State constraints in the linear regulator problem: Case study

In this paper, we consider the problem of minimum-norm control of the double integrator with bilateral inequality constraints for the output. We approximate the constraints by piecewise linear functions and prove that the Langrange multipliers associated with the state constraints of the approximating problem are discrete measures, concentrated in at most two points in every interval of discretization. This allows us to reduce the problem to a convex finite-dimensional optimization problem. An algorithm based on this reduction is proposed and its convergence is examined. Numerical examples illustrate our approach. We also discuss regularity properties of the optimal control for a higher-dimensional state-constrained linear regulator problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45244/1/10957_2005_Article_BF02192567.pd

Cooperative constrained parameter estimation by ADMM-RLS

With recent advances in cloud computing, resources with customizable computational power and memory can be exploited to store and analyze data collected from large sets of devices. Although one can exploit the connection to the cloud to perform all the desired tasks on the cloud itself, in many applications it is also desirable to retrieve and process information locally. In this paper, we present a collection of cloud-aided consensus-based Recursive Least-Squares (RLS) estimators. The approaches are tailored to handle linear and nonlinear consensus constraints and limitations on parameter ranges. All the methods are designed so that raw measurements collected at the device level are processed by the device itself, requiring minimal changes to (possibly pre-existing) RLS estimators. The local estimates are then recursively refined and fused on the cloud to reach consensus among the devices

Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected diagnostics and prognostics

As the connectivity of consumer devices is rapidly growing and cloud computing technologies are becoming more widespread, cloud-aided algorithms for parameter estimation can be developed to exploit the theoretically unlimited storage memory and computational power of the 'cloud', while relying on information provided by multiple sources. With the ultimate goal of developing monitoring, diagnostic and prognostic strategies, this paper focuses on the design of a Recursive Least-Squares (RLS) based estimator for identification over a multitude of similar devices (such as a mass production) connected to the cloud. The proposed approach, that relies on Node-to-Cloud-to-Node (N2C2N) transmissions, is designed so that: (i) estimates of the unknown parameters are computed locally and (ii) the local estimates are refined on the cloud by exploiting the additional information that the devices have similar characteristics. The proposed approach requires minimal changes to local (pre-existing) RLS estimators

Justice (Vol. 09, Iss. 08)

Justice was the official publication of the International Ladies’ Garment Workers’ Union ILGWU from 1919 to 1995. Editions of Justice were published in English, Italian, Spanish, and Yiddish. When compared side by side, the content of some of these different editions of Justice shows significant differences. This is the English-language edition of Justice.Justice_9_8.pdf: 83 downloads, before Oct. 1, 2020