pwc-archive/papers-with-abstracts huggingface dataset:
Optimization, guidance, and control of low-thrust transfers from the Lunar Gateway to low lunar orbit
Authors: Chiara Pozzi, Mauro Pontani, Alessandro Beolchi, Elena Fantino
Date: 2024-05-30 00:00:00

The Gateway will represent a primary space system useful for the Artemis program, Earth-Moon transportation, and deep space exploration. It is expected to serve as a staging location on the way to the lunar surface. This study focuses on low-thrust transfer dynamics, from the Near-Rectilinear Halo Orbit traveled by Gateway to a specified Low-altitude Lunar Orbit (LLO). This research addresses: (i) determination of the minimum-time low-thrust trajectory and (ii) design, implementation, and testing of a guidance and control architecture, for a space vehicle that travels from Gateway to LLO. Orbit dynamics is described in terms of modified equinoctial elements, in the context of a high-fidelity ephemeris model. The minimum-time trajectory from Gateway to a specified lunar orbit is detected through an indirect heuristic approach, which uses the analytical conditions arising in optimal control theory in conjunction with a heuristic technique. However, future missions will pursue a growing level of autonomy, and this circumstance implies the mandatory design of an efficient feedback guidance scheme, capable of compensating for nonnominal flight conditions. This research proposes nonlinear orbit control as a viable option for autonomous explicit guidance of low-thrust transfers from Gateway to LLO. This approach allows defining a feedback law that enjoys quasi-global stability properties without requiring any offline reference trajectory. The overall spacecraft dynamics is modeled including attitude control and actuation. The latter is demanded to an array of reaction wheels, arranged in a pyramidal configuration. Guidance, attitude control, and actuation are implemented in an iterative scheme. Monte Carlo simulations demonstrate that the guidance and control architecture is effective with random starting points from Gateway and the temporary unavailability of the propulsion system.
Efficient Industrial Refrigeration Scheduling with Peak Pricing
Authors: Rohit Konda, Jordan Prescott, Vikas Chandan, Jesse Crossno, Blake Pollard, Dan Walsh, Rick Bohonek, Jason R. Marden
Date: 2024-05-30 00:00:00
Tasks: Management, Scheduling

The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide efficient heat extraction. By adopting tools from inventory control, we characterize the structure of these optimal control policies, exploring the impact of different energy cost-rate structures such as time-of-use (TOU) pricing and peak pricing. While classical threshold policies are optimal under TOU costs, introducing peak pricing challenges their optimality, emphasizing the need for carefully designed control strategies in the presence of significant peak costs. We provide theoretical findings and simulation studies on this phenomenon, offering insights for more efficient industrial refrigeration management.
Deep Koopman Learning using Noisy Data
Authors: Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
Date: 2024-05-26 00:00:00
Tasks: Operator learning

This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under noisy observations. To this end, our proposed solution has two main advantages. First, the proposed method only requires the measurement noise to be bounded. Second, the proposed method modifies the existing deep Koopman operator formulations by characterizing the effect of the measurement noise on the Koopman operator learning and then mitigating it by updating the tunable parameter of the observable functions of the Koopman operator, making it easy to implement. The performance of the proposed method is demonstrated on several standard benchmarks. We then compare the presented method with similar methods proposed in the latest literature on Koopman learning.
Sensitivity Analysis for Piecewise-Affine Approximations of Nonlinear Programs with Polytopic Constraints
Authors: Leila Gharavi, Changrui Liu, Bart De Schutter, Simone Baldi
Date: 2024-05-30 00:00:00
Tasks: Model Predictive Control, Sensitivity

Nonlinear Programs (NLPs) are prevalent in optimization-based control of nonlinear systems. Solving general NLPs is computationally expensive, necessitating the development of fast hardware or tractable suboptimal approximations. This paper investigates the sensitivity of the solutions of NLPs with polytopic constraints when the nonlinear continuous objective function is approximated by a PieceWise-Affine (PWA) counterpart. By leveraging perturbation analysis using a convex modulus, we derive guaranteed bounds on the distance between the optimal solution of the original polytopically-constrained NLP and that of its approximated formulation. Our approach aids in determining criteria for achieving desired solution bounds. Two case studies on the Eggholder function and nonlinear model predictive control of an inverted pendulum demonstrate the theoretical results.
Speed Profile Definition for GLOSA Implementation on Buses Based on Statistical Analysis of Experimental Data
Authors: Daniele Vignarca, Stefano Arrigoni, Edoardo Sabbioni, Federico Cheli
Date: 2024-05-30 00:00:00

Intelligent Transportation Systems (ITS) are pushing an increasing interest and development when dealing with eco-driving systems. In this framework, this paper presents a method to define speed profiles specifically designed for Green Light Optimal Speed Advisory (GLOSA) systems on buses. GLOSA aims to optimize traffic flow by providing vehicles with real-time speed recommendations synchronized with traffic signal timings. Leveraging statistical analysis of experimental data collected from an urban bus, the study develops a methodology to extract meaningful insights into bus behaviour and traffic dynamics. The proposed approach considers road topology, scheduled bus stops, and signal timings to define simple although suitable speed profiles considering the peculiarities of the motion of a bus in an urban scenario. Through extensive data collection robust statistical data are defined, allowing the definition of vehicle motion profile for effectively develop and implement GLOSA systems. This research contributes to the advancement of Intelligent Transportation Systems by providing realistic data and practical insights for optimizing bus operations in urban environments.
Ancillary Services Provision by Cross-Voltage-Level Power Flow Control using Flexibility Regions
Authors: Christian Holger Nerowski, Zongjun Li, Christian Rehtanz
Date: 2024-05-30 00:00:00

The large-scale integration of distributed renewable energy sources into the electricity grid requires the investigation of new methods to ensure stability. For example, Active Distribution Networks (ADNs) can be used at (sub-) transmission levels for emergency operation, provided robust and efficient control is available. This paper investigates the use of Feasible Operating Regions (FORs) and Flexibility Regions (FRs) for Cross-Voltage-Level Power Flow Control (CPFC). The enhancement of network stability due to the provision of ancillary services is illustrated, as is the need for strengthened cooperation between Transmission (TSOs) and Distribution System Operators (DSOs). Optimal power flow methods are considered, focusing on computational advances through PieceWise Linearization (PWL) and convex relaxation techniques aiming to speed up runtime while keeping high accuracy. To illustrate the algorithms' benefits and drawbacks, they are analyzed using exemplary medium voltage grids.
System Identification for Lithium-Ion Batteries with Nonlinear Coupled Electro-Thermal Dynamics via Bayesian Optimization
Authors: Hao Tu, Xinfan Lin, Yebin Wang, Huazhen Fang
Date: 2024-05-30 00:00:00
Tasks: Bayesian Optimization, parameter estimation

Essential to various practical applications of lithium-ion batteries is the availability of accurate equivalent circuit models. This paper presents a new coupled electro-thermal model for batteries and studies how to extract it from data. We consider the problem of maximum likelihood parameter estimation, which, however, is nontrivial to solve as the model is nonlinear in both its dynamics and measurement. We propose to leverage the Bayesian optimization approach, owing to its machine learning-driven capability in handling complex optimization problems and searching for global optima. To enhance the parameter search efficiency, we dynamically narrow and refine the search space in Bayesian optimization. The proposed system identification approach can efficiently determine the parameters of the coupled electro-thermal model. It is amenable to practical implementation, with few requirements on the experiment, data types, and optimization setups, and well applicable to many other battery models.
Generation planning and operation under power stability constraints: A Hydro-Quebec use case
Authors: Alexandre Besner, Alexandre Blondin Massé, Abderrahman Bani, Mouad Morabit, François Berthaut, Luc Charest, David Ialongo, Yves Mbeutcha, Simon Couture-Gagnon, Julien Fournier
Date: 2024-05-30 00:00:00
Tasks: Specificity

Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits, and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by large hydroelectric dams located far north and the extensive 735kV transmission grid that allows the generated power to reach the majority of the load located thousands of kilometers away in the southern region of Quebec. The specificity of the grid has led HQ to develop monitoring tools responsible for generating so-called stability limits. Those stability limits take into account several nonlinear phenomena such as angular stability, frequency stability, or voltage stability. Since generation planning and operation tools rely mostly on mixed integer linear programming formulation, HQ had to adapt its tools to integrate stability limits into them. This paper presents the challenges it faced, especially considering its reserve monitoring tool and unit commitment tool.
Enhancing Battlefield Awareness: An Aerial RIS-assisted ISAC System with Deep Reinforcement Learning
Authors: Hyunsang Cho, Seonghoon Yoo, Bang Chul Jung, Joonhyuk Kang
Date: 2024-05-30 00:00:00
Tasks: Deep Reinforcement Learning, Integrated sensing and communication, ISAC

This paper considers a joint communication and sensing technique for enhancing situational awareness in practical battlefield scenarios. In particular, we propose an aerial reconfigurable intelligent surface (ARIS)-assisted integrated sensing and communication (ISAC) system consisting of a single access point (AP), an ARIS, multiple users, and a sensing target. With deep reinforcement learning (DRL), we jointly optimize the transmit beamforming of the AP, the RIS phase shifts, and the trajectory of the ARIS under signal-to-interference-noise ratio (SINR) constraints. Numerical results demonstrate that the proposed technique outperforms the conventional benchmark schemes by suppressing the self-interference and clutter echo signals or optimizing the RIS phase shifts.
Dynamic Slack Bus
Authors: Federico Milano
Date: 2024-05-30 00:00:00

This letter proposes a general dynamic formulation of slack bus. With this aim, the angle constraint imposed by the slack bus is redefined as a set of differential equations and an energy source. The existence and role of the transient component of this source is also discussed in the letter. Based on this framework, the letter shows that the swing equations of synchronous machines can be interpreted as distributed, dynamic, multi-variable, local slack buses. Other relevant cases, including primary and secondary frequency regulation, passive loads as well as grid following and grid forming converters are discussed.
Hypergraph-Aided Task-Resource Matching for Maximizing Value of Task Completion in Collaborative IoT Systems
Authors: Botao Zhu, Xianbin Wang
Date: 2024-05-30 00:00:00
Tasks: Hypergraph Matching

With the growing scale and intrinsic heterogeneity of Internet of Things (IoT) systems, distributed device collaboration becomes essential for effective task completion by dynamically utilizing limited communication and computing resources. However, the separated design and situation-agnostic operation of computing, communication and application layers create a fundamental challenge for rapid task-resource matching, which further deteriorate the overall task completion effectiveness. To overcome this challenge, we utilize hypergraph as a new tool to vertically unify computing, communication, and task aspects of IoT systems for an effective matching by accurately capturing the relationships between tasks and communication and computing resources. Specifically, a state-of-the-art task-resource matching hypergraph (TRM-hypergraph) model is proposed in this paper, which is used to effectively transform the process of allocating complex heterogeneous resources to convoluted tasks into a hypergraph matching problem. Taking into account computational complexity and storage, a game-theoretic hypergraph matching algorithm is proposed via considering the hypergraph matching problem as a non-cooperative multi-player clustering game. Numerical results demonstrate that the proposed TRM-hypergraph model achieves superior performance in matching of tasks and resources compared with comparison algorithms.
Assessing the impact of weather-induced uncertainties in large-scale electricity systems
Authors: Jan Peper, David Kröger, Jonathan Kipp, Florian Ziel, Christian Rehtanz
Date: 2024-05-30 00:00:00

The future energy system will largely depend on volatile renewable energy sources and temperature-dependent loads, which makes the weather a central influencing factor. This article presents a novel approach for simulating weather scenarios for robust large-scale power system analysis. By applying different signal analysis methods, historical weather data is decomposed into its spectral components, processed appropriately, and then used to generate random, self-consistent weather data. In this process, any weather parameters of different locations can be considered, while their respective dependencies are mapped. The added value is demonstrated by coupling with a state-of-the-art large-scale energy system model for Europe. It is shown that the integrated consideration of different weather influences allows a quantification of the range of fluctuation of various parameters - such as the feed-in of wind and solar power - and thus provides the basis for future resilient grid planning approaches.
Flexible Agent-based Modeling Framework to Evaluate Integrated Microtransit and Fixed-route Transit Designs: Mode Choice, Supernetworks, and Fleet Simulation
Authors: Siwei Hu, Michael F. Hyland, Ritun Saha, Jacob J. Berkel, Geoffrey Vander Veen
Date: 2024-05-29 00:00:00

The integration of traditional fixed-route transit (FRT) and more flexible microtransit has been touted as a means of improving mobility and access to opportunity, increasing transit ridership, and promoting environmental sustainability. To help evaluate integrated FRT and microtransit public transit (PT) system (henceforth ``integrated fixed-flex PT system'') designs, we propose a high-fidelity modeling framework that provides reliable estimates for a wide range of (i) performance metrics and (ii) integrated fixed-flex PT system designs. We formulate the mode choice equilibrium problem as a fixed-point problem wherein microtransit demand is a function of microtransit performance, and microtransit performance depends on microtransit demand. We propose a detailed agent-based simulation modeling framework that includes (i) a binary logit mode choice model (private auto vs. transit), (ii) a supernetwork-based model and pathfinding algorithm for multi-modal transit path choice where the supernetwork includes pedestrian, FRT, and microtransit layers, (iii) a detailed mobility-on-demand fleet simulator called FleetPy to model the supply-demand dynamics of the microtransit service. In this paper, we illustrate the capabilities of the modeling framework by analyzing integrated fixed-flex PT system designs that vary the following design parameters: FRT frequencies and microtransit fleet size, service region structure, virtual stop coverage, and operating hours. We include case studies in downtown San Diego and Lemon Grove, California. The computational results show that the proposed modeling framework converges to a mode choice equilibrium. Moreover, the scenario results imply that introducing a new microtransit service decreases FRT ridership and requires additional subsidies, but it significantly increases job accessibility and slightly reduces total VMT.
Best Ergodic Averages via Optimal Graph Filters in Reversible Markov Chains
Authors: Naci Saldi
Date: 2024-05-29 00:00:00

In this paper, we address the problem of finding the best ergodic or Birkhoff averages in the ergodic theorem to ensure rapid convergence to a desired value, using graph filters. Our approach begins by representing a function on the state space as a graph signal, where the (directed) graph is formed by the transition probabilities of a reversible Markov chain. We introduce a concept of graph variation, enabling the definition of the graph Fourier transform for graph signals on this directed graph. Viewing the iteration in the ergodic theorem as a graph filter, we recognize its non-optimality and propose three optimization problems aimed at determining optimal graph filters. These optimization problems yield the Bernstein, Chebyshev, and Legendre filters. Numerical testing reveals that while the Bernstein filter performs slightly better than the traditional ergodic average, the Chebyshev and Legendre filters significantly outperform the ergodic average, demonstrating rapid convergence to the desired value.
Global and local observability of hypergraphs
Authors: Chencheng Zhang, Hao Yang, Shaoxuan Cui, Bin Jiang, Ming Cao
Date: 2024-05-29 00:00:00

This paper studies observability for non-uniform hypergraphs with inputs and outputs. To capture higher-order interactions, we define a canonical non-homogeneous dynamical system with nonlinear outputs on hypergraphs. We then construct algebraic necessary and sufficient conditions based on polynomial ideals and varieties for global observability at an initial state of hypergraphs. An example is given to illustrate the proposed criteria for observability. Further, necessary and sufficient conditions for local observability are derived based on rank conditions of observability matrices, which provide a framework to study local observability for non-uniform hypergraphs. Finally, the similarity of observability for hypergraphs is proposed using similarity of tensors, which reveals the relation of observability between two hypergraphs and helps to check the observability intuitively.
A new platooning model for connected and autonomous vehicles to improve string stability
Authors: Shouwei Hui, Michael Zhang
Date: 2024-05-29 00:00:00
Tasks: Autonomous Vehicles

This paper presents a novel approach to coordinated vehicle platooning, where the platoon followers communicate solely with the platoon leader. A dynamic model is proposed to account for driving safety under communication delays. General linear stability results are mathematically proven, and numerical simulations are performed to analyze the impact of model parameters in two scenarios: a ring road with initial disturbance and an infinite road with periodic disturbance. The simulation outcomes align with the theoretical analysis, demonstrating that the proposed "look-to-the-leader" platooning strategy significantly outperforms conventional car-following strategies, such as following one or two vehicles ahead, in terms of traffic flow stabilization. This paper introduces a new perspective on organizing platoons for autonomous vehicles, with implications for enhancing traffic stability.
Modeling and Control of a Novel Bi-Quadcopter with Auxiliary Thruster Mechanism
Authors: Vijay Reddy Vundela, Vijay Muralidharan
Date: 2024-05-29 00:00:00
Tasks: Position

In this paper, a new under-actuated Bi-Quadcopter Unmanned Aerial Vehicle is introduced. The proposed drone configuration can be controlled similar to a Bicopter. The dynamics of the proposed Bi-Quadcopter is developed using the Newton-Euler approach. Using the force decomposition technique, a mapping between the control wrench and actuator inputs is developed. A nonlinear position control is applied for the Bi-Quadcopter using the quaternion-based cascaded attitude controller. The performance of the proposed UAV with the control algorithm is verified through simulations. Finally, the actuator failure scenarios were analyzed.
Identifying the Most Influential Driver Nodes for Pinning Control of Multi-Agent Systems with Time-Varying Topology
Authors: Guangrui Zhang, Zhaohui Liu, Xinghuo Yu, Mahdi Jalili
Date: 2024-05-29 00:00:00

Identifying the most influential driver nodes to guarantee the fastest synchronization speed is a key topic in pinning control of multi-agent systems. This paper develops a methodology to find the most influential pinning nodes under time-varying topologies. First, we provide the pinning control synchronization conditions of multi-agent systems. Second, a method is proposed to identify the best driver nodes that can guarantee the fastest synchronization speed under periodically switched systems. We show that the determination of the best driver nodes is independent of the system matrix under certain conditions. Finally, we develop a method to estimate the switching frequency threshold that can make the selected best driver nodes remain the same as the average system. Numerical simulations reveal the feasibility of these methods.
Signal-Comparison-Based Distributed Estimation Under Decaying Average Data Rate Communications
Authors: Jieming Ke, Xiaodong Lu, Yanlong Zhao, Ji-Feng Zhang
Date: 2024-05-29 00:00:00

The paper investigates the distributed estimation problem under low bit rate communications. Based on the signal-comparison (SC) consensus protocol under binary-valued communications, a new consensus+innovations type distributed estimation algorithm is proposed. Firstly, the high-dimensional estimates are compressed into binary-valued messages by using a periodic compressive strategy, dithered noises and a sign function. Next, based on the dithered noises and expanding triggering thresholds, a new stochastic event-triggered mechanism is proposed to reduce the communication frequency. Then, a modified SC consensus protocol is applied to fuse the neighborhood information. Finally, a stochastic approximation estimation algorithm is used to process innovations. The proposed SC-based algorithm has the advantages of high effectiveness and low communication cost. For the effectiveness, the estimates of the SC-based algorithm converge to the true value in the almost sure and mean square sense. A polynomial almost sure convergence rate is also obtained. For the communication cost, the local and global average bit rates for communications decay to zero at a polynomial rate. The trade-off between the convergence rate and the communication cost is established through event-triggered coefficients. A better convergence rate can be achieved by decreasing event-triggered coefficients, while lower communication cost can be achieved by increasing event-triggered coefficients. A simulation example is given to demonstrate the theoretical results.