Sparse Optimal Control of Networks with Multiplicative Noise via Policy Gradient

Authors: Benjamin Gravell, Yi Guo, Tyler Summers

Published: 2019 (Conference Paper)

Source: IFAC Workshop on Distributed Estimation and Control in Networked Systems (NECSYS)

arXiv: 1905.13548

DOI: 10.1016/j.ifacol.2019.12.176

Summary

Showed that one can use policy gradient to automatically design the placement of sensors and actuators in a controller for linear systems with multiplicative noise. Achieved by encoding the preference for certain kinds of sparsity patterns with sparsity-promoting convex regularizers in the objective function.

Abstract

We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks. Various regularization schemes are examined and incorporated into the optimization by the use of gradient, subgradient, and proximal gradient methods. Numerical experiments on a large networked system show that the algorithms converge to performant sparse mean-square stabilizing controllers.

Links

Primary

Paper arxiv.org

Standard

arXiv Abstract 1905.13548 arXiv PDF 1905.13548 arXiv HTML 1905.13548 DOI 10.1016/j.ifacol.2019.12.176

Alternate

sciencedirect.com sciencedirect.com personal.utdallas.edu PDF personal.utdallas.edu researchgate.net researchgate.net GitHub: TSummersLab/polgrad-multinoise github.com

Tags

• Optimal control

• Multiplicative noise

• Networks

• Sensor placement

• Actuator placement