts are my favorite. I listen on my way to school and get a recap without sitting at a computer.
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