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20172022
most citedA Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints

6 citations · 19 across the 7 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

eess.SY20201 cited

Co-design of Optimal Transmission Power and Controller for Networked Control Systems Under State-dependent Markovian Channels

Bin Hu, Tua A. Tamba

This paper considers a co-design problem for industrial networked control systems to ensure both the stability and efficiency properties of such systems. The assurance of such prop…

math.OC20206 cited

Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient-Based Methods and Global Convergence

Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud

Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the global…

math.OC20205 cited

Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems

Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud

Markovian jump linear systems (MJLS) are an important class of dynamical systems that arise in many control applications. In this paper, we introduce the problem of controlling unk…

eess.SY2020

On a notion of stochastic zeroing barrier function

Tua A. Tamba, Bin Hu, Yul Y. Nazaruddin

This note examines the safety verification of the solution of Ito stochastic differential equations using the notion of stochastic zeroing barrier function. The main tools in the p…

math.OC2020

Convergence Guarantees of Policy Optimization Methods for Markovian Jump Linear Systems

Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud

Recently, policy optimization for control purposes has received renewed attention due to the increasing interest in reinforcement learning. In this paper, we investigate the conver…