Francesco Borrelli

About

Professor Francesco Borrelli leads the Model Predictive Control (MPC) Lab at the University of California Berkeley. The lab focuses on both the theoretical and real-time implementation aspects of constrained predictive model-based control. Their research encompasses linear, nonlinear, and hybrid systems applied to both small-scale and complex large-scale problems. The lab's work includes discovering fundamental theoretical results, developing novel control algorithms, and experimentally validating these methods in collaboration with industry and academic partners. The research spans a wide range of systems, including automotive applications, process industries, and robotics, addressing several full-scale industrial challenges.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Automotive engineering
• Mathematical optimization
• Machine Learning
• Computer Security
• Mathematics
• Real-time computing
• Algorithm

Selected publications

• Optimization-Based Collision Avoidance

  IEEE Transactions on Control Systems Technology · 2020 · 401 citations

  Senior authorCorresponding

  This article presents a novel method for exactly reformulating nondifferentiable collision avoidance constraints into smooth, differentiable constraints using strong duality of convex optimization. We focus on a controlled object whose goal is to avoid obstacles while moving in an n-dimensional space. The proposed reformulation is exact, does not introduce any approximations, and applies to general obstacles and controlled objects that can be represented as the union of convex sets. We connect o…

  DOI

• Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections

  IEEE Internet of Things Journal · 2020 · 224 citations

  This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probabil…

  DOI

• Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks

  IEEE Robotics and Automation Letters · 2020 · 90 citations

  Reinforcement learning (RL) for robotics is challenging due to the difficulty in hand-engineering a dense cost function, which can lead to unintended behavior, and dynamical uncertainty, which makes exploration and constraint satisfaction challenging. We address these issues with a new model-based reinforcement learning algorithm, Safety Augmented Value Estimation from Demonstrations (SAVED), which uses supervision that only identifies task completion and a modest set of suboptimal demonstration…

  DOI

• Electric Vehicles for Smart Buildings: A Survey on Applications, Energy Management Methods, and Battery Degradation

  Proceedings of the IEEE · 2020 · 69 citations

  Plug-in electric vehicles (PEVs) have the highest promise for dramatically reducing transportation emissions. No other option has comparable emission reduction potential or as a promising pathway. Still, PEVs can offer more than green transportation. In particular, their onboard storage can further serve the society by providing an energy buffer to increase the reliability, affordability, and sustainability of electric services. These benefits are only achievable by fully exploiting the multifac…

  DOI

• Improving Urban Traffic Throughput With Vehicle Platooning: Theory and Experiments

  IEEE Access · 2020 · 46 citations

  In this paper we present a model-predictive control (MPC) based approach for vehicle platooning in an urban traffic setting. Our primary goal is to demonstrate that vehicle platooning has the potential to significantly increase throughput at intersections, which can create bottlenecks in the traffic flow. To do so, our approach relies on vehicle connectivity: vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In particular, we introduce a customized V2V message set which…

  DOI

Recent grants

• CPS: Synergy: Provably Safe Automotive Cyber-Physical Systems with Humans-in-the-Loop

  NSF · $1.1M · 2012–2015

• CPS:Medium:Collaborative Research: Safe Learning in Co-robots--Theory, Experiments and Education

  NSF · $1.2M · 2019–2023

• CPS:Medium: High Confidence Active Safety Control in Automotive Cyber-Physical Systems

  NSF · $1.4M · 2009–2012

Frequent coauthors

• Ugo Rosolia

  68 shared

• Monimoy Bujarbaruah

  University of California, Berkeley

  61 shared

• Jacopo Guanetti

  52 shared

• Alberto Bemporad

  IMT School for Advanced Studies Lucca

  39 shared

• Manfred Morari

  University of Pennsylvania

  38 shared

• H. Eric Tseng

  33 shared

• Siddharth H. Nair

  University of California, Berkeley

  32 shared

• Yeojun Kim

  30 shared

Awards & honors

• 2009 NSF CAREER Award
• 2012 IEEE Control System Technology Award
• IEEE Fellow (2016)
• Industrial Achievement Award by the International Federation…

Similar researchers at University of California, Berkeley

• Liwei Linh-99
• Costas Grigoropoulosh-80
• Michael Frenklachh-70