Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Abstracts:Presents the recipients of The Roger W. Brockett Control Systems awards for 2024.

Abstracts:Presents the recipients of IEEE CSS awards for 2024.

Abstracts:This review article addresses the problem of learning abstract representations of measurement data in the context of deep reinforcement learning. While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for 1) improving the data efficiency, robustness, and generalization of DRL methods; 2) tackling the curse of dimensionality; and 3) bringing interpretability and insights into black-box DRL. This review provides a comprehensive and complete overview of unsupervised representation learning in DRL by describing the main DL tools used for learning representations of the world, providing a systematic view of the method and principles; summarizing applications, benchmarks, and evaluation strategies; and discussing open challenges and future directions.

Abstracts:Presents reviews for the following list of books, Extremum Seeking Through Delays and PDEs.

Abstracts:The duality between estimation and control is a foundational concept in control theory. Most students learn about the elementary duality—between observability and controllability—in their first graduate course in linear systems theory. Therefore, it comes as a surprise that for a more general class of nonlinear stochastic systems (HMMs), duality is incomplete. Our objective in writing this article is twofold: 1) to describe the difficulty in extending duality to HMMs and 2) to discuss its recent resolution by the authors. A key message is that the main difficulty in extending duality comes from time reversal when going from estimation to control. The reason for time reversal is explained with the aid of the familiar linear deterministic and linear Gaussian models. The explanation is used to motivate the difference between the linear and the nonlinear models. Once the difference is understood, duality for HMMs is described based on our recent work. The article also includes a comparison and discussion of the different types of duality considered in the literature.