pwc-archive/papers-with-abstracts huggingface dataset:
Cloud Control of Connected Vehicle under Bi-directional Time-varying delay: An Application of Predictor-observer Structured Controller
Authors: Ji-An Pan, Qing Xu, Keqiang Li, Chunying Yang, Jianqiang Wang
Date: 2023-09-05 00:00:00

This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays. To mitigate the adverse effects of such delays, a novel predictor-observer structured controller is proposed which compensate for both measurable output delays and unmeasurable, yet bounded, input delays simultaneously. The study begins by novelly constructing an equivalent delay-free inter-connected system model that incorporates the Predictor-Observer controller, considering certain delay boundaries and model uncertainties. Subsequently, a stability analysis is conducted to assess the system's robustness under these conditions. Next, the connected vehicle lateral control scenario is built which contain high-fidelity vehicle dynamic model. The results demonstrate the controller's ability to accurately predict the system states, even under time-varying bi-directional delays. Finally, the proposed method is deployed in a real connected vehicle lateral control system. Comparative tests with a conventional linear feedback controller showcase significantly improved control performance under dominant bi-directional delay conditions, affirming the superiority of the proposed method against the delay.
Dynamic distance-based pricing scheme for high-occupancy-toll lanes along a freeway corridor
Authors: Irene Martínez, Wen-Long Jin
Date: 2023-09-05 00:00:00

Single-occupancy vehicles (SOVs) are charged to use the highoccupancy-toll (HOT) lanes, while high-occupancy-vehicles (HOVs) can drive in them at no cost. The pricing scheme for HOT lanes has been extensively studied at local bottlenecks or at the network level through computationally expensive simulations. However, the HOT lane pricing study on a freeway corridor with multiple origins and destinations as well as multiple interacting bottlenecks is a challenging problem for which no analytical results are available. In this paper, we attempt to fill the gap by proposing to study the traffic dynamics in the corridor based on the relative space paradigm. In this new paradigm, the interaction of multiple bottlenecks and trips can be captured with Vickrey's bathtub model by a simple ordinary differential equation. We consider three types of lane choice behavior and analyze their properties. Then, we propose a distance-based dynamic pricing scheme based on a linear combination of I-controllers. This closed-loop controller is independent of the model and feeds back the travel time difference between HOT lanes and general-purpose lanes. Given the mathematical tractability of the system model, we analytically study the performance of the proposed closed-loop control under constant demand and show the existence and stability of the optimal equilibrium. Finally, we verify the results with numerical simulations considering a typical peak period demand pattern. In the future, we are interested in extending this work and testing the performance of the proposed linear combination of I-controllers for other traffic flow models.
Load Shifting Versus Manual Frequency Reserve: Which One is More Appealing to Flexible Loads?
Authors: Peter A. V. Gade, Trygve Skjøtskift, Charalampos Ziras, Henrik W. Bindner, Jalal Kazempour
Date: 2023-09-05 00:00:00

This paper investigates how a thermostatically controlled load can deliver flexibility either in form of manual frequency restoration reserves (mFRR) or load shifting, and which one is financially more appealing to such a load. A supermarket freezer is considered as a representative flexible load, and a grey-box model describing its temperature dynamics is developed using real data from a supermarket in Denmark. Taking into account price and activation uncertainties, a two-stage stochastic mixed-integer linear program is formulated to maximize the flexibility value from the freezer. For practical reasons, we propose a linear policy to determine regulating power bids, and then linearize the mFRR activation conditions through the McCormick relaxation approach. For computational ease, we develop a decomposition technique, splitting the problem to a set of smaller subproblems, one per scenario. Examined on an out-of-sample simulation based on real Danish spot and balancing market prices in 2022, load shifting shows to be more profitable than mFRR provision, but is also more consequential for temperature deviations in the freezer.
Priority Queue Formulation of Agent-Based Bathtub Model for Network Trip Flows in the Relative Space
Authors: Irene Martinez, Wen-Long Jin
Date: 2023-09-05 00:00:00
Tasks: Computational Efficiency, Scheduling

Agent-based models have been extensively used to simulate the behavior of travelers in transportation systems because they allow for realistic and versatile modeling of interactions. However, traditional agent-based models suffer from high computational costs and rely on tracking physical locations, raising privacy concerns. This paper proposes an efficient formulation for the agent-based bathtub model (AB2M) in the relative space, where each agent's trajectory is represented by a time series of the remaining distance to its destination. The AB2M can be understood as a microscopic model that tracks individual trips' initiation, progression, and completion and is an exact numerical solution of the bathtub model for generic (time-dependent) trip distance distributions. The model can be solved for a deterministic set of trips with a given demand pattern (defined by the start time of each trip and its distance), or it can be used to run Monte Carlo simulations to capture the average behavior and variation stochastic demand patterns, described by probabilistic distributions of trip distances and departure times. To enhance the computational efficiency, we introduce a priority queue formulation, eliminating the need to update trip positions at each time step and allowing us to run large-scale scenarios with millions of individual trips in seconds. We systematically explore the scaling properties and discuss the introduction of biases and numerical errors. The systematic exploration of scaling properties of the modeling of individual agents in the relative space with the AB2M further enhances its applicability to large-scale transportation systems and opens up opportunities for studying travel time reliability, scheduling, and mode choices.
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems
Authors: Chayan Banerjee, Kien Nguyen, Clinton Fookes, Maziar Raissi
Date: 2023-09-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. In this work we explore their utility for reinforcement learning applications. We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL). We introduce a novel taxonomy with the reinforcement learning pipeline as the backbone to classify existing works, compare and contrast them, and derive crucial insights. Existing works are analyzed with regard to the representation/ form of the governing physics modeled for integration, their specific contribution to the typical reinforcement learning architecture, and their connection to the underlying reinforcement learning pipeline stages. We also identify core learning architectures and physics incorporation biases (i.e., observational, inductive and learning) of existing PIRL approaches and use them to further categorize the works for better understanding and adaptation. By providing a comprehensive perspective on the implementation of the physics-informed capability, the taxonomy presents a cohesive approach to PIRL. It identifies the areas where this approach has been applied, as well as the gaps and opportunities that exist. Additionally, the taxonomy sheds light on unresolved issues and challenges, which can guide future research. This nascent field holds great potential for enhancing reinforcement learning algorithms by increasing their physical plausibility, precision, data efficiency, and applicability in real-world scenarios.
Mitigation of stop-and-go traffic waves with intelligent vehicles at low market penetration rates
Authors: Irene Martínez
Date: 2023-09-04 00:00:00

Stop-and-go traffic patterns sometimes manifest on roadways without any discernible congestion triggers. Such a phenomenon has been observed on homogeneous ring roads without lane changes. With the development of vehicle technology and measurement sensors, multiple researchers have focused on studying the influence of automated vehicles on traffic. In particular, there is a focus on the design of string-stable adaptive cruise control (ACC) strategies to dampen stop-and-go waves. However, there is no systematic comparison among different strategies nor a quantitative analysis of the oscillation reduction at low market penetration rates (MPRs). This paper proposes a framework to evaluate the impact of low MPRs across multiple ACC strategies. Then, through Monte Carlo simulations, our findings indicate that multi-vehicle anticipation technology yields nearly equivalent benefits in mitigating stop-and-go patterns compared to full vehicular connectivity, even at a modest MPR of 1\%. In contrast, partial connectivity among vehicles only eliminates stop-and-go waves if the MPR is larger.
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
Authors: Manas Mejari, Ankit Gupta, Dario Piga
Date: 2023-09-04 00:00:00
Tasks: Scheduling

We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances. A data-set consisting of a single state-input-scheduling trajectory is gathered from the system, which is directly utilized to compute polytopic RCI set and controllers by solving a semidefinite program. The proposed method does not require an intermediate LPV model identification step. Through a numerical example, we show that the proposed approach can generate RCI sets with a relatively small number of data samples when the data satisfies certain excitation conditions.
Stochastic Model Predictive Control for Networked Systems with Random Delays and Packet Losses in All Channels
Authors: Marijan Palmisano, Martin Steinberger, Martin Horn
Date: 2023-09-04 00:00:00
Tasks: All, Model Predictive Control

A stochastic Model Predictive Control strategy for control systems with communication networks between the sensor node and the controller and between the controller and the actuator node is proposed. Data packets are subject to random delays and packet loss is possible; acknowledgments for received packets are not provided. The expected value of a quadratic cost is minimized subject to linear constraints; the set of all initial states for which the resulting optimization problem is guaranteed to be feasible is provided. The state vector of the controlled plant is shown to converge to zero with probability one.
On the Analysis and Synthesis of Wind Turbine Side-Side Tower Load Control via Demodulation
Authors: Atindriyo K. Pamososuryo, Sebastiaan P. Mulders, Riccardo Ferrari, Jan-Willem van Wingerden
Date: 2023-09-04 00:00:00

As wind turbine power capacities continue to rise, taller and more flexible tower designs are needed for support. These designs often have the tower's natural frequency in the turbine's operating regime, increasing the risk of resonance excitation and fatigue damage. Advanced load-reducing control methods are needed to enable flexible tower designs that consider the complex dynamics of flexible turbine towers during partial-load operation. This paper proposes a novel modulation-demodulation control (MDC) strategy for side-side tower load reduction driven by the varying speed of the turbine. The MDC method demodulates the periodic content at the once-per-revolution (1P) frequency in the tower motion measurements into two orthogonal channels. The proposed scheme extends the conventional tower controller by augmentation of the MDC contribution to the generator torque signal. A linear analysis framework into the multivariable system in the demodulated domain reveals varying degrees of coupling at different rotational speeds and a gain sign flip. As a solution, a decoupling strategy has been developed, which simplifies the controller design process and allows for a straightforward (but highly effective) diagonal linear time-invariant controller design. The high-fidelity OpenFAST wind turbine software evaluates the proposed controller scheme, demonstrating effective reduction of the 1P periodic loading and the tower's natural frequency excitation in the side-side tower motion.
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
Authors: Shuai Li, Haotian Zheng, Jiawei Wang, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
Date: 2023-09-04 00:00:00

The emergence of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i.e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i.e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control. This paper presents a comparative study between these two types of information flow topology (IFT) from the string stability perspective, with the role of maximum platoon size (MPS) also under investigation. Precisely, we provide a dynamical modeling framework for the mixed platoon under the multi-predecessor-following (MPF) topology and the multi-successor-leading (MSL) topology. Then, a unified method for string stability analysis is presented, with explicit consideration of both IFT and MPS. Numerical results suggest that MSL ("looking behind") outperforms MPF ("looking ahead" ) in mitigating traffic perturbations. In addition, increasing MPS could further improve string stability of mixed traffic flow.
Input Redundancy under Input and State Constraints (Extended version of the submission accepted to Automatica)
Authors: Jean-François Trégouët, Jérémie Kreiss
Date: 2023-09-04 00:00:00

For a given unconstrained dynamical system, input redundancy has been recently redefined as the existence of distinct inputs producing identical output for the same initial state. By directly referring to signals, this definition readily applies to any input-to-output mapping. As an illustration of this potentiality, this paper tackles the case where input and state constraints are imposed on the system. This context is indeed of foremost importance since input redundancy has been historically regarded as a way to deal with input saturations. An example illustrating how constraints can challenge redundancy is offered right at the outset. A more complex phenomenology is highlighted. This motivates the enrichment of the existing framework on redundancy. Then, a sufficient condition for redundancy to be preserved when imposing constraints is offered in the most general context of arbitrary constraints. It is shown that redundancy can be destroyed only when input and state trajectories lie on the border of the set of constraints almost all the time. Finally, those results are specialized and expanded under the assumption that input and state constraints are linear.
Adaptive Maximum Power Transfer for Movable device in Wireless Power Transfer system
Authors: Dong Jun-Kim
Date: 2023-09-04 00:00:00

More and more applications are adopting the charging topology of wireless power transmission. However, most wireless charging systems can not charge mobile devices which are moving in position while charging. Currently, many commercialized wireless charging systems adopt an inductive coupling method, which has very short charging distances. In addition, the frequency of the two coupled coils that produce maximum power transfer keeps varying, depending on the coupling coefficient that relies on the separation between coils, and this tendency becomes more severe when the coupling is strengthened at a close charging distance by the phenomenon called frequency splitting. Therefore, the existing wireless power transmission system using a fixed operating frequency can not optimize power transmission for a fluctuating charging environment as the coupling between coils changes, and charging efficiency is greatly reduced by frequency splitting when charging at a very short distance. To solve this problem, we proposed the method of estimating the RX side power and mutual inductance using the information from the TX side such as input impedance rather than using a direct communication link which adds more cost and complexity. Also, we derived a mathematical model for the above estimation method. To prove this mathematical model, the proposed wireless power transmission system was implemented in a SIMULINK environment, and the system model was validated through simulation. Also comparison between the adaptive frequency tracking method and static impedance matching circuit is made by analyzing simulation results.
Joint vehicle state and parameters estimation via Twin-in-the-Loop observers
Authors: Federico Dettù, Simone Formentin, Sergio Matteo Savaresi
Date: 2023-09-04 00:00:00
Tasks: Friction, State Estimation

Vehicular control systems are required to be both extremely reliable and robust to different environmental conditions, e.g. load or tire-road friction. In this paper, we extend a new paradigm for state estimation, called Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters describing the vehicle operating conditions. In such an approach, a digital-twin of the vehicle (usually already available to the car manufacturer) is employed on-board as a plant replica within a closed-loop scheme, and the observer gains are tuned purely from experimental data. The proposed approach is validated against experimental data, showing to significantly outperform the state-of-the-art solutions.
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors
Authors: Yuanyuan Wei, Sai Mu Dalike Abaxi, Nawaz Mehmood, Luoquan Li, Fuyang Qu, Guangyao Cheng, Dehua Hu, Yi-Ping Ho, Scott Wu Yuan, Ho-Pui Ho
Date: 2023-09-04 00:00:00
Tasks: Transfer Learning

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.
Joint Oscillation Damping and Inertia Provision Service for Converter-Interfaced Generation
Authors: Cheng Feng, Linbin Huang, Xiuqiang He, Yi Wang, Florian Dörfler, Chongqing Kang
Date: 2023-09-04 00:00:00
Tasks: Management

Power systems dominated by converter-interfaced distributed energy resources (DERs) typically exhibit weaker damping capabilities and lower inertia, compromising system stability. Although individual DER controllers are evolving to provide superior oscillation damping capabilities and inertia supports, there is a lack of network-wide coordinated management measures for multiple DERs, potentially leading to unexpected instability and cost-effectiveness problems. To address this gap, this paper introduces a hybrid oscillation damping and inertia management strategy for multiple DERs, considering network coupling effects, and seeks to encourage DERs to provide enhanced damping and inertia with appropriate economic incentives. We first formulate an optimization problem to tune and allocate damping and inertia coefficients for DERs, minimizing associated power and energy costs while ensuring hard constraints for system frequency stability and small-signal stability. The problem is built upon a novel convex parametric formulation that integrates oscillation mode location and frequency trajectory requirements, equipped with a theoretical guarantee, and eliminating the need for iterative tuning and computation burdens. Furthermore, to increase the willingness of DERs to cooperate, we further design appropriate economic incentives to compensate for DERs' costs based on the proposed cost minimization problem, and assess its impact on system cost-efficiency. Numerical tests highlight the effectiveness of the proposed method in promoting system stability and offer insights into potential economic benefits.
Evaluation Mappings of Spatial Accelerator Based On Data Placement
Authors: Zhipeng Wu, Yu Liu
Date: 2023-09-04 00:00:00
Tasks: Relation, Scheduling

The scheduling strategies of workloads are critical to fully exploiting the performance of spatial accelerators, accurate performance models are required to evaluate the mapping of workloads.Recent works proposed various cost-model to describe the dataflow of the spatial accelerator. However, they are less expressive about customized memory hierarchies and thus lead to inaccurate performance models. In this paper, we propose, PolyAcc, a framework for evaluating the mappings of workload on spatial accelerator based on data placement. The Data placement relation describes the temporal-spatial relation of data at different memory levels, which can accurately capture the runtime behavior of hardware units. Based on data placement relations, polyAcc accurately analyzes the data volume for different reuse patterns and estimate metrics, including data reuse, latency, and energy. Overall, polyAcc closely matches the ideal execution time and PE utilization for GEMM and Conv workloads, respectively achieves 0.82%, 18.8% improvements for execution time and energy consumption estimates in validation against Eyeriss architecture compared to the state-of-the-art framework.