arXiv subjects

Erjen Lefeber

Publications and source records attributed to Erjen Lefeber.

10 recordsLinked to original sources

arXiv2026

Improving Cooperative Adaptive Cruise Control Robustness to Parametric Uncertainty via Plant Equivalent Controller Realizations

Cooperative Adaptive Cruise Control (CACC) enables vehicle platooning through inter-vehicle communication, improving traffic efficiency and safety. Conventional CACC relies on feedback linearization, assuming exact knowledge of vehicle parameters. However, the nonlinear longitudinal vehicle dynamics are subject to parametric uncertainty. Applying such feedback linearization with a nominal model yields imperfect cancellation, leading to model mismatch and degraded performance with off-the-shelf CACC controllers. To improve robustness without redesigning the CACC law, we explicitly model the mismatch between the ideal closed-loop dynamics, assumed by the CACC design, and the actual dynamics under parametric uncertainties. Robustness is formulated as an $\mathcal{L}_2$ trajectory-matching problem, minimizing the energy of this mismatch to make the uncertain system behave as closely as possible to the ideal model. This objective is addressed by optimizing over plant equivalent controller (PEC) realizations that preserve the nominal closed-loop behavior while mitigating the effects of parametric uncertainty. Stability and performance are enforced via LMIs, yielding a convex optimization problem applicable to heterogeneous platoons. Experimental results demonstrate improved robustness and performance under parametric uncertainty while preserving nominal behavior.

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arXiv2025

Plant Equivalent Controller Realizations for Attack-Resilient Cyber-Physical Systems

As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns. Among these, stealthy attacks, those that evade conventional detection mechanisms, pose a critical threat to closed-loop performance. This paper introduces a controller-oriented method to enhance CPS resiliency against such attacks without compromising nominal closed-loop behavior. Specifically, we propose the concept of plant equivalent controller (PEC) realizations, representing a class of dynamic output-feedback controllers that preserve the input-output behavior of a given base controller while exhibiting distinct robustness properties in the presence of disturbances and sensor attacks. To quantify and improve robustness, we employ reachable set analysis to assess the impact of stealthy attacks on the closed-loop dynamics. Building on this analysis, we provide mathematical tools (in terms of linear matrix inequalities) to synthesize the optimal PEC realization that minimizes the reachable set under peak-bounded disturbances. The proposed framework thus provides systematic analysis and synthesis tools to enhance the attack resilience of CPSs while maintaining the desired nominal performance. The effectiveness of the approach is demonstrated on the quadruple-tank process subject to stealthy sensor attacks.

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arXiv2025

Analysis and experimental validation of decentralized controllers for delayed spacing policies in vehicle-platooning

In this paper, a novel approach to spacing policies for vehicle platoons and a framework for control design is presented. Whereas traditional approaches aim to mitigate the effect of actuation and communication delays on the spacing error, this paper presents spacing policies that account for delays. A framework for decentralized control design is presented and necessary and sufficient conditions for tracking of delayed-based spacing policies are stated. It is shown that due to the control design, string stability is induced by the spacing policy. The results are supported by experimental validations of the theoretical results.

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arXiv2025

Self-Healing Hybrid Control as a Proxy for Detection and Mitigation of Sensor Attacks in Cooperative Driving

We propose a real-time hybrid controller scheme to detect and mitigate False-Data Injection (FDI) attacks on Cooperative Adaptive Cruise Control (CACC). Our method uses sensor redundancy to create equivalent controller realizations, each driven by distinct sensor subsets but producing identical control inputs when no attack occurs. By comparing control signals and measurements via majority voting, the scheme identifies compromised sensors in real-time and switches to a healthy controller, even under unconstrained attacker switching. The hybrid controller utilizes attack-dependent flow and jump sets, and resets the states of compromised controllers, resulting in a self-healing architecture. Simulation results demonstrate the effectiveness of this approach.

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arXiv2024

High-Performance Model Predictive Control for Quadcopters with Formal Stability Guarantees

In this paper, we present a novel cascade control structure with formal guarantees of uniform almost global asymptotic stability for the state tracking error dynamics of a quadcopter. The proposed approach features a model predictive control strategy for the outer loop, explicitly accounting for the non-zero total thrust constraint. The outer-loop controller generates an acceleration reference, which is then converted into attitude, angular velocity and acceleration references, subsequently tracked by a nonlinear inner-loop controller. The proposed cascade control strategy is validated through numerical case studies, underlying high-fidelity models, demonstrating its ability to track fast trajectories with small error.

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arXiv2024

Platooning of Heterogeneous Vehicles with Actuation Delays: Theoretical and Experimental Results

In this paper we present a prediction-based Cooperative Adaptive Cruise Controller for vehicles with actuation delay, applicable within heterogeneous platoons. We provide a stability analysis for the discrete-time implementation of this controller, which shows the effect of the used sampling times and can be used for selecting appropriate controller gains. The theoretical results are validated by means of experiments using full scale vehicles. This is an extended version of a paper with the same title (submitted to IFAC TDS 2024). Additional mathematical details are provided in this extended version.

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arXiv2024

Optimal Controller Realizations against False Data Injections in Cooperative Driving

To enhance the robustness of cooperative driving to cyberattacks, we study a controller-oriented approach to mitigate the effect of a class of False-Data Injection (FDI) attacks. By reformulating a given dynamic Cooperative Adaptive Cruise Control scheme (the base controller), we show that a class of new but equivalent controllers (base controller realizations) can represent the base controller. This controller class exhibits the same platooning behavior in the absence of attacks, but in the presence of attacks, their robustness varies with the realization. We propose a prescriptive synthesis framework where the base controller and the system dynamics are written in new coordinates via an invertible coordinate transformation on the controller state. Because the input-output behavior is invariant under coordinate transformations, the input-output behavior is unaffected (so controller realizations do not change the system's closed-loop performance). However, each controller realization may require a different combination of sensors. Subsequently, we obtain the optimal combination of sensors that minimizes the effect of FDI attacks by solving a linear matrix inequality while quantifying the FDI's attack impact through reachability analysis. Through simulation studies, we demonstrate that this approach enhances the robustness of cooperative driving without relying on a detection scheme and maintaining all system properties.

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arXiv2023

Impact Sensitivity Analysis of Cooperative Adaptive Cruise Control Against Resource-Limited Adversaries

Cooperative Adaptive Cruise Control (CACC) is a technology that allows groups of vehicles to form in automated, tightly-coupled platoons. CACC schemes exploit Vehicle-to-Vehicle (V2V) wireless communications to exchange information between vehicles. However, the use of communication networks brings security concerns as it exposes network access points that the adversary can exploit to disrupt the vehicles' operation and even cause crashes. In this manuscript, we present a sensitivity analysis of CACC schemes against a class of resource-limited attacks. We present a modelling framework that allows us to systematically compute outer ellipsoidal approximations of reachable sets induced by attacks. We use the size of these sets as a security metric to quantify the potential damage of attacks affecting different signals in a CACC-controlled vehicle and study how two key system parameters change this metric. We carry out a sensitivity analysis for two different controller implementations (as given the available sensors there is an infinite number of realizations of the same controller) and show how different controller realizations can significantly affect the impact of attacks. We present extensive simulation experiments to illustrate the results.

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arXiv2023

A Robust Observer with Gyroscopic Bias Correction for Rotational Dynamics

We propose an observer for rotational dynamics subject to directional and gyroscopic measurements, which simultaneously estimates the gyroscopic biases and attitude rates. We show uniform almost global asymptotic and local exponential stability of the resulting error dynamics, implying robustness against bounded disturbances. This robustness is quantified with respect to a popular nonlinear complementary filter in quantitative simulation studies, and we explore how the measurement noise propagates to the asymptotic errors as a function of tuning. This is an extended version of a paper with the same title (to appear at IFAC WC 2023). Additional mathematical details are provided in this extended version.

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arXiv2019

A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed samples of the uncertainty over the prediction horizon, we can give probabilistic guarantees on constraint satisfaction. Simulation results demonstrate that the scenario-based approach is able to avoid collisions in spite of uncertainty while the non-stochastic baseline controller is not.

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