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Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control arXiv.cs.SY Pub Date : 2024-03-23 Nhat Minh Nguyen, Stephen McIlvanna, Jack Close, Mien Van
Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a novel control scheme for creating multi-agent
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Transactive Local Energy Markets Enable Community-Level Resource Coordination Using Individual Rewards arXiv.cs.SY Pub Date : 2024-03-22 Daniel C. May, Petr Musilek
ALEX (Autonomous Local Energy eXchange) is an economy-driven, transactive local energy market where each participating building is represented by a rational agent. Relying solely on building-level information, this agent minimizes its electricity bill by automating distributed energy resource utilization and trading. This study examines ALEX's capabilities to align participant and grid-stakeholder
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Adaptive Dual Covariance Steering with Active Parameter Estimation arXiv.cs.SY Pub Date : 2024-03-22 Jacob W. Knaup, Panagiotis Tsiotras
This work examines the optimal covariance steering problem for systems subject to unknown parameters that enter multiplicatively with the state and control, in addition to additive disturbances. In contrast to existing works, the unknown parameters are modeled as random variables and are estimated online. This work proposes the utilization of recursive least squares estimation for efficient parameter
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An Optimal Solution to Infinite Horizon Nonlinear Control Problems: Part II arXiv.cs.SY Pub Date : 2024-03-25 Mohamed Naveed Gul Mohamed, Aayushman Sharma, Raman Goyal, Suman Chakravorty
This paper considers the infinite horizon optimal control problem for nonlinear systems. Under the condition of nonlinear controllability of the system to any terminal set containing the origin and forward invariance of the terminal set, we establish a regularized solution approach consisting of a ``finite free final time" optimal transfer problem to the terminal set which renders the set globally
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Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy arXiv.cs.SY Pub Date : 2024-03-25 R. Spencer Hallyburton, Miroslav Pajic
Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly
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A Semi-Lagrangian Approach for Time and Energy Path Planning Optimization in Static Flow Fields arXiv.cs.SY Pub Date : 2024-03-25 Víctor C. da S. Campos, Armando A. Neto, Douglas G. Macharet
Efficient path planning for autonomous mobile robots is a critical problem across numerous domains, where optimizing both time and energy consumption is paramount. This paper introduces a novel methodology that considers the dynamic influence of an environmental flow field and considers geometric constraints, including obstacles and forbidden zones, enriching the complexity of the planning problem
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DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking arXiv.cs.SY Pub Date : 2024-03-25 Yichuan Li, Junkai Zhao, Yixiao Li, Zheng Wu, Rui Cao, Masayoshi Tomizuka, Yunhui Liu
Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations, operational inefficiencies, and process unreliability. This paper introduces a Dynamic Bin-Picking Framework (DBPF) that challenges traditional static
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Looking back and forward: A retrospective and future directions on Software Engineering for systems-of-systems arXiv.cs.SY Pub Date : 2024-03-25 Everton Cavalcante, Thais Batista, Flavio Oquendo
Modern systems are increasingly connected and more integrated with other existing systems, giving rise to systems-of-systems (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose
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BatDeck: Advancing Nano-drone Navigation with Low-power Ultrasound-based Obstacle Avoidance arXiv.cs.SY Pub Date : 2024-03-25 Hanna Müller, Victor Kartsch, Michele Magno, Luca Benini
Nano-drones, distinguished by their agility, minimal weight, and cost-effectiveness, are particularly well-suited for exploration in confined, cluttered and narrow spaces. Recognizing transparent, highly reflective or absorbing materials, such as glass and metallic surfaces is challenging, as classical sensors, such as cameras or laser rangers, often do not detect them. Inspired by bats, which can
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Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL arXiv.cs.SY Pub Date : 2024-03-25 Osama Ahmad, Zawar Hussain, Hammad Naeem
This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints
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Symbolic and User-friendly Geometric Algebra Routines (SUGAR) for Computations in Matlab arXiv.cs.SY Pub Date : 2024-03-25 Manel Velasco, Isiah Zaplana, Arnau Dória-Cerezo, Pau Martí
Geometric algebra (GA) is a mathematical tool for geometric computing, providing a framework that allows a unified and compact approach to geometric relations which in other mathematical systems are typically described using different more complicated elements. This fact has led to an increasing adoption of GA in applied mathematics and engineering problems. However, the scarcity of symbolic implementations
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The Adaptive Workplace: Orchestrating Architectural Services around the Wellbeing of Individual Occupants arXiv.cs.SY Pub Date : 2024-03-25 Andrew Vande Moere, Sara Arko, Alena Safrova Drasilova, Tomáš Ondráček, Ilaria Pigliautile, Benedetta Pioppi, Anna Laura Pisello, Jakub Prochazka, Paula Acuna Roncancio, Davide Schaumann, Marcel Schweiker, Binh Vinh Duc Nguyen
As the academic consortia members of the EU Horizon project SONATA ("Situation-aware OrchestratioN of AdapTive Architecture"), we respond to the workshop call for "Office Wellbeing by Design: Don't Stand for Anything Less" by proposing the "Adaptive Workplace" concept. In essence, our vision aims to adapt a workplace to the ever-changing needs of individual occupants, instead of that occupants are
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Counter-example guided Imitation Learning of Feedback Controllers from Temporal Logic Specifications arXiv.cs.SY Pub Date : 2024-03-25 Thao Dang, Alexandre Donzé, Inzemamul Haque, Nikolaos Kekatos, Indranil Saha
We present a novel method for imitation learning for control requirements expressed using Signal Temporal Logic (STL). More concretely we focus on the problem of training a neural network to imitate a complex controller. The learning process is guided by efficient data aggregation based on counter-examples and a coverage measure. Moreover, we introduce a method to evaluate the performance of the learned
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Spatially temporally distributed informative path planning for multi-robot systems arXiv.cs.SY Pub Date : 2024-03-25 Binh Nguyen, Linh Nguyen, Truong X. Nghiem, Hung La, Jose Baca, Pablo Rangel, Miguel Cid Montoya, Thang Nguyen
This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of
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Data-Driven Extrusion Force Control Tuning for 3D Printing arXiv.cs.SY Pub Date : 2024-03-25 Xavier Guidetti, Ankita Mukne, Marvin Rueppel, Yannick Nagel, Efe C. Balta, John Lygeros
The quality of 3D prints often varies due to different conditions inherent to each print, such as filament type, print speed, and nozzle size. Closed-loop process control methods improve the accuracy and repeatability of 3D prints. However, optimal tuning of controllers for given process parameters and design geometry is often a challenge with manually tuned controllers resulting in inconsistent and
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Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes arXiv.cs.SY Pub Date : 2024-03-25 Seokhyun Chung, Raed Al Kontar
Building a predictive model that rapidly adapts to real-time condition monitoring (CM) signals is critical for engineering systems/units. Unfortunately, many current methods suffer from a trade-off between representation power and agility in online settings. For instance, parametric methods that assume an underlying functional form for CM signals facilitate efficient online prediction updates. However
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HT-LIP Model based Robust Control of Quadrupedal Robot Locomotion under Unknown Vertical Ground Motion arXiv.cs.SY Pub Date : 2024-03-24 Amir Iqbal, Sushant Veer, Christopher Niezrecki, Yan Gu
This paper presents a hierarchical control framework that enables robust quadrupedal locomotion on a dynamic rigid surface (DRS) with general and unknown vertical motions. The key novelty of the framework lies in its higher layer, which is a discrete-time, provably stabilizing footstep controller. The basis of the footstep controller is a new hybrid, time-varying, linear inverted pendulum (HT-LIP)
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Legged Robot State Estimation within Non-inertial Environments arXiv.cs.SY Pub Date : 2024-03-24 Zijian He, Sangli Teng, Tzu-Yuan Lin, Maani Ghaffari, Yan Gu
This paper investigates the robot state estimation problem within a non-inertial environment. The proposed state estimation approach relaxes the common assumption of static ground in the system modeling. The process and measurement models explicitly treat the movement of the non-inertial environments without requiring knowledge of its motion in the inertial frame or relying on GPS or sensing environmental
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Input-to-State Stability of Newton Methods for Generalized Equations in Nonlinear Optimization arXiv.cs.SY Pub Date : 2024-03-24 Torbjørn Cunis, Ilya Kolmanovsky
We show that Newton methods for generalized equations are input-to-state stable with respect to disturbances such as due to inexact computations. We then use this result to obtain convergence and robustness of a multistep Newton-type method for multivariate generalized equations. We demonstrate the usefulness of the results with other applications to nonlinear optimization. In particular, we provide
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Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction arXiv.cs.SY Pub Date : 2024-03-23 Justin Lidard, Hang Pham, Ariel Bachman, Bryan Boateng, Anirudha Majumdar
Tasks where robots must cooperate with humans, such as navigating around a cluttered home or sorting everyday items, are challenging because they exhibit a wide range of valid actions that lead to similar outcomes. Moreover, zero-shot cooperation between human-robot partners is an especially challenging problem because it requires the robot to infer and adapt on the fly to a latent human intent, which
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Perception and Control of Surfing in Virtual Reality using a 6-DoF Motion Platform arXiv.cs.SY Pub Date : 2024-03-23 Premankur Banerjee, Jason Cherin, Jayati Upadhyay, Jason Kutch, Heather Culbertson
The paper presents a system for simulating surfing in Virtual Reality (VR), emphasizing the recreation of aquatic motions and user-initiated propulsive forces using a 6-Degree of Freedom (DoF) motion platform. We present an algorithmic approach to accurately render surfboard kinematics and interactive paddling dynamics, validated through experimental evaluation with \(N=17\) participants. Results indicate
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Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics arXiv.cs.SY Pub Date : 2024-03-23 Zhe Xu, Tao Yan, Simon X. Yang, S. Andrew Gadsden, Mohammad Biglarbegian
This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed
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On the role of network structure in learning to coordinate with bounded rationality arXiv.cs.SY Pub Date : 2024-03-23 Yifei Zhang, Marcos M. Vasconcelos
Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common action, aligning their decisions to maximize their individual goals. We consider a model for networked interactions where agents learn to coordinate their binary actions under a strict bound on their rationality. We first prove
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On the Variational Interpretation of Mirror Play in Monotone Games arXiv.cs.SY Pub Date : 2024-03-22 Yunian Pan, Tao Li, Quanyan Zhu
Mirror play (MP) is a well-accepted primal-dual multi-agent learning algorithm where all agents simultaneously implement mirror descent in a distributed fashion. The advantage of MP over vanilla gradient play lies in its usage of mirror maps that better exploit the geometry of decision domains. Despite extensive literature dedicated to the asymptotic convergence of MP to equilibrium, the understanding
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Balancing Fairness and Efficiency in Energy Resource Allocations arXiv.cs.SY Pub Date : 2024-03-22 Jiayi Li, Matthew Motoki, Baosen Zhang
Bringing fairness to energy resource allocation remains a challenge, due to the complexity of system structures and economic interdependencies among users and system operators' decision-making. The rise of distributed energy resources has introduced more diverse heterogeneous user groups, surpassing the capabilities of traditional efficiency-oriented allocation schemes. Without explicitly bringing
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Autonomous Driving With Perception Uncertainties: Deep-Ensemble Based Adaptive Cruise Control arXiv.cs.SY Pub Date : 2024-03-22 Xiao Li, H. Eric Tseng, Anouck Girard, Ilya Kolmanovsky
Autonomous driving depends on perception systems to understand the environment and to inform downstream decision-making. While advanced perception systems utilizing black-box Deep Neural Networks (DNNs) demonstrate human-like comprehension, their unpredictable behavior and lack of interpretability may hinder their deployment in safety critical scenarios. In this paper, we develop an Ensemble of DNN
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On Optimal Management of Energy Storage Systems in Renewable Energy Communities arXiv.cs.SY Pub Date : 2024-03-20 Giovanni Gino Zanvettor, Marco Casini, Antonio Vicino
Renewable energy communities are legal entities involving the association of citizens, organizations and local businesses aimed at contributing to the green energy transition and providing social, environmental and economic benefits to their members. This goal is pursued through the cooperative efforts of the community actors and by increasing the local energy self-consumption. In this paper, the optimal
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Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation arXiv.cs.SY Pub Date : 2024-03-20 Anton Glushchenko, Konstantin Lastochkin
A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is based on the novel augmentation of a high-gain
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Macroscopic pricing schemes for the utilization of pool ride-hailing vehicles in bus lanes arXiv.cs.SY Pub Date : 2024-03-20 Lynn Fayed, Gustav Nilsson, Nikolas Geroliminis
With the increasing popularity of ride-hailing services, new modes of transportation are having a significant impact on the overall performance of transportation networks. As a result, there is a need to ensure that both the various transportation alternatives and the spatial network resources are used efficiently. In this work, we analyze a network configuration where part of the urban transportation
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Priority-based Energy Allocation in Buildings for Distributed Model Predictive Control arXiv.cs.SY Pub Date : 2024-03-20 Hongyi Li, Jun Xu
Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, which distributes the energy rationally based on priority order. According to the scenarios
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3D Directed Formation Control with Global Shape Convergence using Bispherical Coordinates arXiv.cs.SY Pub Date : 2024-03-20 Omid Mirzaeedodangeh, Farhad Mehdifar, Dimos V. Dimarogonas
In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are
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Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems arXiv.cs.SY Pub Date : 2024-03-20 Simon Stock, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis
While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic
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Lattice piecewise affine approximation of explicit model predictive control with application to satellite attitude control arXiv.cs.SY Pub Date : 2024-03-20 Zhengqi Xu, Jun Xu, Ai-Guo Wu, Shuning Wang
Satellite attitude cotrol is a crucial part of aerospace technology, and model predictive control(MPC) is one of the most promising controllers in this area, which will be less effective if real-time online optimization can not be achieved. Explicit MPC converts the online calculation into a table lookup process, however the solution is difficult to obtain if the system dimension is high or the constraints
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Augmented Labeled Random Finite Sets and Its Application to Group Target Tracking arXiv.cs.SY Pub Date : 2024-03-20 Chaoqun Yang, Mengdie Xu, Xiaowei Liang, Heng Zhang, Xianghui Cao
This paper addresses the problem of group target tracking (GTT), wherein multiple closely spaced targets within a group pose a coordinated motion. To improve the tracking performance, the labeled random finite sets (LRFSs) theory is adopted, and this paper develops a new kind of LRFSs, i.e., augmented LRFSs, which introduces group information into the definition of LRFSs. Specifically, for each element
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Distributed Cooperative Formation Control of Nonlinear Multi-Agent System (UGV) Using Neural Network arXiv.cs.SY Pub Date : 2024-03-20 Si Kheang Moeurn
The paper presented in this article deals with the issue of distributed cooperative formation of multi-agent systems (MASs). It proposes the use of appropriate neural network control methods to address formation requirements (uncertainties dynamic model). It considers an adaptive leader-follower distributed cooperative formation control based on neural networks (NNs) developed for a class of second-order
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An Extended Kuramoto Model for Frequency and Phase Synchronization in Delay-Free Networks with Finite Number of Agents arXiv.cs.SY Pub Date : 2024-03-20 Andreas Bathelt, Vimukthi Herath, Thomas Dallmann
Due to its description of a synchronization between oscillators, the Kuramoto model is an ideal choice for a synchronisation algorithm in networked systems. This requires to achieve not only a frequency synchronization but also a phase synchronization - something the standard Kuramoto model can not provide for a finite number of agents. In this case, a remaining phase difference is necessary to offset
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Charged Momentum: Electric Vehicle Surge in India's 2023 Landscape arXiv.cs.SY Pub Date : 2024-03-20 Rahul Wagh
Electric vehicles (EVs) have emerged as a transformative force in India's transportation sector, offering a sustainable solution to the country's growing energy and environmental challenges. Against the backdrop of rapid urbanization, rising pollution levels, and the need for energy security, EVs have gained traction as a viable alternative to traditional internal combustion engine vehicles. This paper
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Quantifying the Aggregate Flexibility of EV Charging Stations for Dependable Congestion Management Products: A Dutch Case Study arXiv.cs.SY Pub Date : 2024-03-20 Nanda Kishor Panda, Simon H. Tindemans
Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and
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A Control-Recoverable Added-Noise-based Privacy Scheme for LQ Control in Networked Control Systems arXiv.cs.SY Pub Date : 2024-03-20 Xuening Tang, Xianghui Cao, Wei Xing Zheng
As networked control systems continue to evolve, ensuring the privacy of sensitive data becomes an increasingly pressing concern, especially in situations where the controller is physically separated from the plant. In this paper, we propose a secure control scheme for computing linear quadratic control in a networked control system utilizing two networked controllers, a privacy encoder and a control
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Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles arXiv.cs.SY Pub Date : 2024-03-20 Ying Shuai Quan, Jian Zhou, Erik Frisk, Chung Choo Chung
This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorporating a state observer for obstacles into
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Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization arXiv.cs.SY Pub Date : 2024-03-20 Canchen Jiang, Hao Wang
Community battery systems have been widely deployed to provide services to the grid. Unlike a single battery storage system in the community, coordinating multiple community batteries can further unlock their value, enhancing the viability of community battery solutions. However, the centralized control of community batteries relies on the full information of the system, which is less practical and
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Federated reinforcement learning for robot motion planning with zero-shot generalization arXiv.cs.SY Pub Date : 2024-03-20 Zhenyuan Yuan, Siyuan Xu, Minghui Zhu
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i.e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments. We develop a federated reinforcement learning framework that enables collaborative learning of multiple learners and a central server, i.e., the Cloud, without sharing
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Safety-Aware Reinforcement Learning for Electric Vehicle Charging Station Management in Distribution Network arXiv.cs.SY Pub Date : 2024-03-20 Jiarong Fan, Ariel Liebman, Hao Wang
The increasing integration of electric vehicles (EVs) into the grid can pose a significant risk to the distribution system operation in the absence of coordination. In response to the need for effective coordination of EVs within the distribution network, this paper presents a safety-aware reinforcement learning (RL) algorithm designed to manage EV charging stations while ensuring the satisfaction
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Projection-free computation of robust controllable sets with constrained zonotopes arXiv.cs.SY Pub Date : 2024-03-20 Abraham P. Vinod, Avishai Weiss, Stefano Di Cairano
We study the problem of computing robust controllable sets for discrete-time linear systems with additive uncertainty. We propose a tractable and scalable approach to inner- and outer-approximate robust controllable sets using constrained zonotopes, when the additive uncertainty set is a symmetric, convex, and compact set. Our least-squares-based approach uses novel closed-form approximations of the
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Optimal control of continuous-time symmetric systems with unknown dynamics and noisy measurements arXiv.cs.SY Pub Date : 2024-03-20 Hamed Taghavian, Florian Dorfler, Mikael Johansson
An iterative learning algorithm is presented for continuous-time linear-quadratic optimal control problems where the system is externally symmetric with unknown dynamics. Both finite-horizon and infinite-horizon problems are considered. It is shown that the proposed algorithm is globally convergent to the optimal solution and has some advantages over adaptive dynamic programming, including being unbiased
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Integrating Large Language Models for Severity Classification in Traffic Incident Management: A Machine Learning Approach arXiv.cs.SY Pub Date : 2024-03-20 Artur Grigorev, Khaled Saleh, Yuming Ou, Adriana-Simona Mihaita
This study evaluates the impact of large language models on enhancing machine learning processes for managing traffic incidents. It examines the extent to which features generated by modern language models improve or match the accuracy of predictions when classifying the severity of incidents using accident reports. Multiple comparisons performed between combinations of language models and machine
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Adversarial Attacks and Defenses in Automated Control Systems: A Comprehensive Benchmark arXiv.cs.SY Pub Date : 2024-03-20 Vitaliy Pozdnyakov, Aleksandr Kovalenko, Ilya Makarov, Mikhail Drobyshevskiy, Kirill Lukyanov
Integrating machine learning into Automated Control Systems (ACS) enhances decision-making in industrial process management. One of the limitations to the widespread adoption of these technologies in industry is the vulnerability of neural networks to adversarial attacks. This study explores the threats in deploying deep learning models for fault diagnosis in ACS using the Tennessee Eastman Process
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A Log-domain Interior Point Method for Convex Quadratic Games arXiv.cs.SY Pub Date : 2024-03-20 Bingqi Liu, Dominic Liao-McPherson
In this paper, we propose an equilibrium-seeking algorithm for finding generalized Nash equilibria of non-cooperative monotone convex quadratic games. Specifically, we recast the Nash equilibrium-seeking problem as variational inequality problem that we solve using a log-domain interior point method and provide a general purpose solver based on this algorithm. This approach is suitable for non-potential
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Digital Twin-Driven Reinforcement Learning for Obstacle Avoidance in Robot Manipulators: A Self-Improving Online Training Framework arXiv.cs.SY Pub Date : 2024-03-19 Yuzhu Sun, Mien Van, Stephen McIlvanna, Nguyen Minh Nhat, Kabirat Olayemi, Jack Close, Seán McLoone
The evolution and growing automation of collaborative robots introduce more complexity and unpredictability to systems, highlighting the crucial need for robot's adaptability and flexibility to address the increasing complexities of their environment. In typical industrial production scenarios, robots are often required to be re-programmed when facing a more demanding task or even a few changes in
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Current-Based Impedance Control for Interacting with Mobile Manipulators arXiv.cs.SY Pub Date : 2024-03-19 Jelmer de Wolde, Luzia Knoedler, Gianluca Garofalo, Javier Alonso-Mora
As robots shift from industrial to human-centered spaces, adopting mobile manipulators, which expand workspace capabilities, becomes crucial. In these settings, seamless interaction with humans necessitates compliant control. Two common methods for safe interaction, admittance, and impedance control, require force or torque sensors, often absent in lower-cost or lightweight robots. This paper presents
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Hybrid Unsupervised Learning Strategy for Monitoring Industrial Batch Processes arXiv.cs.SY Pub Date : 2024-03-19 Christian W. Frey
Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced
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Most Likely Sequence Generation for $n$-Grams, Transformers, HMMs, and Markov Chains, by Using Rollout Algorithms arXiv.cs.SY Pub Date : 2024-03-19 Yuchao Li, Dimitri Bertsekas
In this paper we consider a transformer with an $n$-gram structure, such as the one underlying ChatGPT. The transformer provides next word probabilities, which can be used to generate word sequences. We consider methods for computing word sequences that are highly likely, based on these probabilities. Computing the optimal (i.e., most likely) word sequence starting with a given initial state is an
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Autonomous Underground Freight Transport Systems -- The Future of Urban Logistics? arXiv.cs.SY Pub Date : 2024-03-13 Lasse Bienzeisler, Torben Lelke, Bernhard Friedrich
We design a concept for an autonomous underground freight transport system for Hanover, Germany. To evaluate the resulting system changes in overall traffic flows from an environmental perspective, we carried out an agent-based traffic simulation with MATSim. Our simulations indicate comparatively low impacts on network-wide traffic volumes. Local CO2 emissions, on the other hand, could be reduced
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Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls arXiv.cs.SY Pub Date : 2024-03-11 Chengleyang Lei, Wei Feng, Peng Wei, Yunfei Chen, Ning Ge, Shiwen Mao
An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to usually-limited individual abilities, these robots require an edge information hub (EIH), which is capable of not only communications but also sensing and computing. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial
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Secure and Energy-efficient Unmanned Aerial Vehicle-enabled Visible Light Communication via A Multi-objective Optimization Approach arXiv.cs.SY Pub Date : 2024-03-03 Lingling Liu, Aimin Wang, Jing Wu, Jiao Lu, Jiahui Li, Geng Sun
In this research, a unique approach to provide communication service for terrestrial receivers via using unmanned aerial vehicle-enabled visible light communication is investigated. Specifically, we take into account a unmanned aerial vehicle-enabled visible light communication scenario with multiplex transmitters, multiplex receivers, and a single eavesdropper, each of which is equipped with a single
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Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling arXiv.cs.SY Pub Date : 2024-02-19 Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in which they interact with the underlying Markov process to shape the finite-time performance of SA remains
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Minimal Constraint Violation Probability in Model Predictive Control for Linear Systems arXiv.cs.SY Pub Date : 2024-02-16 Michael Fink, Tim Brüdigam, Dirk Wollherr, Marion Leibold
Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing a small probability of constraint violation. In this work, we propose a linear model predictive control approach that minimizes the probability that linear
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Denoising Diffusion-Based Control of Nonlinear Systems arXiv.cs.SY Pub Date : 2024-02-03 Karthik Elamvazhuthi, Darshan Gadginmath, Fabio Pasqualetti
We propose a novel approach based on Denoising Diffusion Probabilistic Models (DDPMs) to control nonlinear dynamical systems. DDPMs are the state-of-art of generative models that have achieved success in a wide variety of sampling tasks. In our framework, we pose the feedback control problem as a generative task of drawing samples from a target set under control system constraints. The forward process
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Practical Framework for Problem-Based Learning in an Introductory Circuit Analysis Course arXiv.cs.SY Pub Date : 2024-01-29 Sebastian Martin, Salvador Pineda, Juan Perez-Ruiz, Natalia Alguacil, Antonio Ruiz-Gonzalez
Introductory courses on electric circuits at undergraduate level are usually presented in quite abstract terms, with questions and problems quite far from practical problems. This causes the students have difficulties to apply that theory to solve practical technical problems. On the other hand, electric circuits are everywhere in our lives, so we have plenty of real practical problems. Here we compile
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Digital requirements engineering with an INCOSE-derived SysML meta-model arXiv.cs.SY Pub Date : 2024-01-29 James S. Wheaton, Daniel R. Herber
Traditional requirements engineering tools do not readily access the system architecture model defined in SysML and related Profiles, often resulting in duplication of basic system model elements that nevertheless lack the connectivity and expressive detail possible in a SysML-defined model. Without architecture model connectivity, requirements can suffer from imprecision and inconsistent terminology