About

Ricardo G. Sanfelice is a professor specializing in hybrid dynamical systems, cyber-physical systems, and control theory. His research focuses on the modeling, analysis, and control of hybrid systems, which combine continuous and discrete dynamics. He has made significant contributions to hybrid feedback control, hybrid model predictive control, and the stability and robustness of hybrid systems. Sanfelice's work encompasses both theoretical foundations and practical applications, including observer design for hybrid systems and control barrier functions for attack recovery with provable guarantees. He has authored and co-authored numerous books, book chapters, journal articles, and conference papers that address various aspects of hybrid control systems, nonlinear control, and cyber-physical systems. His academic background includes a PhD thesis on robust hybrid control systems from the University of California, Santa Barbara, and a BS thesis on current control for AC motors from Universidad Nacional de Mar del Plata. Sanfelice's research integrates computational methods, control design, and system stability to advance the understanding and implementation of hybrid dynamical systems in engineering.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Control engineering
• Mathematics
• Applied mathematics
• Law
• Physics
• Environmental science
• Mathematical analysis

Selected publications

• Control Barrier Function based Attack-Recovery with Provable Guarantees

  IEEE Transactions on Automatic Control · 2026-01-01

  preprintOpen access

  This paper investigates security guarantees for cyber-physical systems (CPS) against actuator attacks. We in troduce a new attack detection mechanism based on zeroing control barrier function (ZCBF) conditions. We propose an adaptive recovery mechanism that responds based on the system's proximity to safety violations. Our attack-detection mechanism has been proven to be sound, meaning it consistently detects adversarial attacks without any false negatives. Additionally, we propose a novel hybrid control law that addresses delays in attack detection and prevents Zeno behavior. We also propose a sampling-based method to verify whether a set is a viability domain for CPS. Finally, we employ a Quadratic Programming (QP) approach for synthesizing control laws for the hybrid control policy, utilizing the viability domain to ensure safety in the presence of adversarial attacks on system actuators. The efficacy of the proposed method is demonstrated in a simulation case study involving a quadrotor system.

  PublisherOA PDFDOI

• HyRNN: Hybrid Recurrent Neural Networks for Approximating Hybrid Dynamical Systems

  Proceedings of the AAAI Conference on Artificial Intelligence · 2026-03-14

  articleOpen access1st authorCorresponding

  For a class of hybrid dynamical systems, we show that a recurrent neural network with hybrid dynamics, which we refer to as a hybrid dynamic recurrent neural network (HyRNN), can be constructed to approximate solutions to hybrid systems over bounded (hybrid) time horizons. Specifically, given a desired precision level, we show that a hybrid system with dynamics resembling those of recurrent neural networks for continuous-time and discrete-time systems can be designed so that, for each bounded hybrid time horizon, its solutions are close to the solutions to the given hybrid system. Through the use of universal approximation theorems, we show that the approximation result holds for traditional smooth activation functions, such as sigmoid and arctan, and that extensions to ReLU functions are possible, and characterize the complexity of the proposed HyRNN.

  PublisherOA PDFDOI

• Model Predictive Control of Hybrid Dynamical Systems

  IEEE Transactions on Automatic Control · 2026-01-01

  article1st authorCorresponding

  The problem of controlling hybrid dynamical systems using model predictive control (MPC) is formulated and sufficient conditions for asymptotic stability of a set are provided. Hybrid dynamical systems are modeled in terms of hybrid equations, involving a differential equation and a difference equation with inputs and constraints. The proposed hybrid MPC algorithm uses a suitable prediction and control horizon construction inspired by hybrid time domains. Structural properties of the hybrid optimization problem, its feasible set, and its value function are provided. Checkable conditions to guarantee asymptotic stability of a set are provided. These conditions are given in terms of properties on the stage cost, terminal cost, and the existence of static state-feedback laws, related through a control Lyapunov function condition. Examples illustrate the results throughout the paper.

  PublisherDOI

• Autonomous Satellite Rendezvous via Hybrid Feedback Optimization

  Open MIND · 2026-02-24

  preprintSenior author

  As satellites have proliferated, interest has increased in autonomous rendezvous, proximity operations, and docking (ARPOD). A fundamental challenge in these tasks is the uncertainties when operating in space, e.g., in measurements of satellites' states, which can make future states difficult to predict. Another challenge is that satellites' onboard processors are typically much slower than their terrestrial counterparts. Therefore, to address these challenges we propose to solve an ARPOD problem with feedback optimization, which computes inputs to a system by measuring its outputs, feeding them into an optimization algorithm in the loop, and computing some number of iterations towards an optimal input. We focus on satellite rendezvous, and satellites' dynamics are modeled using the continuous-time Clohessy-Wiltshire equations, which are marginally stable. We develop an asymptotically stabilizing controller for them, and we use discrete-time gradient descent in the loop to compute inputs to them. Then, we analyze the hybrid feedback optimization system formed by the stabilized Clohessy-Wiltshire equations with gradient descent in the loop. We show that this model is well-posed and that maximal solutions are both complete and non-Zeno. Then, we show that solutions converge exponentially fast to a ball around a rendezvous point, and we bound the radius of that ball in terms of system parameters. Simulations show that this approach provides up to a 98.4\% reduction in the magnitude of disturbances across a range of simulations, which illustrates the viability of hybrid feedback optimization for autonomous satellite rendezvous.

  DOI

• Distributed Nonconvex Optimization with Exponential Convergence Rate via Hybrid Systems Methods

  Journal of Optimization Theory and Applications · 2026-04-01

  articleOpen accessSenior author

  Abstract We present a hybrid systems framework for distributed multi-agent optimization in which agents execute computations in continuous time and communicate in discrete time. The optimization algorithm is analogous to a continuous-time form of parallelized coordinate descent. Agents implement an update-and-hold strategy in which gradients are computed at communication times and held constant during flows between communications. The completeness of solutions under these hybrid dynamics is established. Then, we prove that this system is globally exponentially stable to a minimizer of a possibly nonconvex, smooth objective function that satisfies the Polyak-Łojasiewicz (PL) condition. Simulation results are presented for three different applications and illustrate the convergence rates and the impact of initial conditions upon convergence.

  PublisherOA PDFDOI

• Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins

  ArXiv.org · 2026-05-16

  articleOpen access

  Digital twins (DTs) rely on continuous synchronization between physical systems and their virtual counterparts through online parameter estimation under uncertainty. In many practical settings, however, this task is challenged by low observability, weak excitation, nonlinear dynamics, and noisy or biased measurements. In this work, we develop a new mathematical framework that integrates Weighted Flow Matching (WFM) generative modeling with physics-informed nonlinear filtering to enhance parameter estimation in DTs. WFM relies on dynamic reweighting of training samples, which guides the generative model toward parameter regimes most informative of the evolving system state. This generative component is tightly coupled with a physics-informed filtering architecture based on the Unscented Kalman Filter (UKF), yielding a unified DT framework that combines data-driven probability transport with physically consistent state and parameter estimation. The effectiveness of the new integrated framework is demonstrated within a spacecraft DT architecture, where stable moment of inertia estimation is achieved under uncertain and noisy sensing, with significant performance improvements over established approaches such as Extended Kalman Filtering (EKF) and Ensemble Kalman Filtering (EnKF). These results highlight the potential of weighted generative modeling as a core mechanism for real-time DT synchronization in operational and mission-critical systems.

  PublisherOA PDF

• Lyapunov-like Descriptions of Strong Forward Invariance for Differential Inclusions

  SIAM Journal on Control and Optimization · 2026-01-13

  article
  PublisherDOI

• Conical transition graphs for analysis of asymptotic stability in hybrid dynamical systems

  Nonlinear Analysis Hybrid Systems · 2026-01-02

  articleSenior author
  PublisherDOI

• Autonomous Satellite Rendezvous via Hybrid Feedback Optimization

  arXiv (Cornell University) · 2026-02-24

  articleOpen accessSenior author

  As satellites have proliferated, interest has increased in autonomous rendezvous, proximity operations, and docking (ARPOD). A fundamental challenge in these tasks is the uncertainties when operating in space, e.g., in measurements of satellites' states, which can make future states difficult to predict. Another challenge is that satellites' onboard processors are typically much slower than their terrestrial counterparts. Therefore, to address these challenges we propose to solve an ARPOD problem with feedback optimization, which computes inputs to a system by measuring its outputs, feeding them into an optimization algorithm in the loop, and computing some number of iterations towards an optimal input. We focus on satellite rendezvous, and satellites' dynamics are modeled using the continuous-time Clohessy-Wiltshire equations, which are marginally stable. We develop an asymptotically stabilizing controller for them, and we use discrete-time gradient descent in the loop to compute inputs to them. Then, we analyze the hybrid feedback optimization system formed by the stabilized Clohessy-Wiltshire equations with gradient descent in the loop. We show that this model is well-posed and that maximal solutions are both complete and non-Zeno. Then, we show that solutions converge exponentially fast to a ball around a rendezvous point, and we bound the radius of that ball in terms of system parameters. Simulations show that this approach provides up to a 98.4\% reduction in the magnitude of disturbances across a range of simulations, which illustrates the viability of hybrid feedback optimization for autonomous satellite rendezvous.

  PublisherOA PDF

• A Hybrid Model Predictive Control Framework for Docking and Stabilization of Composite Rigid Spacecraft Dynamics

  2026-01-08

  article

  This paper presents a hybrid model predictive control (MPC)–based framework for the rendezvous and docking of a deputy spacecraft and a passively tumbling chief on a circular orbit. Both spacecraft are modeled as rigid bodies with coupled translational and rotational dynamics. The objective is to safely guide the deputy spacecraft to dock with the chief spacecraft, and stabilize the motion of the composite rigid body. Depending on the proximity between these spacecraft, the overall control problem is partitioned into two phases—1) capture, and 2) post-capture phase—each with unique control objectives, dynamical models, and constraints. During the capture phase, unanticipated low velocity collision contacts between the spacecraft are taken into consideration, while in the post-capture phase, simultaneous stabilization of the tumbling motion and guiding the composite system to a predetermined parking orbit are achieved. Simulation results are reported to demonstrate the effectiveness of the proposed control framework.

  PublisherDOI

Recent grants

• Collaborative Research: CPS: Medium: Constraint Aware Planning and Control for Cyber-Physical Systems

  NSF · $616k · 2020–2025

• CAREER: Enabling Design of Future Smart Grids via Input/Output Hybrid Systems Tools

  NSF · $400k · 2012–2014

• CAREER: Enabling Design of Future Smart Grids via Input/Output Hybrid Systems Tools

  NSF · $328k · 2014–2018

• CPS: Synergy: Collaborative Research: Computationally Aware Cyber-Physical Systems

  NSF · $432k · 2015–2020

• Hybrid Predictive Control for Distributed Multi-agent Systems

  NSF · $415k · 2017–2021

Frequent coauthors

• Warren E. Dixon

  University of Florida

  272 shared

• Lorenzo Marconi

  University of Bologna

  270 shared

• João P. Hespanha

  266 shared

• Luca Zaccarian

  265 shared

• Faryar Jabbari

  University of California, Irvine

  264 shared

• Andrew G. Alleyne

  University of Minnesota

  264 shared

• Hitay Özbay

  Bilkent University

  264 shared

• Robert R. Bitmead

  University of California, San Diego

  264 shared

Awards & honors

• 2013 SIAM Control and Systems Theory Prize
• National Science Foundation CAREER award
• Air Force Young Investigator Research Award
• 2010 IEEE Control Systems Magazine Outstanding Paper Award
• 2012 STAR Higher Education Award for his contributions to ST…