Work by Jeb Stefan and Takashi Tanaka, CDC 2019

Discussion by Ben Gravell, January 31, 2020

Keywords: Linear quadratic regulator, information, theory, regularization, matrix inequality, iterative

Summary

Researchers from UT Austin formulate a problem of jointly optimizing quadratic cost of a linear system and an information-theoretic communication cost which accommodates partial channel capacity. It is demonstrated empirically that this optimization can be solved with an iterative semidefinite program (SDP), and that the communication cost acts as a regularizer on the control gains, which in some cases promotes sparsity. This is similar to our own work on learning sparse control; we would love to see how data driven approaches could augment info-theoretic notions in the case of unknown dynamics.

Paper link is forthcoming once the CDC proceedings have been published. Author website is here.

Work by Javier Tapia, Espen Knoop , Mojmir Mutny, Miguel A. Otaduy, and Moritz Bacher, 2020

Keywords: Convex, optimization, feasible, design

Summary

Researchers at Disney have created a method for doing optimized sensor selection for soft robotics. A large number of presumptive fabricable sensors are virtually generated and are then culled down to a small set which give good pose estimation in simulation. They then verify the method experimentally by fabricating physical soft robots with highly elastic strain sensors embedded in a flexible polymer. Although our own research is geared toward theoretical analysis of sensor and actuator selection in the case of well-defined networked linear systems, we found this study a fascinating tangent.

Watch the video and read the paper here.

Work by Shane Barratt, Guillermo Angeris, Stephen Boyd, 2020

Keywords: Convex, optimization, feasible, design

Summary

This work looks at the meta-optimization setting where the original convex problem is infeasible, unbounded, or pathological (bad) and the problem is changed by a small (minimum) amount to become feasible, bounded, and nonpathological (good). The problem parameter perturbation is itself minimized by the meta-optimization. The authors give examples from control and economic theory, showing that their method can be used as a design tool e.g. slightly changing the mass properties of an aerospace vehicle such that a constrained trajectory planning problem becomes feasible.

Read the paper on arXiv here.

Work by John C. Doyle, IEEE TAC 1978

Keywords: Robust control, optimal control, robustness margins, linear quadratic Gaussian, linear quadratic regulator, Kalman filter

Summary

In this classic paper with the famously short 3-word long abstract “there are none”, it is shown by counterexample that linear-quadratic Gaussian (LQG) regulators do not possess any gain or phase stability robustness margins. This stands in stark contrast to the case of linear quadratic regulators (LQR) which were shown a year prior by Safanov and Athens to possess impressive guaranteed 6dB gain and 60 degree phase margins.

Read the paper here or from IEEE Xplore.

See Stephen Tu’s nice walkthrough of this paper.