This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Adaptive Control, Control Barrier Functions (CBFs), and connected graphs. The main elements employed in the integrative approach are an adaptive control design that ensures stability, a CBF-based safety filter that generates safe commands based on a reference model dynamics, and a reference model that ensures formation control with multi-agent systems when no uncertainties are present. The overall control design is shown to lead to a closed-loop adaptive system that is stable, avoids unsafe regions, and converges to a desired formation of the multi-agents. Numerical examples are provided to support the theoretical derivations. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Accurate detection of road edges is a key step in generating high-definition (HD) maps for autonomous driving. This work presents a road edge detection strategy that finds pavement drop-offs, curbs, or similar vertical deviations from the road surface by processing LiDAR data within individual scan lines, approximating each scan line as a road cross section in transverse-height coordinates. The key insight of this work is to exploit optimal extrema filtering to find maximum and minimum values in the first and second derivatives of the road profile, along with finding the corresponding strength of each extrema. The road boundary points, e.g., locations of sudden deviation from the road surface, are shown to correspond to extrema with the highest correlation strengths. The method was evaluated using real-world data collected from the Penn State mapping van operating at Penn States Larson Transportation Institute test track. Results demonstrate that the proposed approach yields consistent boundary detection, even in challenging overlapping scenarios. As well, special road mapping cases - such as when there are vertical overlapping features over the roads edge that block driving access (guardrails, vegetation, signage, etc.) - can be handled in the processing steps via simple modifications. A key advantage of the result is that it can process LiDAR data into road-surface estimates at the 2D scan-line level, allowing very rapid data processing and avoiding 3D point cloud processing.
The purpose of this paper is to develop a model of a multi-nodal subway system and formulate as a Mixed Integer Linear Programming (MILP) problem. The objective is to optimize the subway departure schedule with the aim of improving average passenger waiting times. By incorporating key operational constraints such as track capacity, energy consumption, and real-world demand patterns, this study aims to enhance the overall efficiency and reliability of the metro system while ensuring a better experience for passengers. A segment of the Bucharest subway will be used for validation of the proposed scheme. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Immersive experiences and art installations are becoming ever more popular. While they are usually developed using specialized technology (such as head-mounted displays) or dedicated, fully controlled environments (often black rooms with video projections on the walls and floor), the implementation of immersive installations in open, public spaces (such as city squares or shopping malls) remains a particularly challenging and interesting task: How to deal with unfavorable conditions on site (noise, stray light etc.) and how to effectively engage passers-by? In this paper, we present the generative evolutive interactive installation Ariadne's Fibres, located in one of the busiest shopping malls in Paris, France. With advanced tracking of passers-by, it provides a unique experimental platform for field research on what may catch attention and trigger immersion. In this context, Ariadne's Fibres is part of an ongoing study investigating, through trajectory analysis and questionnaires, the key parameters for immersion in open, public spaces. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Studies have shown the advantages of combining Art and Science allowing a greater range of engagement by students across multiple senses and thus improving their long term retention and understanding of complex scientific notions, as well as the potential to reach out to broader audiences. This paper offers some insights from the "infinity sCaR 2D Animated Cartoons for Control Education Rise" project. The goal of this project is to develop 2D animated cartoons on Control to both motivate engineering students to pursue further studies in Control and also to help them better appreciate some of the foundational concepts. In consequence, the authors hope this will increase the impact of Control on the younger generation. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
This work proposes a novel heuristic approach for the Flying Sidekick Traveling Salesman Problem, that represents the first truck-and-drone routing problem defined in the literature. This approach integrates data science and machine learning techniques with combinatorial optimization methods. The aim is to determine a good/optimal customer-to-vehicle assignment a priori, reducing the solution space of the truck-and-drone routing problem. An extensive computational campaign on benchmark instances has been conducted to evaluate the effectiveness of the proposed approach. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
The interplay between heterogeneous elements in control systems, such as processes and robots, often leads to hybrid optimization problems. This work integrates the path planning of sensing agents into the process control problem while circumventing this complexity. To this end, the proposed Model Predictive Control (MPC) strategy utilizes continuous variables to model the robots' movements and optimizes both system performance and path planning over a prediction horizon. As a result, robots move and sense in ways that maximize control performance. Moreover, the stochastic nonlinear formulation of the MPC controller allows it to dynamically adjust to constraint violations while maintaining probabilistic guarantees. To illustrate the proposed method, an academic example is employed in which a single robot monitors two separated tanks. Our simulations show that the proposed strategy enhances the flexibility of control systems with agents in the loop, providing a viable and efficient solution for applications ranging from industrial automation to resource management in uncertain environments. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
WEST is a metallic tokamak designed for long pulse operation with actively cooled components. The main missions of WEST include high fluence plasma divertor exposure and long pulse H-mode demonstration in a full tungsten environment. Achieving long duration and high performance plasma discharges, while ensuring machine protection, requires features such as specific controllers to keep the plasma in a steady state for several minutes. This paper presents the plasma current and loop voltage controllers implemented into the WEST Plasma Control System to achieve plasma of 1000 s. A simple circuit equation is used to model the tokamak and then the controllers are designed accordingly; their parameters are identified using the WEST database. The controller is then tuned and tested in simulation before being deployed on WEST. Experimental results are presented to illustrate the efficiency of the controller. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Connected and Automated Vehicles (CAVs) represent a technological advancement that can effectively reshape the mobility system as we know it today. In fact, besides being themselves more efficient than traditional vehicles, these vehicles can be used to implement vehicle-based control strategies, as is the purpose of this work. More in detail, we present a freeway control strategy in which a variable speed limit control is actuated by means of groups of vehicles, here denoted clusters, which are used as control actuators to enforce a certain speed to surrounding traffic. Different from other approaches already existing in the literature, this study investigates the application of Deep Q-learning networks (DQN) to define the speed that the clusters of CAVs must maintain to decongest a freeway stretch. The proposed method employs an enriched version of the Cell Transmission Model (CTM) to simulate the traffic dynamics in the presence of groups of CAVs, and uses a DQN-based controller to determine optimal variable speed limits for CAV clusters. Numerical validations, performed on the real stretch of the A20 freeway in the Netherlands, demonstrate significant reductions in Total Travel Time (an improvement of about 27% compared to the uncontrolled case), showing the effectiveness of vehicle based control strategies using reinforcement learning. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
The intersection of biomedical applications and advanced control algorithms is a promising direction for therapy optimization. Dynamic models of biological systems, such as tumor growth, enable control algorithms to design personalized and adaptive treatment strategies that improve therapeutic outcomes. Tumor modeling is an evolving field that aims to understand tumor behavior better, predict the precise drug responses, and design optimized dosage regimens. Our approach focuses on incorporating individual patient characteristics into mathematical models, enabling the identification of patient-specific differences. We model tumor progression over time under varying chemotherapy regimens using ordinary differential equations (ODEs). These equations include parameters that describe key biological processes, such as tumor growth and necrosis rates, dead cell washout, and drug efficacy. Parameter estimation is essential for the development of personalized and adaptive treatment strategies. In this work, we use a Physics-informed neural network (PINN) to identify the model parameters. We also extended the method to handle impulsive inputs in the form of instantaneously injected chemotherapeutic drug doses. We evaluated this method on in silico data. The results demonstrated high fitting accuracy and confirmed the applicability of the proposed approach under simplified conditions; however, further refinement is required to handle in vivo data in the future. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
This study aims to determine the optimal operating conditions of an industrial agriculture digester to maximize the biomethane productivity. This involves identifying key decision variables, such as the input substrate feed rate and digestate recirculation flow, while ensuring compliance with critical operational constraints, including command saturation, biomethane production, Organic Loading Rate (OLR), and Hydraulic Retention Time (HRT) limits. Advanced optimization techniques, specifically genetic algorithms, are considered to fine-tune process parameters to achieve the specifications. Additionally, multiple scenarios are explored regarding the premix feeding strategy of input materials, ensuring an optimized and adaptive approach to enhance overall system performance and efficiency. The proposed solution leads to about 11 % increase of the biomethane production over a month in comparison to an existing initial solution. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
This paper addresses the design of an average consensus control law for perturbed multi-agent systems subject to unknown bounded-in-average disturbances. The consensus strategy is developed for both continuous-time and asynchronous event-triggered implementations. A fully distributed control approach is proposed to ensure practical stability within a specific attractor. The key novelty lies in leveraging projection matrix properties to design the Laplacian weights and triggering parameters through feasible Linear Matrix Inequalities (LMIs). Numerical results validate the theoretical analysis and demonstrate the method's scalability and efficiency, even for large networks with random topologies (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
This article discusses classic and today's approaches to business process improvement. The integration of modern tools, such as Process Intelligence, offers new opportunities by using data-driven insights to improve processes. In addition, a review of current applications of Generative Artificial Intelligence (Generative AI) demonstrates the potential to increase efficiency in domains ranging from human resources and e-commerce to finance and healthcare. Despite these advances, the article emphasizes the importance of a holistic approach that combines new technologies with proven methodologies. This ensures not only operational efficiency, but also strategic alignment with organizational goals.
