arXiv subjects

Carlo Novara

Publications and source records attributed to Carlo Novara.

At least 19 recordsLinked to original sources

arXiv2026

Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields

This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections. The approach leverages a Finite Horizon Optimal Control Problem (FHOCP) that employs Time-Varying Artificial Potential Fields (TV-APF). By utilizing short-horizon motion prediction and a dedicated conflict-zone occupancy coefficient, the framework suitably accounts for potential collisions within the FHOCP. The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory. Simulation results in multi-vehicle traffic scenarios demonstrate the effectiveness of the approach.

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arXiv2026

A Finite-Gain Stability Approach to NMPC Design: the Extended Version

This paper proposes a novel approach to design of Nonlinear Model Predictive Control (NMPC) schemes based on Finite-Gain Stability (FGS) concepts. The proposed formulation considers the case where the plant is affected by unknown but bounded disturbances, which renders difficult the classical Lyapunov-based analysis/design. Based on FGS conditions for a closed-loop system, we develop a systematic NMPC design methodology, allowing us to choose the relevant NMPC parameters that lead to closed-loop FGS and provide a satisfactory tracking performance, also for the case of time-varying reference signals. A simulated example is presented to demonstrate the effectiveness of our framework, concerned with lateral/longitudinal control of an automated vehicle.

eess.SY↗

arXiv2026

EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling

Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth observation satellites (EOS) increase operational flexibility, they also significantly raise scheduling complexity. The lack of a unified, open-source benchmark makes it difficult to compare algorithms across studies. This paper introduces EOS-Bench, a comprehensive framework for systematic and reproducible evaluation of scheduling methods. By integrating high-fidelity orbital dynamics and platform constraints, EOS-Bench generates 1,390 scenarios and 13,900 benchmark instances, spanning from small-scale validation cases to large coordination problems with up to 1,000 satellites and 10,000 requests. We further propose a scenario characterisation scheme to quantify structural difficulty based on factors such as opportunity density, task flexibility, conflict intensity, and satellite congestion. A multidimensional evaluation protocol is introduced, assessing performance across five metrics: task profit, completion rate, workload balance, timeliness, and runtime. The framework is evaluated using mixed-integer programming, heuristics, meta-heuristics, and deep reinforcement learning across both agile and non-agile settings. Results show that EOS-Bench effectively distinguishes solver performance across scales and conditions, revealing trade-offs between solution quality and computational efficiency, and providing deeper insight into scenario complexity. EOS-Bench offers a unified and extensible open testbed for advancing research in Earth observation satellite scheduling. The code and data are available at https://github.com/Ethan19YQ/EOS-Bench.

cs.NI↗

arXiv2026

Unifying Decision Making and Trajectory Planning in Automated Driving through Time-Varying Potential Fields

This paper proposes a unified decision making and local trajectory planning framework based on Time-Varying Artificial Potential Fields (TVAPFs). The TVAPF explicitly models the predicted motion via bounded uncertainty of dynamic obstacles over the planning horizon, using information from perception and V2X sources when available. TVAPFs are embedded into a finite horizon optimal control problem that jointly selects the driving maneuver and computes a feasible, collision free trajectory. The effectiveness and real-time suitability of the approach are demonstrated through a simulation test in a multi-actor scenario with real road topology, highlighting the advantages of the unified TVAPF-based formulation.

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arXiv2025

Identification of contractive Lur'e-type systems via kernel-based Lipschitz design

This paper addresses the problem of identifying contractive Lur'e-type systems. Specifically, it proposes an identification framework that integrates linear prior knowledge with a kernel representation of the nonlinear feedback while systematically enforcing contractivity via Lipschitz constant design. The resulting algorithms provide models that are accurate in prediction, interpretable, and faithful to the contractive nature of the true system. Numerical experiments demonstrate that enforcing contractivity significantly improves parameter estimation and yields models that are both accurate and physically meaningful.

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arXiv2025

A kernel-based approach to physics-informed nonlinear system identification

This paper presents a kernel-based framework for physics-informed nonlinear system identification. The key contribution is a structured methodology that extends kernel-based techniques to seamlessly embed partially known physics-based models, improving parameter estimation and overall model accuracy. The proposed method enhances traditional modeling approaches by embedding a parametric model, which provides physical interpretability, with a kernel-based function, which accounts for unmodeled dynamics. The two models' components are identified from the data simultaneously, thereby minimizing a suitable cost that balances the relative importance of the physical and the black-box parts of the model. Additionally, nonlinear state smoothing is employed to address scenarios involving state-space models with not fully measurable states. Numerical simulations on an experimental benchmark system demonstrate the effectiveness of the proposed approach, achieving up to 51% reduction in simulation root mean square error compared to physics-only models and 31% performance improvement over state-of-the-art identification techniques.

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arXiv2025

A Quantum-Compliant Formulation for Network Epidemic Control

We deal with controlling the spread of an epidemic disease on a network by isolating one or multiple locations by banning people from leaving them. To this aim, we build on the susceptible-infected-susceptible and the susceptible-infected-removed discrete-time network models, encapsulating a control action that captures mobility bans via removing links from the network. Then, we formulate the problem of optimally devising a control policy based on mobility bans that trades-off the burden on the healthcare system and the social and economic costs associated with interventions. The binary nature of mobility bans hampers the possibility to solve the control problem with standard optimization methods, yielding a NP-hard problem. Here, this is tackled by deriving a Quadratic Unconstrained Binary Optimization (QUBO) formulation of the control problem, and leveraging the growing potentialities of quantum computing to efficiently solve it.

eess.SY↗

arXiv2025

A Quantum Approach for Optimal Transient Control in Network-Based Epidemic Models

Effective epidemic control is crucial for mitigating the spread of infectious diseases, particularly when pharmaceutical interventions such as vaccines or treatments are limited. Non-pharmaceutical strategies, including mobility restrictions, are key in reducing transmission rates but require careful optimization to balance public health benefits and socioeconomic costs. Quantum computing is emerging as a powerful tool for solving complex optimization problems that are intractable for classical methods and can thus be leveraged to handle mobility restrictions. This article presents a new approach to optimizing epidemic control strategies using quantum computing techniques. We focus on non-pharmaceutical interventions, particularly mobility restriction, modeled as a discrete-time network epidemic process based on the susceptible-infected-susceptible and susceptible-infected-removed frameworks. The control problem is formulated as a combinatorial optimization task, inherently NP-hard due to the binary nature of intervention decisions. To tackle this computational complexity, we derive a Quadratic Unconstrained Binary Optimization representation of the control problem, enabling its efficient solution via quantum computing resources. Our methodology is validated through numerical simulations on realistic case studies, showcasing the potential of quantum algorithms for enhancing epidemic control strategies. These findings pave the way for leveraging quantum optimization in broader applications of networked dynamical systems, demonstrating its viability for complex decision-making processes in public health management.

physics.soc-ph↗

arXiv2025

Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies

Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-uniform observations, i.e., data scenarios that include missing measurements, multiple runs, or aggregated observations. The goal is to provide a general approach for accurately recovering the overall dynamics of possibly nonlinear systems, allowing the capture of the system behavior over time from non-uniform observations. The proposed approach exploits prior knowledge by integrating domain-specific, interpretable, physical principles with black-box approximators, proving significant flexibility and adaptability in handling different types of non-uniform measurements, and addressing the limitations of traditional linear and black-box methods. The description of this novel framework is supported by a theoretical study on the effect of non-uniform observations on the accuracy of parameter estimation. Specifically, we demonstrate the existence of upper bounds on the parametric error resulting from missing measurements and aggregated observations. Then, the effectiveness of the approach is demonstrated through two case studies. These include a practical application with missing samples, i.e., the identification of a continuous stirred-tank reactor using real data, and a simulated Lotka-Volterra system under aggregated observations. The results highlight the ability of the framework to robustly estimate the system parameters and to accurately reconstruct the model dynamics despite the availability of non-uniform measurements.

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arXiv2024

Quantum optimization for Nonlinear Model Predictive Control

Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in ``fast'' applications, especially if a large number variables is involved. To overcome this issue, we propose a quantum computing approach for the solution of the NMPC optimization problem. Assuming the availability of an efficient quantum computer, the approach has the potential to considerably decrease the computational time and/or enhance the solution quality compared to classical algorithms.

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arXiv2024

A scalable, gradient-stable approach to multi-step, nonlinear system identification using first-order methods

This paper presents three main contributions to the field of multi-step system identification. First, drawing inspiration from Neural Network (NN) training, it introduces a tool for solving identification problems by leveraging first-order optimization and Automatic Differentiation (AD). The proposed method exploits gradients with respect to the parameters to be identified and leverages Linear Parameter-Varying (LPV) sensitivity equations to model gradient evolution. Second, it demonstrates that the computational complexity of the proposed method is linear in both the multi-step horizon length and the parameter size, ensuring scalability for large identification problems. Third, it formally addresses the "exploding gradient" issue: via a stability analysis of the LPV equations, it derives conditions for a reliable and efficient optimization and identification process for dynamical systems. Simulation results indicate that the proposed method is both effective and efficient, making it a promising tool for future research and applications in nonlinear system identification and non-convex optimization.

eess.SY↗

arXiv2024

On the Scientific Method: The Role of Hypotheses and Involved Mathematics

The paper investigates the role of data, hypotheses and mathematical methods that can be used in the discovery of a law y=fo(u), relating variables u and y of a physical phenomenon, making use of experimental measurements of such variables. Since the exact knowledge of the function fo cannot be expected, the problem of deriving approximate functions giving a small approximation error, measured by some function norm, is discussed. The main contributions of the paper are summarized as follows. At first, it is proven that deriving a reliable approximation, i.e., having a finite error, is not possible using measured data only. Thus, for deriving a reliable approximation, hypotheses on the function fo and on the disturbances corrupting the measurements must be introduced. Second, necessary and sufficient conditions for deriving a reliable approximation are provided. If such conditions are satisfied, suitable accuracy properties of the approximation can be defined, called theoretical properties. Third, it is shown that it is not possible to verify the conditions necessary for deriving a reliable approximation, but it is possible to verify that hypotheses on fo and on the disturbances are falsified by experimental measurements, showing that no function and disturbances satisfying the given hypotheses exist, able to reproduce the measurements (this is called falsification property). The above properties are then discussed for hypotheses belonging to the following classes: Parametric Probabilistic, where fo is assumed to be a function depending on a vector p and the disturbances are assumed to be stochastic variables; Set Membership class, where fo is assumed to be a bounded smooth function and the disturbances are assumed to be bounded variables; Parametric Set Membership class, able to integrate Parametric Probabilistic hypotheses with Set Membership hypotheses.

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arXiv2024

Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION

In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical principles with black-box basis functions to compensate for unmodeled dynamics, ensuring accuracy over long prediction horizons and computational efficiency. Additionally, we introduce penalty terms to enforce physical consistency and stability during training. We provide a comprehensive analysis of theoretical properties related to multi-step nonlinear system identification, establishing bounds on parameter estimation errors and conditions for gradient stability and sparsity recovery. The proposed framework demonstrates significant potential for improving model accuracy and reliability in various engineering applications, making a substantial step towards the effective use of combined off-white and sparse black models in system identification. The effectiveness of the proposed approach is demonstrated on a nonlinear system identification benchmark.

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arXiv2024

Noise Models in the LISA Mission

This document briefly describes the noise models and shapes used for the synthesis of the Drag-Free and Attitude Control System in the LISA space mission. LISA (Laser Interferometer Space Antenna) is one of the next large-class missions from the European Space Agency (ESA), expected to be launched in 2034. The main goal of the mission is to detect the gravitational waves, which are undulatory perturbations of the space-time fabric, extremely important to collect experimental proofs for the General Relativity Theory. In the 90s, different international collaborations of institutes laid the foundations for the first ground-based interferometers (see, e.g., LIGO and Virgo). However, ground-based interferometers have a limited bandwidth due to the Earth's environmental noises and short arm-length of few kilometers. Therefore, they cannot observe gravitational waves belonging to the portion of the spectrum below 1 Hz. This issue can be overcome by means of space-based interferometers, that can have arm-lengths up to millions of kilometers and exploit a quieter environment than the Earth's surface. The LISA system is affected by actuation, sensing and environmental disturbances and noises. Among the actuation noises we have those given by the Micro Propulsion System (MPS), the Gravitational Reference Sensor (GRS) and the Optical Assembly (OA) motor. Among the sensing noises we consider the interferometer, the Differential Wavefront Sensor (DWS) and the GRS. The environmental disturbances are given by the solar radiation pressure, the test-mass stiffness and self-gravity, and the environmental noises acting directly on the test-mass.

astro-ph.IM↗

arXiv2023

One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION

The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directly into a backpropagation-based learning algorithm. The main result is a method to compute in closed form the gradient of a multi-step loss function, while enforcing physical properties and constraints. The derived algorithm has been exploited to identify the unknown inertia matrix of a space debris, and the results show the reliability of the method in capturing the physical adherence of the estimated parameters.

eess.SY↗

arXiv2023

A Minimum-propellant Pontryagin-based Nonlinear MPC for Spacecraft Rendezvous in Lunar Orbit: the Extended Version

We propose a Nonlinear Model Predictive Control approach to spacecraft rendezvous in non-Keplerian Lunar orbits. The approach is based on the Pontryagin Minimum Principle and allows the accomplishment of minimum-propellant maneuvers. The relative motion between the chaser and the target is described by the nonlinear and unstable dynamics of the circular restricted three body-problem. In the proposed formulation, we design a minimum-propellant controller, which leads to a bang-bang behavior of the control signal. Under suitable assumptions, simplified dynamics is employed as prediction model, in order to reduce the complexity of the controller algorithm but, at the same time, without penalizing the controller tracking performance. The proposed approach's effectiveness is validated by a simulation example.

eess.SY↗

arXiv2022

Set Membership based Nonlinear Model Predictive Control

We present a numerically efficient Nonlinear Model Predictive Control (NMPC) approach, called Set Membership based NMPC (SM-NMPC). In particular, a Set Membership method is used to derive from data an approximation and tight bounds on the optimal NMPC control law. These quantities are used to reduce the dimensionality and volume of the search domain of the NMPC optimization problem, allowing a significant shortening of the computation time. The proposed SM-NMPC strategy is tested in simulation, considering realistic autonomous vehicle scenarios, like parallel parking and lane keeping maneuvers.

eess.SY↗

arXiv2022

Embedded Model Control of Networked Control Systems: an Experimental Case-study -- Stability analysis and further results

In Networked Control Systems (NCS), the absence of physical communication links in the loop leads to relevant issues, such as measurement delays and asynchronous execution of the control commands. These issues may lead to unwanted control behaviours. This ArXiv paper is intended to give additional results to the work presented in "Embedded Model Control of Networked Control Systems: an Experimental Case-study". The last one presents an original approach, based on the Embedded Model Control, to deal with experimental scenarios characterized by asynchronous control timing. The effectiveness of the proposed approach is demonstrated with a differential-drive robot, first with high-fidelity simulations and finally with several experimental tests. Specifically, the present work aims to study the stability analysis of the EMC experimental setup and to give further experimental results, to complement those presented in the main paper, "Embedded Model Control of Networked Control Systems: an Experimental Case-study".

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