Research Highlights

Our research focuses on nonlinear dynamics and control, safety-critical control, and time delay systems with applications to connected automated vehicles, and robotic and autonomous systems.

Robotic Systems
Connected Automated Vehicles
Time Delay Systems

Robotic Systems

Safety-critical Control via Reduced-order Models

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. A summary article is below.

[J32]   Cohen et al., Safety-critical control for autonomous systems: Control barrier functions via reduced-order models, ARC 2024

Model-free Safety on Robotic Systems

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

Connected Automated Vehicles

Connected Heavy-duty Trucks

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

Control of Connected Automated Vehicles

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.

[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
[J21]   Shen et al., Energy-Efficient Connected Cruise Control With Lean Penetration of Connected Vehicles, IEEE ITS 2023
[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

Traffic Control by Connected Automated Vehicles

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.

[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
[P14]   Molnár et al., Open and closed loop traffic control by connected automated vehicles, CDC 2020

Traffic Prediction for Connected Vehicles

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

Time Delay Systems

Safety of Time Delay Systems

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

Control of Epidemiological Models

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

Machine Tool Vibrations

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

Predictor Feedback Control

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