SANDy — Safe Autonomy & Nonlinear Dynamics Lab

We established a framework to control autonomous systems – robotic systems and vehicles – with formal safety guarantees based on reduced-order models. This approach has been implemented on several real-world systems: fixed-wing aircraft, quadrotors, wheeled robots, legged robots, manipulator, and heavy-duty truck.

[J37]	Gacsi et al., Activated Backstepping with Control Barrier Functions for the Safe Navigation of Automated Vehicles, LCSS 2025
[P36]	Cohen et al., Safety-Critical Controller Synthesis with Reduced-Order Models, ACC 2025
[J32]	Cohen et al., Safety-critical control for autonomous systems: Control barrier functions via reduced-order models, ARC 2024
[P28]	Taylor et al., Safe Backstepping with Control Barrier Functions, CDC 2022

We developed controllers for guaranteeing safety in the presence of real-life uncertainties and disturbances. We implemented this method on various mechanical system models, including road vehicles and spacecraft.

[J36]	Alan et al., Generalizing Robust Control Barrier Functions From a Controller Design Perspective, OJCSYS 2025
[J34]	van Wijk et al., Disturbance-Robust Backup Control Barrier Functions: Safety Under Uncertain Dynamics, LCSS 2024
[J24]	Alan et al., Parameterized Barrier Functions to Guarantee Safety Under Uncertainty, LCSS 2023
[J20]	Alan et al., Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions, LCSS 2023

We proposed a framework to guarantee safety on robotic systems while satisfying the intention of a human operator, through a learning-based switching law between multiple backup controllers. We implemented this method on a dual-track robot.

[P32]

Janwani et al., A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions, ICRA 2024

In collaboration with Dow Chemical, we designed motion plans for quadrupedal robots that allow them to operate in chemical facilities, such as climb up and down in distillation columns.

[P31]	Lee et al., Safety-Critical Control of Quadrupedal Robots with Rolling Arms for Autonomous Inspection of Complex Environments, ICRA 2024
[J22]	Molnar et al., Mechanical Design, Planning, and Control for Legged Robots in Distillation Columns, JCND 2023

In collaboration with Miso Robotics, we depeloped a safety-critical control algorithm that allows collision-free cooking with robotic manipulators by minimally modifying existing motion plans.

[J18]

Singletary et al., Safety-Critical Manipulation for Collision-Free Food Preparation, RAL/IROS 2022

We established a framework for safety-critical control of robotic systems based on reduced-order kinematics. In case of trivial kinematic equations, this leads to model-free control. We implemented the method on legged, wheeled and flying robots to execute obstacle avoidance tasks.

[P29]	Molnar et al., Safety-Critical Control with Bounded Inputs via Reduced Order Models, ACC 2023
[J17]	Molnar et al., Model-Free Safety-Critical Control for Robotic Systems, RAL/ICRA 2022

We used preference-based learning to tune robust safety-critical controllers for performant and provably safe behavior. We implemented this method on a quadruped that is controlled by using on-board cameras, with robustness to disturbances and perception errors.

[P26]

Cosner et al., Safety-Aware Preference-Based Learning for Safety-Critical Control, L4DC 2022

We used self-supervised learning to estimate the uncertainty of perception modules, such as stereo vision, and used this uncertainty estimate for robust safety-critical control.

[P24]

Cosner et al., Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision, ICRA 2022

We developed a control method to safely transition between various motion primitives (such as lie, stand, walk or jump behaviors) on legged robots. We implemented this approach on a quadruped.

[P23]

Ubellacker et al., Verifying Safe Transitions between Dynamic Motion Primitives on Legged Robots, IROS 2021

We established safety-critical controllers that are robust to perception errors, and we implemented them to execute obstacle avoidance tasks by a Segway using on-board cameras.

[P22]

Cosner et al., Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State, IROS 2021

We wrote a textbook on the topics of human driving behavior, longitudinal dynamics and control of automated vehicles, connected cruise control, and experiments with connected automated vehicles.

In collaboration with Navistar, we depeloped longitudinal controllers for safe and energy-efficient driving of connected automated trucks. The controllers leverage road elevation data and information about preceding vehicles from on-board sensors and vehicle-to-vehicle (V2V) connectivity. We tested the proposed controllers in experiments using a full-scale heavy-duty vehicle, both on a closed test track and on public highways.

[J31]	Alan et al., Integrating Safety with Performance in Connected Automated Truck Control: Experimental Validation, IEEE IV 2024
[P12]	He et al., Improving fuel economy of heavy-duty vehicles in daily driving, ACC 2020

We introduced connected cruise control (CCC) strategies by which connected automated vehicles can regulate their logitudinal motions while responding to multiple vehicles ahead of them. We studied stability and smoothness of driving behavior, the effect of communication, feedback and actuation delays, energy-efficiency and safety.

[P34]	Chen et al., Safety-Critical Connected Cruise Control: Leveraging Connectivity for Safe and Efficient Longitudinal Control of Automated Vehicles, ITSC 2024
[P30]	Molnar et al., On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions, CDC 2023
[J30]	Shen et al., Energy-efficient Reactive and Predictive Connected Cruise Control, IEEE IV 2024
[P27]	Molnar et al., Input-to-State Safety with Input Delay in Longitudinal Vehicle Control, TDS 2022
[J08]	Molnár et al., Application of predictor feedback to compensate time delays in connected cruise control, IEEE ITS 2018

We studied the behavior of mixed traffic systems where human drivers and connected automated vehicles (CAVs) coexist. By leveraging connectivity, CAVs can regulate their motions such that they make the overall traffic flow smoother, that ultimately mitigates traffic jams and improves mobility.

[J33]	Molnar et al., Destroying Phantom Jams with Connectivity and Automation: Nonlinear Dynamics and Control of Mixed Traffic, TRSC 2024
[J27]	Guo et al., Connected Cruise and Traffic Control for Pairs of Connected Automated Vehicles, IEEE ITS 2023
[J25]	Zhao et al., Safety-critical traffic control by connected automated vehicles, TRC 2023
[C02]	Molnár et al., Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles, In AI-enabled Technologies for Autonomous and Connected Vehicles 2022

In collaboration with Ford, we depeloped an on-board traffic prediction method that provides speed previews for vehicles via vehicle-to-vehicle (V2V) connectivity. Prediction was implemented on a physical vehicle and tested in real highway traffic.

[J15]	Molnár et al., Delayed Lagrangian Continuum Models for On-Board Traffic Prediction, TRC 2021
[P25]	Molnár et al., On-Board Traffic Prediction for Connected Vehicles: Implementation and Experiments on Highways, ACC 2022

We developed a control framework to achieve formal guarantees of safe behavior for control systems that have time delays in their control loops. The frameworks covers systems with input delays and state delays.

[J26]	Kiss et al., Control barrier functionals: Safety-critical control for time delay systems, IJRNC 2023
[J23]	Molnar et al., Safety-Critical Control with Input Delay in Dynamic Environment, TCST 2023
[P27]	Molnar et al., Input-to-State Safety with Input Delay in Longitudinal Vehicle Control, TDS 2022
[P18]	Kiss et al., Certifying Safety for Nonlinear Time Delay Systems via Safety Functionals: A Discretization Based Approach, ACC 2021

We established controllers for epidemiological models that describe the spread of Covid-19. These controllers give guidance on the required human intervention for mitigating the spread of the virus and for providing safety with respect to the level of infection or hospitalization. Click on the image for a press release of our work.

[J14]	Molnár et al., Safety-Critical Control of Compartmental Epidemiological Models With Measurement Delays, LCSS 2021
[J13]	Ames et al., Safety-Critical Control of Active Interventions for COVID-19 Mitigation, IEEE Access 2020

We studied the behavior of delayed dynamical models describing machine tool vibrations in metal cutting processes such as turning and milling. Through stability and bifurcation analysis, we identified those choices of technological parameters that enable manufacturing engineers to avoid harmful vibrations (chatter) during the cutting process.

[J11]	Molnar et al., Experimental investigation of dynamic chip formation in orthogonal cutting, IJMTM 2019
[J10]	Molnar et al., Closed-form estimations of the bistable region in metal cutting via the method of averaging, IJNM 2019
[J07]	Molnar et al., On the analysis of the double Hopf bifurcation in machining processes via centre manifold reduction, PRSA 2017
[J06]	Molnár et al., Extension of process damping to milling with low radial immersion, IJAMT 2017
[J03]	Molnár et al., Estimation of the bistable zone for machining operations for the case of a distributed cutting-force model, JCND 2016
[J02]	Molnár et al., State-dependent distributed-delay model of orthogonal cutting, Nonlinear Dynamics 2016

We investigated predictor feeback control techniques to overcome the detrimental effects of time delays in control loops (i.e., sensory, communication, feedback and actuation delays). We leveraged these methods in the context of controlling connected automated vehicles, epidemiological models and safety-critical time delay systems.

[C01]	Molnar et al., The Smith predictor, the modified Smith predictor and the finite spectrum assignment: A comparative study, In Stability, control and application of time-delay systems 2019
[J04]	Molnar et al., On the robust stabilizability of unstable systems with feedback delay by finite spectrum assignment, JVC 2016