pwc-archive/papers-with-abstracts huggingface dataset:
Learning Constrained Corner Node Trajectories of a Tether Net System for Space Debris Capture
Authors: Feng Liu, Achira Boonrath, Prajit KrisshnaKumar, Elenora M. Botta, Souma Chowdhury
Date: 2023-07-06 00:00:00

The earth's orbit is becoming increasingly crowded with debris that poses significant safety risks to the operation of existing and new spacecraft and satellites. The active tether-net system, which consists of a flexible net with maneuverable corner nodes launched from a small autonomous spacecraft, is a promising solution for capturing and disposing of such space debris. The requirement of autonomous operation and the need to generalize over scenarios with debris scenarios in different rotational rates makes the capture process significantly challenging. The space debris could rotate about multiple axes, which, along with sensing/estimation and actuation uncertainties, calls for a robust, generalizable approach to guiding the net launch and flight - one that can guarantee robust capture. This paper proposes a decentralized actuation system combined with reinforcement learning for planning and controlling this tether-net system. In this new system, four microsatellites with cold gas type thrusters act as the corner nodes of the net and can thus help control or correct the flight of the net after launch. The microsatellites pull the net to complete the task of approaching and capturing the space debris. The proposed method uses a RL framework that integrates a proximal policy optimization to find the optimal solution based on the dynamics simulation of the net and the microsatellites performed in Vortex Studio. The RL framework finds the optimal trajectory that is both fuel-efficient and ensures a desired level of capture quality.
Analysis and design of model predictive control frameworks for dynamic operation -- An overview
Authors: Johannes Köhler, Matthas A. Müller, Frank Allgöwer
Date: 2023-07-06 00:00:00
Tasks: Model Predictive Control

This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further research.
Meta Federated Reinforcement Learning for Distributed Resource Allocation
Authors: Zelin Ji, Zhijin Qin, Xiaoming Tao
Date: 2023-07-06 00:00:00
Tasks: Federated Learning, Meta-Learning, reinforcement-learning, Reinforcement Learning

In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.
Secure-by-Construction Synthesis for Control Systems
Authors: Bingzhuo Zhong, Siyuan Liu, Marco Caccamo, Majid Zamani
Date: 2023-07-05 00:00:00

In this paper, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Moreover, we demonstrate the flexibility of our approach by incorporating user-defined cost functions into the construction of secure-by-construction controllers. Finally, we validate the effectiveness of our results through two case studies, illustrating the practical applicability and benefits of our proposed approach.
Energy optimization for Full-Duplex Wireless-Powered IoT Networks using Rotary-Wing UAV with Multiple Antennas
Authors: Leyla Fathollahi, Mahmood Mohassel Feghhi, Mahmoud Atashbar
Date: 2023-07-05 00:00:00

In this paper, we propose a novel design for the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex (FD) wireless-powered Internet of Things (IoT) networks. In this network, the UAV is equipped with an antenna array, and the $K$ IoT sensors, which are distributed randomly, use single-antenna to communicate. By sending the energy, the UAV as a hybrid access point, charges the sensors and collects information from them. Then, to manage the time and optimize the energy, the sensors are divided into N groups, so that the UAV equipped with multi-input multi-output (MIMO) technology can serve the sensors in a group, during the total time $T$. We provide a simple implementation of the wireless power transfer protocol in the sensors by using the time division multiple access (TDMA) scheme to receive information from the users. In other words, the sensors of each group receive energy from the UAV, when it hovers over the sensors of the previous group, and also when the UAV flies over the previous group to the current group. The sensors of each group send their information to the UAV, when the UAV is hovering over their group. Under these assumptions, we formulate two optimization problems: a sum throughput maximization problem, and a total time minimization problem. Numerical results show that our proposed optimal network provides better performance than the existing networks. In fact, our novel design can serve more sensors at the cost of using more antennas compared to that of the conventional networks.
An Overview of the NET Playground -- A Heterogeneous, Multi-Functional Network Test Bed
Authors: Paul Schwenteck, Sandra Zimmermann, Caspar von Lengerke, Giang T. Nguyen, Christian Scheunert, Frank H. P. Fitzek
Date: 2023-07-05 00:00:00

This paper provides an overview of the hardware and software components used in our test bed project the NET Playground. All source information is stored in the GitLab repository (https://gitlab.com/Paulteck/net-playground). In the Hardware section, we present sketches and 3D views of mechanical parts and technical drawings of printed boards. The Software section discusses relay control using shell scripts and the utilization of Ansible for automation. We also introduce a C++ framework for connecting with the INA231 energy sensor. This paper serves as a reference for understanding and replicating our project's hardware and software components.
Demand Response by Aggregates of Domestic Water Heaters with Adaptive Model Predictive Control
Authors: F. Conte, S. Massucco, F. Silvestro, D. Cirio, M. Rapizza
Date: 2023-07-05 00:00:00
Tasks: Management, Model Predictive Control

This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This allows the control to be adaptive, i.e., able to adjust its decisions to the system characteristics, which vary over time due to the daily distribution of users hot water consumption. To answer the demand response requirements, aggregated power variations are realized by modifying the temperature set-points of the water heaters without compromising the users comfort. The developed approach allows tracking a regulation signal and mitigating the so-called rebound, i.e., the recovery of energy needed by the aggregate at the end of the service to return to the baseline thermal state. Analyses in a simulation environment allow the validation of the potentialities of the proposed method.
Hypercongestion, autonomous vehicle, and urban spatial structure
Authors: Takao Dantsuji, Yuki Takayama
Date: 2023-07-05 00:00:00
Tasks: Autonomous Vehicles

This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hypercongestion mitigation by perimeter control decreases the commuting cost in the short-run and results in a less dense urban spatial structure in the long-run. Furthermore, we reveal that the impact of autonomous vehicles depends on the presence of hypercongestion. Introduction of autonomous vehicles may increase the commuting cost in the presence of hypercongestion and cause a decrease in suburban population, but make cities spatially expanded outward.This result contradicts that of the standard bottleneck model. When perimeter control is implemented, the introduction of autonomous vehicles decreases the commuting cost and results in a less dense urban spatial structure. These results show that hypercongestion is a key factor that can change urban spatial structures.
Energy Consumption of Electric Vehicles: Effect of Lateral Dynamics
Authors: Simran Kumari, Susenjit Ghosh, Ashish R. Hota, Siddhartha Mukhopadhyay
Date: 2023-07-05 00:00:00
Tasks: Prediction

Current research on energy related problems such as eco-routing, eco-driving and range prediction for electric vehicles (EVs) primarily considers the effect of longitudinal dynamics on EV energy consumption. However, real-world driving includes longitudinal as well as lateral motion. Therefore, it is important to understand the effects of lateral dynamics on battery energy consumption. This paper conducts an analysis of the stated effect and validates its significance through simulations. Specifically, this study demonstrates that inclusion of the effect of lateral dynamics can improve accuracy and reliability of solutions in eco-routing, eco-driving and range prediction applications.
Convex Optimal Power Flow Based on Power Injection-based Equations and Its Application in Bipolar DC Distribution Network
Authors: Yiyao Zhou, Qianggang Wang, Yuan Chi, Jianquan Liao, Tao Huang, Niancheng Zhou, Xiaolong Xu, Xuefei Zhang
Date: 2023-07-05 00:00:00

Optimal power flow (OPF) is a fundamental tool for analyzing the characteristics of bipolar DC distribution network (DCDN). However, existing OPF models face challenges in reflecting the power distribution and exchange of bipolar DCDN directly since its decision variables are voltage and current. This paper addresses this issue by establishing a convex OPF model that can be used for the planning and operation of bipolar DCDN. First, the power flow characteristics of bipolar DCDN are revealed through power injection-based equations, upon which the original OPF model is established. Next, the original OPF model undergoes a transformation into a convex OPF model based on second-order cone programming (SOCP) through variable substitution, secondorder cone relaxation, McCormick relaxation, and first-order Taylor expansion, respectively. Finally, the sequence bound tightening algorithm (STBA) is employed to tighten the boundaries of McCormick envelopes in each iteration to ensure the exactness of the convex OPF model. The effectiveness of this novel OPF model for bipolar DCDN is verified through two case studies, i.e., capacity configuration of distributed generation (DG) and operation optimization of bipolar DCDN.
Wholesale Market Participation of DERA: Competitive DER Aggregation
Authors: Cong Chen, Ahmed S. Alahmed, Timothy D. Mount, Lang Tong
Date: 2023-07-05 00:00:00

We consider the aggregation of distributed energy resources (DERs) by a profit-seeking DER aggregator (DERA), participating directly in the wholesale electricity market and constrained by the distribution network access. We propose a competitive DER aggregation that maximizes the DERA's profit subject to that each customer of the DERA gains no less surplus and pays no higher energy cost than that under the regulated retail tariff. The DERA participates in the wholesale electricity market as virtual storage with optimized generation offers and consumption bids derived from the DERA's competitive aggregation. Also derived are DERA's bid curves for the distribution network access and DERA's profitability when competing with the regulated retail tariff. We show that, with the same distribution network access, the proposed DERA's wholesale market participation achieves the same welfare-maximizing outcome as when its customers participate directly in the wholesale market. We empirically evaluate how many DERAs can survive in the long-run equilibrium. Numerical studies also compare the proposed DERA with existing benchmarks on surpluses of DERA's customers and DERA profits as functions of the DER adoption level and the distribution network access.
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication
Authors: Yunhao Quan, Nan Cheng, Xiucheng Wang, Jinglong Shen, Longfei Ma, Zhisheng Yin
Date: 2023-07-05 00:00:00
Tasks: Collision Avoidance, Explainable Artificial Intelligence (XAI)

Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication. As a result, UAVs have been extensively used as aerial base stations (ABSs) to supplement ground-based cellular networks for various applications. However, existing UAV-assisted communication schemes mainly focus on trajectory optimization and power allocation, while ignoring the issue of collision avoidance during UAV flight. To address this issue, this paper proposes an interpretable UAV-assisted communication scheme that decomposes reliable UAV services into two sub-problems. The first is the constrained UAV coordinates and power allocation problem, which is solved using the Dueling Double DQN (D3QN) method. The second is the constrained UAV collision avoidance and trajectory optimization problem, which is addressed through the Monte Carlo tree search (MCTS) method. This approach ensures both reliable and efficient operation of UAVs. Moreover, we propose a scalable interpretable artificial intelligence (XAI) framework that enables more transparent and reliable system decisions. The proposed scheme's interpretability generates explainable and trustworthy results, making it easier to comprehend, validate, and control UAV-assisted communication solutions. Through extensive experiments, we demonstrate that our proposed algorithm outperforms existing techniques in terms of performance and generalization. The proposed model improves the reliability, efficiency, and safety of UAV-assisted communication systems, making it a promising solution for future UAV-assisted communication applications
Wholesale Market Participation of DERA: DSO-DERA-ISO Coordination
Authors: Cong Chen, Subhonmesh Bose, Timothy D. Mount, Lang Tong
Date: 2023-07-05 00:00:00

Distributed energy resource aggregators (DERAs) must share the distribution network together with the distribution utility in order to participate in the wholesale electricity markets that are operated by independent system operators (ISOs). We propose a forward auction that a distribution system operator (DSO) can utilize to allocate distribution network access limits to DERAs. As long as the DERAs operate within their acquired limits, these limits define operating envelopes that guarantee distribution network security, thus defining a mechanism that requires no real-time intervention from the DSOs for DERAs to participate in the wholesale markets. Our auctions take the form of robust and risk-sensitive markets with bids/offers from DERAs and utility's operational costs. Properties of the proposed auction, e.g., resulting surpluses of DSO and the DERAs, and the auction prices, along with empirical performance studies, are presented.
An Adaptive Overcurrent Protection for Solar-based DC Microgrids Using IEC 61850
Authors: Saeed Sanati, Maher Azzouz, Ahmed Awad
Date: 2023-07-04 00:00:00

Over-Current (OC) protection is one of the pervasive protections in solar-based DC microgrids. Fast operation is a key advantage of its popularity. On the other hand, utilizing OC in DC microgrids has some challenges that are not in AC grids. Some of these challenges are related to the grounding approach of the DC microgrid, and others are related to the high rise time of the fault current in DC microgrids. Considering these challenges, an adaptive OC scheme with high selectivity and speed is presented in this paper. The proposed scheme is communication-assisted and relies on IEC 61850 protocol. In this scheme, different setting groups for each OC relay are defined, and based on the grid and fault conditions, a setting group is selected. This option is performed considering the data transferred via communication level using IEC 61850 protocol between relays. To evaluate the efficiency of the proposed scheme, simulations using MATLAB software and the experimental tests using OPAL-RT real-time simulator and Zenon software are presented.
Safe Connectivity Maintenance of Underactuated Multi-Agent Networks in Dynamic Oceanic Environments
Authors: Nicolas Hoischen, Marius Wiggert, Claire J. Tomlin
Date: 2023-07-04 00:00:00

Autonomous multi-agent systems are increasingly being deployed in environments where winds and ocean currents have a significant influence. Recent work has developed control policies for single agents that leverage flows to achieve their objectives in dynamic environments. However, in multi-agent systems, these flows can cause agents to collide or drift apart and lose direct inter-agent communications, especially when agents have low propulsion capabilities. To address these challenges, we propose a hierarchical multi-agent control approach that allows arbitrary single-agent performance policies that are unaware of other agents to be used in multi-agent systems while ensuring safe operation. We first develop a safety controller using potential functions, solely dedicated to avoiding collisions and maintaining inter-agent communication. Next, we design a low-interference safe interaction (LISIC) policy that trades off the performance policy and the safety control to ensure safe and performant operation. Specifically, when the agents are at an appropriate distance, LISIC prioritizes the performance policy while smoothly increasing the safety controller when necessary. We prove that under mild assumptions on the flows experienced by the agents, our approach can guarantee safety. Additionally, we demonstrate the effectiveness of our method in realistic settings through an extensive empirical analysis with simulations of fleets of underactuated autonomous surface vehicles operating in dynamic ocean currents where these assumptions do not always hold.
The Quantum Advantage in Binary Teams and the Coordination Dilemma: Part II
Authors: Shashank A. Deshpande, Ankur A. Kulkarni
Date: 2023-07-04 00:00:00

In our previous work, we have shown that the use of a quantum architecture in decentralised control allows access to a larger space of control strategies beyond what is classically implementable through common randomness, and can lead to an improvement in the cost -- a phenomenon we called the quantum advantage. In the previous part of this two part series, we showed, however, that not all decision problems admit such an advantage. We identified a decision-theoretic property of the cost called the `coordination dilemma' as a necessary condition for the quantum advantage to manifest. In this article, we investigate the impact on the quantum advantage of a scalar parameter that captures the extent of the coordination dilemma. We show that this parameter can be bounded within an open interval for the quantum advantage to exist, and for some classes, we precisely identify this range of values. This range is found to be determined by the information of the agents.
The Quantum Advantage in Binary Teams and the Coordination Dilemma: Part I
Authors: Shashank A. Deshpande, Ankur A. Kulkarni
Date: 2023-07-04 00:00:00
Tasks: Articles

We have shown that entanglement assisted stochastic strategies allow access to strategic measures beyond the classically correlated measures accessible through passive common randomness, and thus attain a quantum advantage in decentralised control. In this two part series of articles, we investigate the decision theoretic origins of the quantum advantage within a broad superstructure of problem classes. Each class in our binary team superstructure corresponds to a parametric family of cost functions with a distinct algebraic structure. In this part, identify the only problem classes that benefit from quantum strategies. We find that these cost structures admit a special decision-theoretic feature -- `the coordination dilemma'. Our analysis hence reveals some intuition towards the utility of non-local quantum correlations in decentralised control.
Data Transmission and Communication via Electrolytic Flow Channel
Authors: Khoi Ly, Chong-Chan Kim, Robert Shepherd
Date: 2023-07-04 00:00:00

As an alternative approach to ionic data transmission with hydrogel as substrate, this work explores the possible applications of liquid electrolyte filling cavity of a stretchable, flexible elastomeric tubing, which is the primary ingredient used in redox flow battery systems. While hydrogel-based ionic impedance characterization and its data communication capability have been well studied, the multifunctional use of redox flow battery electrolyte for data communication, in addition to powering, is novel, especially in the context of soft robotics. This work also describes a simple signal conditioning technique that addresses the signal decaying problem due to long-distance ionic data transmission. Finally, we describe the prototypical design of a decentralized data communication between two, or possibly more, systems of power and control. This work presents the concept of a decentralized control of a robot's hydraulic actuation system, that will allow for the system's functional robustness in the event of one of the two, or possibly more, modules lose power or become severed from the robot's body.