arXiv subjects

Nathan van de Wouw

Publications and source records attributed to Nathan van de Wouw.

At least 19 recordsLinked to original sources

arXiv2026

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.

eess.SY↗

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.

eess.SY↗

arXiv2026

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.

eess.SY↗

arXiv2026

Generalizing Reduced Rank Extrapolation to Low-Rank Matrix Sequences

Reduced rank extrapolation (RRE) is an acceleration method typically used to accelerate the iterative solution of nonlinear systems of equations using a fixed-point process. In this context, the iterates are vectors generated from a fixed-point mapping function. However, when considering the iterative solution of large-scale matrix equations, the iterates are low-rank matrices generated from a fixed-point process for which, generally, the mapping function changes in each iteration. To enable acceleration of the iterative solution for these problems, we propose two novel generalizations of RRE. First, we show how to effectively compute RRE for sequences of low-rank matrices. Second, we derive a formulation of RRE that is suitable for fixed-point processes for which the mapping function changes each iteration. We demonstrate the potential of the methods on several numerical examples involving the iterative solution of large-scale Lyapunov and Riccati matrix equations.

math.NA↗

arXiv2026

Confidentiality of Linear Control Systems with Quadratic Output Under Sensor Attacks [Extended Version]

We study the state estimation problem for linear control systems with quadratic outputs which are locally unobservable at the equilibrium. We show that, despite this inherent lack of observability, an adversary with sensor read and write capability can induce observability by injecting an appropriate signal into the measurement channel. Taking the role of an adversary, we characterize when an injected signal can or cannot induce observability and, in the successful case, construct an observer that achieves local exponential convergence of state estimates to the true states of the system. A simulation study demonstrates our results.

math.OC↗

arXiv2026

Uncertainty-based perturb and observe for data-driven optimization

Data-based adaptive optimization methods hold great promise for the performance optimization of uncertain, time-varying processes. However, current methods are often based on continuous perturbation which is in general undesired for real-life (e.g., industrial) applications. In this paper, a new uncertainty-based perturb-and-observe method is developed that addresses this limitation and reduces the required number of perturbations, while retaining the capability to track time-varying optima. The method is based on the philosophy of `only perturbing when needed,' and is shown to converge to the optimum under mild conditions. A simulation-based case study on a photo-voltaic solar array demonstrates that it can outperform the standard perturb and observe approach as well as three other data-based optimization methods.

eess.SY↗

arXiv2026

Considering Perspectives for Automated Driving Ethics: Collective Risk in Vehicular Motion Planning

Recent automated vehicle (AV) motion planning strategies evolve around minimizing risk in road traffic. However, they exclusively consider risk from the AV's perspective and, as such, do not address the ethicality of its decisions for other road users. We argue that this does not reduce the risk of each road user, as risk may be different from the perspective of each road user. Indeed, minimizing the risk from the AV's perspective may not imply that the risk from the perspective of other road users is also being minimized; in fact, it may even increase. To test this hypothesis, we propose an AV motion planning strategy that supports switching risk minimization strategies between all road user perspectives. We find that the risk from the perspective of other road users can generally be considered different to the risk from the AV's perspective. Taking a collective risk perspective, i.e., balancing the risks of all road users, we observe an AV that minimizes overall traffic risk the best, while putting itself at slightly higher risk for the benefit of others, which is consistent with human driving behavior. In addition, adopting a collective risk minimization strategy can also be beneficial to the AV's travel efficiency by acting assertively when other road users maintain a low risk estimate of the AV. Yet, the AV drives conservatively when its planned actions are less predictable to other road users, i.e., associated with high risk. We argue that such behavior is a form of self-reflection and a natural prerequisite for socially acceptable AV behavior. We conclude that to facilitate ethicality in road traffic that includes AVs, the risk-perspective of each road user must be considered in the decision-making of AVs.

cs.RO↗

arXiv2026

Collision Probability Estimation for Optimization-based Vehicular Motion Planning

Many motion planning algorithms for automated driving require estimating the probability of collision (POC) to account for uncertainties in the measurement and estimation of the motion of road users. Common POC estimation techniques often utilize sampling-based methods that suffer from computational inefficiency and a non-deterministic estimation, i.e., each estimation result for the same inputs is slightly different. In contrast, optimization-based motion planning algorithms require computationally efficient POC estimation, ideally using deterministic estimation, such that typical optimization algorithms for motion planning retain feasibility. Estimating the POC analytically, however, is challenging because it depends on understanding the collision conditions (e.g., vehicle's shape) and characterizing the uncertainty in motion prediction. In this paper, we propose an approach in which we estimate the POC between two vehicles by over-approximating their shapes by a multi-circular shape approximation. The position and heading of the predicted vehicle are modelled as random variables, contrasting with the literature, where the heading angle is often neglected. We guarantee that the provided POC is an over-approximation, which is essential in providing safety guarantees. For the particular case of Gaussian uncertainty in the position and heading, we present a computationally efficient algorithm for computing the POC estimate. This algorithm is then used in a path-following stochastic model predictive controller (SMPC) for motion planning. With the proposed algorithm, the SMPC generates reproducible trajectories while the controller retains its feasibility in the presented test cases and demonstrates the ability to handle varying levels of uncertainty.

cs.RO↗

arXiv2026

Risk Estimation for Automated Driving

Safety is a central requirement for automated vehicles. As such, the assessment of risk in automated driving is key in supporting both motion planning technologies and safety evaluation. In automated driving, risk is characterized by two aspects. The first aspect is the uncertainty on the state estimates of other road participants by an automated vehicle. The second aspect is the severity of a collision event with said traffic participants. Here, the uncertainty aspect typically causes the risk to be non-zero for near-collision events. This makes risk particularly useful for automated vehicle motion planning. Namely, constraining or minimizing risk naturally navigates the automated vehicle around traffic participants while keeping a safety distance based on the level of uncertainty and the potential severity of the impending collision. Existing approaches to calculate the risk either resort to empirical modeling or severe approximations, and, hence, lack generalizability and accuracy. In this paper, we combine recent advances in collision probability estimation with the concept of collision severity to develop a general method for accurate risk estimation. The proposed method allows us to assign individual severity functions for different collision constellations, such as, e.g., frontal or side collisions. Furthermore, we show that the proposed approach is computationally efficient, which is beneficial, e.g., in real-time motion planning applications. The programming code for an exemplary implementation of Gaussian uncertainties is also provided.

cs.RO↗

arXiv2025

Uncertainty-Based Perturb and Observe for Fast Optimization of Unknown, Time-Varying Processes

Model-free adaptive optimization methods are capable of optimizing unknown, time-varying processes even when other optimization methods are not. However, their practical application is often limited by perturbations that are used to gather information on the unknown cost and its gradient. The aim of this paper is to develop a perturb-and-observe (P&O) method that reduces the need for such perturbations while still achieving fast and accurate tracking of time-varying optima. To this end, a (time-varying) model of the cost is constructed in an online fashion, taking into account the uncertainty on the measured performance cost as well as the decreasing reliability of older measurements. Perturbations are only used when this is expected to lead to improved performance over a certain time horizon. Convergence conditions are provided under which the strategy converges to a neighborhood of the optimum. Finally, simulation results demonstrate that uncertainty-based P\&O can reduce the number of perturbations significantly while still tracking a time-varying optimum accurately.

eess.SY↗

arXiv2025

Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks

Robust Recurrent Neural Networks (R-RENs) are a class of neural networks that have built-in system-theoretic robustness and incremental stability properties. In this manuscript, we leverage these properties to construct a data-driven Fault Detection and Isolation (FDI) method for sensor faults with proven performance guarantees. The underlying idea behind the scheme is to construct a bank of multiple R-RENs (acting as fault isolation filters), each with different levels of sensitivity (increased or decreased) to faults at different sensors. That is, each R-REN is designed to be specifically sensitive to faults occurring in a particular sensor and robust against faults in all the others. The latter is guaranteed using the built-in incremental stability properties of R-RENs. The proposed method is unsupervised (as it does not require labeled data from faulty sensors) and data-driven (because it exploits available fault-free input-output system trajectories and does not rely on dynamic models of the system under study). Numerical simulations on a roll-plane model of a vehicle demonstrate the effectiveness and practical applicability of the proposed methodology.

eess.SY↗

arXiv2025

Data-Efficient Extremum-Seeking Control Using Kernel-Based Function Approximation

Existing extremum-seeking control (ESC) approaches typically rely on applying repeated perturbations to input parameters and performing measurements of the corresponding performance output. The required separation between the different timescales in the ESC loop means that performing these measurements can be time-consuming. Moreover, performing these measurements can be costly in practice, e.g., due to the use of resources. With these challenges in mind, it is desirable to reduce the number of measurements needed to optimize performance. Therefore, in this work, we present a sampled-data ESC approach aimed at reducing the number of measurements that need to be performed. In the proposed approach, we use input-output data obtained during regular operation of the extremum-seeking controller to construct online an approximation of the system's underlying cost function. By using this approximation to perform parameter updates when a decrease in the cost can be guaranteed, instead of performing additional measurements to perform this update, we make more efficient use of data collected during regular operation of the extremum-seeking controller. As a result, we indeed obtain a reduction in the required number of measurements to achieve optimization. We provide a stability analysis of the novel sampled-data ESC approach, and demonstrate the benefits of the synergy between kernel-based function approximation and standard ESC in simulation on a multi-input dynamical system.

eess.SY↗

arXiv2025

Efficient Reduction of Interconnected Subsystem Models using Abstracted Environments

We present two frameworks for structure-preserving model order reduction of interconnected subsystems, improving tractability of the reduction methods while ensuring stability and accuracy bounds of the reduced interconnected model. Instead of reducing each subsystem independently, we take a low-order abstraction of its environment into account to better capture the dynamics relevant to the external input-output behaviour of the interconnected system, thereby increasing accuracy of the reduced interconnected model. This approach significantly reduces the computational costs of reduction by abstracting instead of fully retaining the environment. The two frameworks differ in how they generate these abstracted environments: one abstracts the environment as a whole, whereas the other abstracts each individual subsystem. By relating low-level errors introduced by reduction and abstraction to the resulting high-level error on the interconnected system, we are able to translate high-level accuracy requirements (on the reduced interconnected system) to low-level specifications (on abstraction and reduction errors) using techniques from robust performance analysis. By adhering to these low-level specifications, restricting the introduced low-level errors, both frameworks automatically guarantee the accuracy and stability of the reduced interconnected system. We demonstrate the effectiveness of both frameworks by applying them to a structural dynamics model of a two-stroke wafer stage, achieving improved accuracy and/or greater reduction compared to an existing method from literature.

eess.SY↗

arXiv2024

Hybrid Model-Data Fault Diagnosis for Wafer Handler Robots: Tilt and Broken Belt Cases

This work proposes a hybrid model- and data-based scheme for fault detection, isolation, and estimation (FDIE) for a class of wafer handler (WH) robots. The proposed hybrid scheme consists of: 1) a linear filter that simultaneously estimates system states and fault-induced signals from sensing and actuation data; and 2) a data-driven classifier, in the form of a support vector machine (SVM), that detects and isolates the fault type using estimates generated by the filter. We demonstrate the effectiveness of the scheme for two critical fault types for WH robots used in the semiconductor industry: broken-belt in the lower arm of the WH robot (an abrupt fault) and tilt in the robot arms (an incipient fault). We derive explicit models of the robot motion dynamics induced by these faults and test the diagnostics scheme in a realistic simulation-based case study. These case study results demonstrate that the proposed hybrid FDIE scheme achieves superior performance compared to purely data-driven methods.

cs.RO↗

arXiv2024

Fault Isolation for the Ink Deposition Process in High-End Industrial Printers

This paper presents a mathematical framework for modeling the dynamic effects of three fault categories and six fault variants in the ink channels of high-end industrial printers. It also introduces a hybrid approach that combines model-based and data-based methods to detect and isolate these faults effectively. A key challenge in these systems is that the same piezo device is used for actuation (generating ink droplets) and for sensing and, as a consequence, sensing is only available when there is no actuation. The proposed Fault Detection (FD) filter, based on the healthy model, uses the piezo self-sensing signal to generate a residual, while taking the above challenge into account. The system is flagged as faulty if the residual energy exceeds a threshold. Fault Isolation (FI) is achieved through linear regression or a k-nearest neighbors approach to identify the most likely fault category and variant. The resulting hybrid Fault Detection and Isolation (FDI) method overcomes traditional limitations of model-based methods by isolating different types of faults affecting the same entries (i.e., equations) in the ink channel dynamics. Moreover, it is shown to outperform purely data-driven methods in fault isolation, especially when data is scarce. Experimental validation demonstrates superior FDI performance compared to state-of-the-art methods.

eess.SY↗

arXiv2024

Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning

Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. However, it has been shown that despite FL's partial protection of local data privacy, information about clients' data can still be inferred from shared model updates during training. In recent years, several privacy-preserving approaches have been developed to mitigate this privacy leakage in FL, though they often provide privacy at the cost of model performance or system efficiency. Balancing these trade-offs presents a significant challenge in implementing FL schemes. In this manuscript, we introduce a privacy-preserving FL framework that combines differential privacy and system immersion tools from control theory. The core idea is to treat the optimization algorithms used in standard FL schemes (e.g., gradient-based algorithms) as a dynamical system that we seek to immerse into a higher-dimensional system (referred to as the target optimization algorithm). The target algorithm's dynamics are designed such that, first, the model parameters of the original algorithm are immersed in its parameters; second, it operates on distorted parameters; and third, it converges to an encoded version of the true model parameters from the original algorithm. These encoded parameters can then be decoded at the server to retrieve the original model parameters. We demonstrate that the proposed privacy-preserving scheme can be tailored to offer any desired level of differential privacy for both local and global model parameters, while maintaining the same accuracy and convergence rate as standard FL algorithms.

cs.CR↗

arXiv2024

Abstracted Model Reduction: A General Framework for Efficient Interconnected System Reduction

This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected models, it is usually not sufficient to consider the subsystems separately. Instead, structure-preserving reduction methods should be employed, which consider the interconnected dynamics to select which subsystem dynamics to retain in reduction. However, structure-preserving methods are often not computationally tractable. To overcome this issue, we propose to connect each subsystem model to a low-order abstraction of its environment to reduce it both effectively and efficiently. By means of a high-fidelity structural-dynamics model from the lithography industry, we show, on the one hand, significantly increased accuracy with respect to standard subsystem reduction and, on the other hand, similar accuracy to direct application of expensive structure-preserving methods, while significantly reducing computational cost. Furthermore, we formulate a systematic approach to automatically determine sufficient abstraction and reduction orders to preserve stability and guarantee a given frequency-dependent error specification. We apply this approach to the lithography equipment use case and show that the environment model can indeed be reduced by over 80\% without significant loss in the accuracy of the reduced interconnected model.

eess.SY↗

arXiv2024

Impact-Aware Control using Time-Invariant Reference Spreading

With the goal of increasing the speed and efficiency in robotic manipulation, a control approach is presented that aims to utilize intentional simultaneous impacts to its advantage. This approach exploits the concept of the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These vector fields are coupled via an impact model in proximity of the expected impact area, minimizing the otherwise large impact-induced velocity errors and control efforts. We show how a nonsmooth physics engine can be used to construct this impact model for complex scenarios, which warrants applicability to a large range of possible impact states without requiring contact stiffness and damping parameters. In addition, a novel interim-impact control mode provides robustness in the execution against the inevitable lack of exact impact simultaneity and the corresponding unreliable velocity error during the time when contact is only partially established. This interim mode uses a position feedback signal that is derived from the ante-impact velocity reference to promote contact completion, and smoothly transitions into the post-impact mode. An experimental validation of time-invariant reference spreading control is presented for the first time through a set of 600 robotic hit-and-push and dual-arm grabbing experiments.

cs.RO↗