This paper presents a finite-time synchronization framework for a class of nonlinear systems with constant delays. The control strategy utilizes the backstepping technique to design a control input that drives the synchronization error between the master and slave systems to zero within finite time. This framework ensures fast convergence while accounting for the challenges posed by the time delays. The structure of the generalized nonlinear system enables the application of this approach to a wide range of chaotic systems. In particular, in this paper, the Genesio-Tesi chaotic system and Sprott Circuit system are employed to validate the proposed methodology through simulation. The resulting simulations clearly demonstrate the effectiveness of the proposed control strategy, confirming its capability to achieve finite-time synchronization.

We investigate the challenge of designing robust external excitations to control and synchronize a population of non-interacting homotypic harmonic oscillators, specifically, theta neurons. The Theta model emulates the bursting behavior observed in spiking cells, characterized by periodic oscillations in their membrane electric potential.Our approach involves formulating this optimization task as an optimal mean-field control problem for the linear continuity/Fokker-Planck equation in the space of probability measures. To address this problem numerically, we employ an indirect deterministic descent method, leveraging an exact representation of the increment of the objective functional.As a main contribution, we delve into practical aspects in the implementation of the proposed method and expose several results of numerical experiments.

Quantum entanglement is a key resource for quantum technologies, yet its efficient and high-fidelity generation remains a challenge due to the complexity of quantum dynamics. This paper presents a quantum optimal control framework to maximize bipartite entanglement within a fixed time horizon, under bounded control inputs. By leveraging Pontryagin’s Minimum Principle, we derive a set of necessary conditions that guide the design of time-dependent control fields to steer a two-qubit system toward maximally entangled Bell states. The entanglement is quantified using concurrence, and the control objective is formulated as maximizing this measure at the terminal time. Our approach is validated through numerical simulations of Liouville–von Neumann dynamics. The results demonstrate the effectiveness of switching-based control strategies in achieving robust entanglement, offering insights into practical implementations of quantum control for entanglement generation in quantum networks.

Ensuring Unmanned Aerial Vehicle (UAV) safety during takeoff and landing in dynamic environments remains challenging due to possible sensor failures and undetected obstacles. This paper introduces an external anomaly detection framework using Vision Transformers (ViTs) and Large Language Models (LLMs) to monitor UAV landing zones via fixed cameras. Anomalies, such as occupied or unsafe landing areas, trigger emergency responses to prevent collisions and ensure the safety of the UAV and its surroundings. The system employs a ViT-based feature extractor and a binary classifier, enhanced through a multimodal Teacher-Student architecture—leveraging both visual and textual inputs during training but using only images at inference. Simulation results demonstrate the framework’s effectiveness as a redundant safety mechanism for UAV operations.

The problem of precise modeling of Synchronous Generator (SG) devices is demanding and crucial for monitoring, stability, and control of electric grids and microgrids. SGs are known to exhibit nonlinear behavior, and because they may work in a wide range of operating points due to different electric network phenomena like load changes, current intermittent renewable energy resources, temperature variations, topological changes, etc., the modeling task of SG can be a challenging problem. This paper proposes a new method to find a global model for a synchronous generator from the input and output measurement data using state-of-the-art methods in artificial intelligence (AI) like fuzzy clustering, subspace identification, and Takagi–Sugeno (T-S) fuzzy modeling. The fuzzy C-means method is utilized to cluster the measurement data, and then using a subspace identification method based on Markov parameters concepts, the dynamic model of SG for each cluster is obtained in the form of a state space model. A Takagi–Sugeno fuzzy model is applied to determine the corresponding outputs based on the measured inputs and the available state space models. The effectiveness of the proposed approach is illustrated by applying it to the data obtained from simulations on a known fourth-order SG nonlinear model and the data obtained from real-world experimental tests.

We study the problem of designing “robust” external excitations for control and synchronization of an assembly of homotypic harmonic oscillators representing so-called theta neurons. The model of theta neuron (Theta model) captures, in main, the bursting behavior of spiking cells in the brain of biological beings, enduring periodic oscillations of the electric potential in their membrane. Our task is to find an external stimulus (control), which steers all neurons of a given population to their desired phases (i.e., excites/slows down its spiking activity) with the highest probability. Our methodology is the following: The optimization problem at hand is formulated as an optimal mean-field control problem for the local continuity equation in the space of probability measures. To solve this problem numerically, we propose an indirect deterministic descent method based on an exact representation of the increment (infinite-order variation) of the objective functional. We illustrate the modus operandi of the proposed method discuss some aspects of its practical realization and provide some results of numerical experiments.

Reliable high-fidelity quantum state transformation has always been considered as an inseparable part of quantum information processing. In this regard, Pontryagin Minimum (or maximum) Principle (PMP) has proved to play an important role to achieve the maximum fidelity in an optimum time or energy. Motivated by this, in this work, we formulate a control constrained optimal control problem where we aim to minimize time and also energy subjected to a quantum system satisfying the bilinear Schrödinger equation. We derive the first order optimality conditions through the application of PMP resulting in a boundary value problem. Next, in order to obtain efficient numerical results, we exploit a particular family of physics-informed neural networks that are specifically designed to tackle the indirect method based on the PMP. We show that this method can significantly speed up the process by first obtaining a set of relations which finally let us compute the optimal control strategy to determine the time- and energy-optimal protocol driving a general initial state to a target state by a quantum Hamiltonian with bounded control. We make use of the so-called “qutip” package in python, and the newly developed “tfc” python package.

Frequency response analysis (FRA) suffers from the interpretation of results despite its potential ability to detect faults related to the power transformer windings. This article presents a technique for interpreting frequency responses, which is based on image processing and a deep learning method called graph convolutional neural network (CNN). The proposed procedure transfers frequency responses into 2-D images through a visualization technique. The resulting images are aggregated into a dataset to be used as the CNN input. The proposed technique is applied on frequency responses of two different winding models with short circuit (SC) faults. The SC faults with different intensities are applied on different sections of a simulated ladder model winding and a 20 kV winding of a 1.6 MVA distribution transformer. After determining the frequency response for each faulty case and applying the visualization technique, the precise locating of the SC faults is performed by the CNN. Then, the results are analyzed by performance evaluation metrics. At this stage, the high performance of the CNN in the use of 2-D images instead of the conventional method is observed. Finally, by testing the high impedance SC faults in different sections of the simulated winding model and applying the suggested method step by step, early detection of the SC fault is also performed in this article. It should be noted that the suggested technique, in addition to its accuracy and high detection speed, can be considered as an important step in automatic interpretation of frequency responses for online monitoring of transformers.

Humans tend to build environments with structure, which consists of mainly planar surfaces. From the intersection of planar surfaces arise straight lines. Lines have more degrees of freedom than points. Thus, line-based structure-from-motion (SfM) provides more information about the environment. In this article, we present solutions for SfM using lines, namely, incremental SfM. These approaches consist of designing state observers for a camera’s dynamical visual system looking at a 3-D line. We start by presenting a model that uses spherical coordinates for representing the line’s moment vector. We show that this parameterization has singularities, and, therefore, we introduce a more suitable model that considers the line’s moment and shortest viewing ray. Concerning the observers, we present two different methodologies. The first uses a memory-less state-of-the-art framework for dynamic visual systems. Since the previous states of the robotic agent are accessible—while performing the 3-D mapping of the environment—the second approach aims at exploiting the use of memory to improve the estimation accuracy and convergence speed. The two models and the two observers are evaluated in simulation and real data, where mobile and manipulator robots are used.

Recovering the 3D structure of the surrounding environment is an essential task in any vision-controlled Structure-from-Motion (SfM) scheme. This paper focuses on the theoretical properties of the SfM, known as the incremental active depth estimation. The term incremental stands for estimating the 3D structure of the scene over a chronological sequence of image frames. Active means that the camera actuation is such that it improves estimation performance. Starting from a known depth estimation filter, this paper presents the stability analysis of the filter in terms of the control inputs of the camera. By analyzing the convergence of the estimator using the Lyapunov theory, we relax the constraints on the projection of the 3D point in the image plane when compared to previous results. Nonetheless, our method is capable of dealing with the cameras' limited field-of-view constraints. The main results are validated through experiments with simulated data.

In this paper, we propose a mapping technique that builds a continuous representation of the environment from range data. The strategy presented here encodes the probability of points in space to be occupied using 2.5D B-spline surfaces. For a fast update rate, the surface is recursively updated as new measurements arrive. The proposed B-spline map is less susceptible to precision and interpolation errors that are present in occupancy grid-based methods. From simulation and experimental results, we show that this approach leverages the floating point resolution of continuous metric maps and the fast update/access/merging advantages of discrete metric maps. Thus, the proposed method is suitable for online robotic tasks such as localization and path planning, requiring minor modification to existing software that usually operates on metric maps.

This work considers an AC/DC microgrid that is composed by an integrated PV/wind microgrid, a storage system (battery), DC and AC loads, and between the two buses there is a bidirectional static converter to transfer power. The PV source is connected into the DC bus through a Booster converter while the double fed induction generator that is used in the wind energy system is connected directly with the AC bus. Using the fact that there is a complementarity role between the renewable sources, the purpose of this work is to analyze and improve the power flow dynamics between the AC and DC buses by designing an appropriate control strategy that stabilizes the microgrid during a lack of power generation at one of the renewable sources. Several simulation studies are presented to illustrate the performance evaluation of the proposed microgrid.

The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localization problem consists of estimating the relative pose between two robots, by sensing each other's pose or sharing information about the perceived environment. Most solutions for these problems use a set of discrete data without taking into account the chronological order of the events. This paper builds on recent results on continuous estimation to propose a framework that estimates the depth and relative pose between two non-holonomic vehicles. The basic idea consists in estimating the depth of the points by explicitly considering the dynamics of the camera mounted on a ground robot, and feeding the estimates of 3D points observed by both cameras in a filter that computes the relative pose between the robots. We evaluate the convergence for a set of simulated scenarios and show experimental results validating the proposed framework.

This paper addresses the source localization problem of an acoustic fish-tag using the Time-of-Arrival measurement of an acoustic signal, transmitted by the fish-tag. The Time-of-Arrival measurements denote the pseudo-range information between the acoustic receiver and the fish-tag, except that the Time-of-Transmission of the acoustic signal is unknown. Starting with the pseudo-range measurement equation, a globally valid quasi-linear time-varying measurement model is presented that is independent of the Time-of-Transmission of the acoustic signal. Using this measurement model, an Uniformly Globally Asymptotically Stable (UGAS), three stage estimation strategy (eXogenous Kalman Filter) is designed to estimate the position of an acoustic fish-tag and evaluated against a benchmark Extended Kalman Filter based estimator. The efficacy of the developed estimation method is demonstrated experimentally, in presence of intermittent observations using an array of receivers mounted on three Unmanned Surface Vessels (USVs).

This paper presents a 2D B-spline mapping framework for representing unstructured environments in a compact manner. While occupancy-grid and landmark-based maps have been successfully employed by the robotics community in indoor scenarios, outdoor long-term autonomous operations require a more compact representation of the environment. This work tackles this problem by interpolating the data of a high frequency sensor using B-spline curves. Compared to lines and circles, splines are more powerful in the sense that they allow for the description of more complex shapes in the scene. In this work, spline curves are continuously tracked and aligned across multiple sensor readings using lightweight methods, making the proposed framework suitable for robot navigation in outdoor missions. In particular, a Simultaneous Localization and Mapping (SLAM) algorithm specifically tailored for B-spline maps is presented here. The efficacy of the proposed framework is demonstrated by Software-in-the-Loop (SiL) simulations in different scenarios.

Moving Path Following (MPF) control laws allows an autonomous vehicle to converge to and follow a path that is moving with respect to an inertial frame. This paper formally extends the MPF methods present in the literature to the case of three dimensional paths that can be moving with time-varying linear and angular velocities with respect to an inertial frame. A 3D MPF error space and a MPF Lyapunov-based control law is derived at kinematic level for a fixed-wing unmanned aerial vehicle. Formal convergence proofs and validation using flight test results are presented, demonstrating the effectiveness of the proposed method.

This paper addresses the tracking problem of the state variables of a nonlinear planar dynamic model of an overactuated electric vehicle with four-wheel independent drive (4WID) topology. In order to track the state variables of the system it is proposed a new sliding mode controller based on a nonlinear planar model. The controller explores the overactuated system in order to redistribute the control effort to the remaining actuators when a fault occurs. Although the system has multiple solutions due to the access of the torque applied in each wheel independently, there could be particular fault events where the remaining healthy actuators may not be able to maintain the system stability. In those particular cases the inclusion of the steering control variable is an important advantage as it allows the controller to manipulate the control effort in any directions. The proposed controller is validated in various driving scenarios with different fault schemes. The simulations are carried out with a high-fidelity vehicular model provided by the simulation software Carsim in co-simulation with Matlab/Simulink.

The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors - upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.

This paper introduces the moving path following (MPF) problem, in which a vehicle is required to converge to and follow a desired geometric moving path, without a specific temporal specification, thus generalizing the classical path following that only applies to stationary paths. Possible tasks that can be formulated as an MPF problem include tracking terrain/air vehicles and gas clouds monitoring, where the velocity of the target vehicle or cloud specifies the motion of the desired path. We derive an error space for MPF for the general case of time-varying paths in a two-dimensional space and subsequently an application is described for the problem of tracking single and multiple targets on the ground using an unmanned aerial vehicle (UAV) flying at constant altitude. To this end, a Lyapunov-based MPF control law and a path-generation algorithm are proposed together with convergence and performance metric results. Real-world flight tests results that took place in Ota Air Base, Portugal, with the ANTEX-X02 UAV demonstrate the effectiveness of the proposed method.

Robotic odor source localization is a promising tool with numerous applications in safety, search and rescue, and environmental science. In this paper, we present an algorithm for odor source localization using multiple cooperating robots equipped with chemical sensors. Laplacian feedback is employed to maintain the robots in a formation, introducing spatial diversity that is used to better establish the position of the flock relative to the plume and its source. Robots primarily move upwind but use odor information to adjust their position and spacing so that they are centered on the plume and trace its structure. Real-world experiments were performed with an ethanol plume inside a wind tunnel, and used to both validate the algorithm and assess the impact of different formation shapes.

This paper addresses the problem of time-coordination of a team of cooperating multirotor unmanned aerial vehicles that exchange information over a supporting time-varying network. A distributed control law is developed to ensure that the vehicles meet the desired temporal assignments of the mission, while flying along predefined collision-free paths, even in the presence of faulty communication networks, temporary link losses, and switching topologies. In this paper, the coordination task is solved by reaching consensus on a suitably defined coordination state. Conditions are derived under which the coordination errors converge to a neighborhood of zero. Simulation and flight test results are presented to validate the theoretical findings. Note to Practitioners-This paper presents an approach which enables a fleet of multirotor UAVs to follow a set of desired trajectories and coordinate along them, thus satisfying specific spatial and temporal assignments. The proposed solution can be employed in applications in which multiple vehicles are tasked to execute cooperative, collision-free maneuvers, and accomplish a common goal in a safely manner. An example is sequential monitoring, in which the UAVs have to visit and monitor a set of points of interest, while maintaining a desired temporal separation between each other. In this paper, we also simulate a scenario in which the vehicles, positioned in a square room, are required to exchange position with each other. It is shown that the proposed control algorithm not only ensures that the UAVs arrive at the final destinations at the same time, but also guarantees safety, i.e., the vehicles avoid collision with each other at all times.

The use of groups of autonomous marine vehicles has enormous potential in numerous marine applications, perhaps the most relevant of which is the surveying and exploration of the oceans, still widely unknown and misunderstood. In many mission scenarios requiring the concerted operation of multiple marine vehicles carrying distinct, yet complementary sensor suites, relative positioning and formation control becomes mandatory. However, the constraints placed by the medium make it hard to both communicate and localize vehicles, even in relation to each other. In this paper, we deal with the challenging problem of keeping an autonomous underwater vehicle in a moving triangular formation with respect to 2 leader vehicles. We build upon our previous theoretical work on range-only formation control, which presents simple feedback laws to drive the controlled vehicle to its intended position in the formation using only ranges obtained to the leading vehicles with no knowledge of the formation path. We then introduce the real-world constraints associated with the use of autonomous underwater vehicles, especially the low frequency characteristics of acoustic ranging and its unreliability. We discuss the required changes to implement the solution in our vehicles, and provide simulation results using a full dynamic and communication model. Finally, we present the results of real world trials using MEDUSA-class autonomous marine vehicles.

This paper describes a decentralized coordination strategy for multi robot patrolling missions. To this effect, the theory of Gaussian Processes (usually used for estimation purposes) is suitably adapted to tackle the problem of harbour patrolling. The introduction of a time varying dependency in the probabilistic formulation (thus allowing for the sampled field to be dynamic, i.e., changing in time) makes the proposed solution suitable for the type of mission considered. Moreover, the advantages of Voronoi tessellations are exploited to automatically distribute the vehicles over the environment. The resulting algorithm takes into account several constraints and can be tailored based on the communication and computational capabilities of the robots, thus making it suitable for heterogeneous systems. Numerical simulations and experiments involving three autonomous marine surface vehicles in a harbour scenario at the Parque Expo site in Lisbon are discussed.

This paper addresses the problem of observability of the relative motion of two AUVs equipped with velocity and depth sensors, and inter-vehicle ranging devices. We start by exploiting nonlinear observability concepts to analyze, using observability rank conditions, some types of relative AUV motions. Because rank conditions only provide binary information regarding observability, we then derive a specific observability index (metric) and study its dependence on the types of relative motions executed by the vehicles. In particular, it is shown that the degradation of observability depends on the range and angle between the relative velocity and position vectors. The problem addressed bears affinity with that of single beacon localization. For this reason, the results derived are validated experimentally in a equivalent, single beacon navigation scenario.