Search

In recent years, several competitions have highlighted the need to investigate vision-based solutions to address scenarios with functional insufficiencies in perception, world modeling and localization. This article presents the Vision-based Lane Keeping System (VbLKS) developed by the DEI-Unipd Team within the context of the Bosch Future Mobility Challenge 2022. The main contribution lies in a Simulation-to-Reality (Sim2Real) GPS-denied VbLKS for a 1:10-scale autonomous vehicle. In this VbLKS, the input to a tailored Pure Pursuit (PP) based control strategy, namely the Lookahead Heading Error (LHE), is estimated at a constant lookahead distance employing a Convolutional Neural Network (CNN). A training strategy for a compact CNN is proposed, emphasizing data generation and augmentation on simulated camera images from a 3D Gazebo simulator, and enabling real-time operation on low-level hardware. A tailored PP-based lateral controller equipped with a derivative action and a PP-based velocity reference generation are implemented. Tuning ranges are established through a systematic time-delay stability analysis. Validation in a representative controlled laboratory setting is provided., Comment: 16 pages, 23 figures

This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions., Comment: accepted for presentation at the 2024 IEEE 63rd IEEE Conference on Decision and Control (CDC2024)

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors., Comment: accepted for presentation as WiP at the 2024 IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2024)

This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance., Comment: accepted for presentation as WiP at the 2024 IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2024)

Star-shaped Tilted Hexarotors are rapidly emerging for applications highly demanding in terms of robustness and maneuverability. To ensure improvement in such features, a careful selection of the tilt angles is mandatory. In this work, we present a rigorous analysis of how the force subspace varies with the tilt cant angles, namely the tilt angles along the vehicle arms, taking into account gravity compensation and torque decoupling to abide by the hovering condition. Novel metrics are introduced to assess the performance of existing tilted platforms, as well as to provide some guidelines for the selection of the tilt cant angle in the design phase., Comment: accepted for presentation at the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE2024)

We present a novel laboratory experiment, boiling stratified flow, as an analogy for atmospheric moist convection. A layer of diluted syrup is placed below freshwater in a beaker and heated from below. The vertical temperature profile in the experiment is analogous to the vapor mixing ratio in the atmosphere while the vertical profile of freshwater concentration in the experiment is analogous to the potential temperature profile in the atmosphere. Boiling starts when the bottom of the syrup layer reaches the boiling point, producing bubbles and vortex rings that stir the two-layer density interface and bring colder fresh water into the syrup layer. When the syrup layer at the beginning of the experiment is sufficiently thin and diluted, the vortex rings entrain more cold water than needed to remove superheating in the syrup layer, ending the boiling. When the syrup layer is deep and concentrated, the boiling is steady since the entrained colder water instantaneously removes the superheating in the bottom syrup layer. A theory is derived to predict the entrainment rate and the transition between the intermittent and steady boiling regimes, validated by experimental data. We suggest that these dynamics may share similarities with the mixing and lifecycle of cumulus convection.

In this paper, we propose the METANET with service station (METANET-s) model, a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process of merging back into the highway mainstream. We explore the capability of the METANET-s to capture well both traffic back propagation and capacity drops, which are typically caused by the presence of vehicles joining again the mainstream traffic from the service station. Therefore, capturing these effects is crucial to improving the model's predictive capabilities. Finally, we perform a comparative analysis with the Cell Transmission Model with service station (CTM-s), showcasing that the METANET-s describes the traffic evolution much better than its first-order counterpart., Comment: 7 pages, 4 figures, to be published in European Control Conference (ECC) 2024

We use video footage of a water tunnel experiment to construct a two-dimensional reduced-order model of the flapping dynamics of an inverted flag in uniform flow. The model is obtained as the reduced dynamics on an attracting spectral submanifold (SSM) that emanates from the two slowest modes of the unstable fixed point of the flag. Beyond an unstable fixed point and a limit cycle expected from observations, our SSM-reduced model also confirms the existence of two unstable fixed points for the flag, which were found by previous studies. Importantly, the model correctly reconstructs the dynamics from a small number of general trajectories and no further information on the system. In the chaotic flapping regime, we construct a 4D SSM-reduced model that captures the system's chaotic attractor.

Quantum walks have proven to be a universal model for quantum computation and to provide speed-up in certain quantum algorithms. The discrete-time quantum walk (DTQW) model, among others, is one of the most suitable candidates for circuit implementation, due to its discrete nature. Current implementations, however, are usually characterized by quantum circuits of large size and depth, which leads to a higher computational cost and severely limits the number of time steps that can be reliably implemented on current quantum computers. In this work, we propose an efficient and scalable quantum circuit implementing the DTQW on the $2^n$-cycle based on the diagonalization of the conditional shift operator. For $t$ time-steps of the DTQW, the proposed circuit requires only $O(n^2 + nt)$ two-qubit gates compared to the $O(n^2 t)$ of the current most efficient implementation based on quantum Fourier transforms. We test the proposed circuit on an IBM quantum device for a Hadamard DTQW on the $4$- and $8$-cycle characterized by periodic dynamics and recurrent generation of maximally entangled single-particle states. Experimental results are meaningful well beyond the regime of few time steps, paving the way for reliable implementation and use on quantum computers., Comment: Accepted version: 24 pages, 12 figures

We develop a model reduction technique for non-smooth dynamical systems using spectral submanifolds. Specifically, we construct low-dimensional, sparse, nonlinear and non-smooth models on unions of slow and attracting spectral submanifolds (SSMs) for each smooth subregion of the phase space and then properly match them. We apply this methodology to both equation-driven and data-driven problems, with and without external forcing., Comment: Submitted to International Journal of Non-Linear Mechanics

Urban traffic congestion remains a pressing challenge in our rapidly expanding cities, despite the abundance of available data and the efforts of policymakers. By leveraging behavioral system theory and data-driven control, this paper exploits the DeePC algorithm in the context of urban traffic control performed via dynamic traffic lights. To validate our approach, we consider a high-fidelity case study using the state-of-the-art simulation software package Simulation of Urban MObility (SUMO). Preliminary results indicate that DeePC outperforms existing approaches across various key metrics, including travel time and CO$_2$ emissions, demonstrating its potential for effective traffic management, Comment: This paper has been submitted to IEEE ECC24

Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.

Ghost imaging and differential ghost imaging are well-known imaging techniques based on the use of both classical and quantum correlated states of light. Since the existence of correlations has been shown to be the main resource to implement ghost imaging and differential ghost-imaging protocols, here we analyze the advantages and disadvantages of using two different kinds of superthermal states of light, which are more correlated than the typically employed thermal states. To make a fair comparison, we calculate the contrast (C) and the signal-to-noise ratio (SNR) of the reconstruct image. While the larger values of C suggest the usefulness of these superthermal states, the values of SNR do not improve by increasing the intensity fluctuations of light. On the contrary, they are the same as those exhibited by thermal light., Comment: 7 pages, 5 figures

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.

Correlated states of light, both classical and quantum, can find useful applications in the implementation of several imaging techniques. Among the employed sources, pseudo-thermal states, generated by the passage of a laser beam through a diffuser, represent the standard choice. To produce light with a higher level of correlation, in this work we consider and characterize the speckled-speckle field obtained with two diffusers using both a numerical simulation and an experimental implementation. In order to discuss the potential usefulness of super-thermal light in imaging protocols, we analyze the behavior of some figures of merit, namely the contrast, the signal-to-noise ratio and the image resolution. The obtained results clarify the possible advantages offered by this kind of light, and at the same time better emphasize the reasons why it does not outperform pseudo-thermal light., Comment: 16 pages, 11 figures

The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.

Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently employed in model-based control schemes, dimensionality reduction introduces model uncertainty which can potentially compromise the stability and safety of the original high-dimensional system. In this work, we propose a novel reduced-order model predictive control (ROMPC) scheme to solve constrained optimal control problems for nonlinear, high-dimensional systems. To address the challenges of using ROMs in predictive control schemes, we derive an error bounding system that dynamically accounts for model reduction error. Using these bounds, we design a robust MPC scheme that ensures robust constraint satisfaction, recursive feasibility, and asymptotic stability. We demonstrate the effectiveness of our proposed method in simulations on a high-dimensional soft robot with nearly 10,000 states., Comment: 9 pages, 3 figures, To be presented at Conference for Decision and Control 2023

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control, and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a stabilizing online distributed control law. Finally, numerical examples involving a cubic formation following a straight path in the 3D space are provided to validate the proposed control strategies., Comment: 35 pages, 3 figures, submitted to the European Journal of Control on December 31st, 2023 (version 2)

This paper deals with water management over open-channel networks (OCNs) subject to water height imbalance. The OCN is modeled by means of graph theoretic tools and a regulation scheme is designed basing on an outer reference generation loop for the whole OCN and a set of local controllers. Specifically, it is devised a fully distributed adaptive consensus-based algorithm within the discrete-time domain capable of (i) generating a suitable tracking reference that stabilizes the water increments over the underlying network at a common level; (ii) coping with general flow constraints related to each channel of the considered system. This iterative procedure is derived by solving a guidance problem that guarantees to steer the regulated network - represented as a closed-loop system - while satisfying requirements (i) and (ii), provided that a suitable design for the local feedback law controlling each channel flow is already available. The proposed solution converges exponentially fast towards the average consensus thanks to a Metropolis-Hastings design of the network parameters without violating the imposed constraints over time. In addition, numerical results are reported to support the theoretical findings, and the performance of the developed algorithm is discussed in the context of a realistic scenario., Comment: 14 pages, 7 figures

In this paper, a robust control solution for a satellite equipped with a robotic manipulator is presented. First, the dynamic model of the system is derived based on quaternions to describe the evolution of the attitude of the base satellite. Then, a non-singular terminal sliding mode controller that employs quaternions for attitude control, is proposed for concurrently handling all the degrees of freedom of the space manipulator. Moreover, an additional adaptive term is embedded in the controller to estimate the upper bounds of disturbances and uncertainties. The result is a resilient solution able to withstand unmodelled dynamics and interactions. Lyapunov theory is used to prove the stability of the controller and numerical simulations allow assessing performance and fuel efficiency., Comment: New figures and some text clarifications

We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combines travel time, from a fixed origin to a desired destination, and the monetary cost of using city facilities, parking or service stations. The traffic authority can influence the commuters' preferred routing choice by means of personalized discounts on parking tickets and on the energy price at service stations. We formalize the problem of designing these monetary incentives optimally as a large-scale bilevel game, where constraints arise at both levels due to the finite capacities of city facilities and incentives budget. Then, we develop an efficient decentralized solution scheme with convergence guarantees based on BIG Hype, a recently-proposed hypergradient-based algorithm for hierarchical games. Finally, we validate our model via numerical simulations over the Anaheim's network, and show that the proposed approach produces sensible results in terms of traffic decongestion and it is able to solve in minutes problems with more than 48000 variables and 110000 constraints., Comment: Submitted to IEEE CDC23

Hierarchical decision making problems, such as bilevel programs and Stackelberg games, are attracting increasing interest in both the engineering and machine learning communities. Yet, existing solution methods lack either convergence guarantees or computational efficiency, due to the absence of smoothness and convexity. In this work, we bridge this gap by designing a first-order hypergradient-based algorithm for Stackelberg games and mathematically establishing its convergence using tools from nonsmooth analysis. To evaluate the \textit{hypergradient}, namely, the gradient of the upper-level objective, we develop an online scheme that simultaneously computes the lower-level equilibrium and its Jacobian. Crucially, this scheme exploits and preserves the original hierarchical and distributed structure of the problem, which renders it scalable and privacy-preserving. We numerically verify the computational efficiency and scalability of our algorithm on a large-scale hierarchical demand-response model.

The generation of a large amount of entanglement is a necessary condition for a quantum computer to achieve quantum advantage. In this paper, we propose a method to efficiently generate pseudo-random quantum states, for which the degree of multipartite entanglement is nearly maximal. We argue that the method is optimal, and use it to benchmark actual superconducting (IBM's ibm_lagos) and ion trap (IonQ's Harmony) quantum processors. Despite the fact that ibm_lagos has lower single-qubit and two-qubit error rates, the overall performance of Harmony is better thanks to low error rate in state preparation and measurement and to the all-to-all connectivity of qubits. Our result highlights the relevance of the qubits network architecture to generate highly entangled state., Comment: 14 pages, 8 figures

Characterizing and mitigating errors in current noisy intermediate-scale devices is important to improve performance of next generations of quantum hardware. In order to investigate the importance of the different noise mechanisms affecting quantum computation, we perform full quantum process tomography of single qubits in a real quantum processor in which echo experiments are implemented. Besides error sources already included in standard models, the obtained results show the dominant role of coherent errors, which we practically correct by inserting random single-qubit unitaries in the quantum circuit, significantly increasing the circuit length over which quantum computations on actual quantum hardware produce reliable results., Comment: 7 pages, 5 figures

This paper analyzes how the presence of service stations on highways affects traffic congestion. We focus on the problem of optimally designing a service station to achieve beneficial effects in terms of total traffic congestion and peak traffic reduction. Microsimulators cannot be used for this task due to their computational inefficiency. We propose a genetic algorithm based on the recently proposed CTMs, that efficiently describes the dynamics of a service station. Then, we leverage the algorithm to train a neural network capable of solving the same problem, avoiding implementing the CTMs. Finally, we examine two case studies to validate the capabilities and performance of our algorithms. In these simulations, we use real data extracted from Dutch highways., Comment: Submitted to IFAC Worlds conference 2023

Modeling and control of high-dimensional, nonlinear robotic systems remains a challenging task. While various model- and learning-based approaches have been proposed to address these challenges, they broadly lack generalizability to different control tasks and rarely preserve the structure of the dynamics. In this work, we propose a new, data-driven approach for extracting low-dimensional models from data using Spectral Submanifold Reduction (SSMR). In contrast to other data-driven methods which fit dynamical models to training trajectories, we identify the dynamics on generic, low-dimensional attractors embedded in the full phase space of the robotic system. This allows us to obtain computationally-tractable models for control which preserve the system's dominant dynamics and better track trajectories radically different from the training data. We demonstrate the superior performance and generalizability of SSMR in dynamic trajectory tracking tasks vis-a-vis the state of the art, including Koopman operator-based approaches., Comment: 9 pages, 4 figures, 1 table, Submission to International Conference for Robotics and Automation 2023

This paper proposes a non-monetary traffic demand management scheme, named CARMA, as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in both the desired arrival time and Value of Time (VOT). We consider a generalized notion of VOT by allowing it to vary dynamically on each day (e.g., according to trip purpose and urgency), rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. We introduce a non-tradable mobility credit, named karma, that is used by commuters to bid for access to the fast lane. Commuters who get outbid or do not participate in the CARMA scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We model the collective commuter behaviors under CARMA as a Dynamic Population Game (DPG), in which a Stationary Nash Equilibrium (SNE) is guaranteed to exist. Unlike existing monetary schemes, CARMA is demonstrated, both analytically and numerically, to achieve a) an equitable traffic assignment with respect to heterogeneous income classes and b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. With extensive numerical analysis, we show that CARMA is able to retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperform tolling under a well-designed redistribution scheme. We also highlight the privacy-preserving feature of CARMA, i.e., its ability to tailor to the private preferences of commuters without centrally collecting the information.

In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station. The presented model enhances the classical CTM dynamics by adding the dynamics associated with the service stations, where the vehicles may stop before merging back into the mainstream. We name it CTMs. We discuss its flexibility in describing different complex scenarios where multiple stations are characterized by different drivers' average stopping times corresponding to different services. The model has been developed to help design control strategies aimed at decreasing traffic congestion. Thus, we discuss how classical control schemes can interact with the proposed \gls{CTMs}. Finally, we validate the proposed model through numerical simulations and assess the effects of service stations on traffic evolution, which appear to be beneficial, especially for relatively short congested periods., Comment: This work is submitted to the 61st IEEE CDC 2022

We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs). We use observed data to locate a low-dimensional, attracting slow SSM and compute a maximally sparse approximation to the reduced dynamics on it. The recently released SSMLearn algorithm uses implicit optimization to fit a spectral submanifold to data and reduce the dynamics to the normal form. Here, we present two simplified algorithms, which reformulate manifold fitting and normal form computation as explicit problems under certain assumptions. We show on both numerical and experimental datasets that these algorithms yield accurate and sparse rigorous models for essentially nonlinear (or non-linearizable) dynamics. The new algorithms are significantly simplified and provide a speedup of several orders of magnitude., Comment: 28 pages, 6 figures

We re-examine possible dependencies on redshift of the spectral parameters of blazars observed at very-high energies (VHEs) with Imaging Atmospheric Cherenkov telescopes (IACTs). This is relevant to assess potential effects with the source distance of the photon to axion-like particle (ALP) mixing, that would deeply affect the propagation of VHE photons across the Universe. We focus our spectral analysis on 38 BL Lac objects (32 high-peaked and 6 intermediate-peaked) up to redshift $z\simeq 0.5$, and a small sample of 5 Flat Spectrum Radio Quasars up to $z=1$ treated independently to increase the redshift baseline. The 78 independent spectra of these sources are first of all carefully corrected for the gamma-gamma interaction with photons of the Extragalactic Background Light, that are responsible for the major redshift-dependent opacity effect. Then, the corrected spectra are fitted with simple power-laws to infer the intrinsic spectral indices $\Gamma_{\rm em}$ at VHE, to test the assumption that such spectral properties are set by the local rather than the global cosmological environment. We find some systematic anti-correlations with redshift of $\Gamma_{\rm em}$ that might indicate, although with low-significance, a spectral anomaly potentially requiring a revision of the photon propagation process. More conclusive tests with higher statistical significance will require the observational improvements offered by the forthcoming new generation of Cherenkov arrays (CTA, ASTRI, LHAASO)., Comment: 17 pages, 12 figures

In modern buildings renewable energy generators and storage devices are spreading, and consequently the role of the users in the power grid is shifting from passive to active. We design a demand response scheme that exploits the prosumers' flexibility to provide ancillary services to the main grid. We propose a hierarchical scheme to coordinate the interactions between the distribution system operator and a community of smart prosumers. The framework inherits characteristics from price-based and incentive-based schemes and it retains the advantages of both. We cast the problem as a Stackelberg game with the prosumers as followers and the distribution system operator as leader. We solve the resulting bilevel optimization program via a KKT reformulation, proving the existence and the convergence to a local Stackelberg equilibrium. Finally, we provide numerical simulations to corroborate our claims on the benefits of the proposed framework., Comment: Accepted to 61st IEEE Conference on Decision and Control (CDC), 2022

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework.

We present laboratory experiments that investigate the structure and flow characteristics of gravity currents travelling through an array of roughness elements. The roughness elements are of comparable height to the gravity current such that the current flows through the roughness array rather than over it. The frontal velocity and density structure are measured as the current transitions from flowing along a smooth bed to flowing through the roughness array, and then back to a smooth bed. We find that, upon entering the roughness array, the gravity current decelerates and the density structure changes from the head and tail structure typical of smooth bed gravity currents, to a wedge shape. A model is presented that explains the deceleration and change in shape based on a dynamic balance between a pressure gradient within the current tail and a drag force associated with individual roughness elements. This model accurately predicts the deceleration of the gravity current, supporting the proposed dynamic balance., Comment: Accepted by Physical Review of Fluids. 20 pages, 17 figures

We derive low-dimensional, data-driven models for transitions among exact coherent states (ECSs) in one of the most studied canonical shear flows, the plane Couette flow. These one- or two-dimensional nonlinear models represent the leading-order reduced dynamics on attracting spectral submanifolds (SSMs), which we construct using the recently developed SSMLearn algorithm from a small number of simulated transitions. We find that the energy input and output rates provide efficient parametrizations for the most important SSMs. By restricting the dynamics to these SSMs, we obtain reduced-order models that also reliably predict nearby, off-SSM transitions that were not used in their training., Comment: Accepted version

In this paper we consider the localization problem for a visual sensor network. Inspired by the alternate attitude and position distributed optimization framework discussed in [1], we propose an estimation scheme that exploits the unit dual quaternion algebra to describe the sensors pose. This representation is beneficial in the formulation of the optimization scheme allowing to solve the localization problem without designing two interlaced position and orientation estimators, thus improving the estimation error distribution over the two pose components and the overall localization performance. Furthermore, the numerical experimentation asserts the robustness of the proposed algorithm w.r.t. the initial conditions., Comment: 7 pages, 4 figures, 2 algorithms, 2019 18th European Control Conference (ECC) Napoli, Italy, June 25-28, 2019

For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios. Adopting the standard least-squares approach, in this work we derive both the (centralized) analytic solution to this issue and two distributed iterative schemes, which allow to establish a connection between the convergence behavior of consensus algorithm toward the optimal estimate and the theory of the stochastic matrices that describe the network system dynamics. This study on the one hand highlights the role of the topological links that define the neighborhood of agent nodes, while on the other allows to optimize the convergence rate by easy parameter tuning. The theoretical findings are validated considering different network topologies by means of numerical simulations., Comment: 7 pages, 4 figures, 2 tables, extension of the manuscript presented at the 2019 European Control Conference

Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization. To this end, a numerical projection operator Newton's method is developed to find a solution by the minimization of a cost functional able to capture all these different tasks. To stabilize the formation, a particular potential function has been designed, allowing to obtain specified geometrical configurations while the barycenter position and velocity of the system follows a desired trajectory., Comment: 7 pages, 5 figures, 1 table, extension of the manuscript presented at the 2019 European Control Conference

In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle structured environments without relying on metric information. Specifically, the aim is to account for the trade-off between local communication given by bearing visibility sensors installed on each agent involved, optimal deployment in closed unknown scenarios and focus of a group of agents on one point of interest. The particular targets of this study can be summarized as 1. the computation, under certain topological assumptions, of a lower bound for the number of required agents, which are provided by a realistic geometric model (e.g. a round shape) to emphasize physical limitations; 2. the minimization of the number of nodes and links maintaining a distributed approach over a connected communication graph; 3. the identification of an activation cluster around an event with a radial decreasing intensity, sensed by each agent; 4. the attempt to send the agents belonging to the cluster towards the most intense point in the scenario by minimizing a weighted isoperimetric functional., Comment: 8 pages, 4 figures, extension of the manuscript presented at 2019 Mediterranean Conference on Control and Automation