activity
20182020
most citedNear-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework

7 citations · 15 across the 3 of their papers we have counts for

collaborators

6 papers

cs.RO2020

Formation and Reconfiguration of Tight Multi-Lane Platoons

Roya Firoozi, Xiaojing Zhang, Francesco Borrelli

Advances in vehicular communication technologies are expected to facilitate cooperative driving. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execut…

eess.SY20197 cited

Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Framework

Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli

In this paper, we propose a novel framework for approximating the explicit MPC policy for linear parameter-varying systems using supervised learning. Our learning scheme guarantees…

eess.SY20192 cited

Autonomous Parking of Vehicle Fleet in Tight Environments

Xu Shen, Xiaojing Zhang, Francesco Borrelli

The problem of autonomous parking of vehicle fleets is addressed in this paper. We present a system-level modeling and control framework which allows investigating different vehicl…

eess.SY2019

Adaptive MPC under Time Varying Uncertainty: Robust and Stochastic

Monimoy Bujarbaruah, Xiaojing Zhang, Marko Tanaskovic +1

This paper deals with the problem of formulating an adaptive Model Predictive Control strategy for constrained uncertain systems. We consider a linear system, in presence of bounde…

cs.LG20196 cited

Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks

Xiaojing Zhang, Monimoy Bujarbaruah, Francesco Borrelli

In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing appr…

math.OC2018

Robust Model Predictive Control with Adjustable Uncertainty Sets

Yeojun Kim, Xiaojing Zhang, Jacopo Guanetti +1

In this paper, we present Robust Model Predictive Control (MPC) problems with adjustable uncertainty sets. In contrast to standard Robust MPC problems with known uncertainty sets,…