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Maybe check out Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard. It has a coherent Bayesian formulation with consistent notation on m
by bonoboTP 3y ago
Maybe check out Probabilistic Robotics by Dieter Fox, Sebastian Thrun, and Wolfram Burgard. It has a coherent Bayesian formulation with consistent notation on many Kalman-related topics. Also with the rise of AI/ML, classic control theory ideas are being merged with reinforcement learning.
- girzel 3y agoThanks for the recommendation! It would never have occurred to me to look at robotics, but I can understand why that's very relevant. I read Feedback Control for Computer Systems not too long ago, which felt like yet another restatement of the same ideas; I guess that counts as "classic control theory".
- esafak 3y agoI agree that Bayesian filtering is the most general and logical approach. There are Bayesian derivations of the Kalman filter too. Here is a broad survey: https://people.bordeaux.inria.fr/pierre.delmoral/chen_bayesian.pdf https://people.bordeaux.inria.fr/pierre.delmoral/chen_bayesi...