pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Relay Selection Scheme for Relay-assisted HARQ
Authors: Weihang Ding, Mohammad Shikh-Bahaei
Date: 2023-05-04 00:00:00

In wireless communication networks, relays are required when the quality of the direct link between the source and the destination is not high enough to support reliable transmission because of long distances or obstacles. Selecting the proper relay node (RN) to support hybrid automatic repeat request (HARQ) is of great importance in such a relay-assisted network. Different from previous works, whether to participate in the transmission is determined by each RN itself in this work, thus reducing the overhead. As RNs do not need to obtain channel state information about the whole network, there is no significant overhead in the system. Using the numbers of transmission attempts required by both channels calculated from the obtained channel state information, each RN sets a timer and forwards the packet when time-out occurs. Simulation results show that our proposed method significantly improves the performance of the system. When the channels are of relatively high quality, the performance of our method is close to the optimal relay selection which requires full information about the network.
ALADIN-based Distributed Model Predictive Control with dynamic partitioning: An application to Solar Parabolic Trough Plants
Authors: P. Chanfreut, J. M. Maestre, D. Krishnamoorthy, E. F. Camacho
Date: 2023-05-04 00:00:00
Tasks: Clustering, Model Predictive Control

This article presents a distributed model predictive controller with time-varying partitioning based on the augmented Lagrangian alternating direction inexact Newton method (ALADIN). In particular, we address the problem of controlling the temperature of a heat transfer fluid (HTF) in a set of loops of solar parabolic collectors by adjusting its flow rate. The control problem involves a nonlinear prediction model, decoupled inequality constraints, and coupled affine constraints on the system inputs. The application of ALADIN to address such a problem is combined with a dynamic clustering-based partitioning approach that aims at reducing, with minimum performance losses, the number of variables to be coordinated. Numerical results on a 10-loop plant are presented.
A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators
Authors: Minwoo Lee, Kyu Tae Kim, Jongho Park
Date: 2023-05-04 00:00:00
Tasks: Computational Efficiency

Self-sustained oscillations are ubiquitous in nature and engineering. In this paper, we propose a novel output-only system-identification framework for identifying the system parameters of a self-sustained oscillator affected by Gaussian white noise. A Langevin model that characterizes the self-sustained oscillator is postulated, and the corresponding Fokker--Planck equation is derived from stochastic averaging. From the drift and diffusion terms of the Fokker--Planck equation, unknown parameters of the system are identified. We develop a numerically efficient algorithm for enhancing the accuracy of parameter identification. In particular, a modified Levenberg--Marquardt optimization algorithm tailored to output-only system identification is introduced. The proposed framework is demonstrated on both numerical and experimental oscillators with varying system parameters that develop into self-sustained oscillations. The results show that the computational cost required for performing the system identification is dramatically reduced by using the proposed framework. Also, system parameters that were difficult to be extracted with the existing method could be efficiently computed with the system identification method developed in this study. Pertaining to the robustness and computational efficiency of the presented framework, this study can contribute to an accurate and fast diagnosis of dynamical systems under stochastic forcing.
A Soft Coordination Method of Heterogeneous Devices in Distribution System Voltage Control
Authors: Licheng Wang, Tao Wang, Gang Huang, Ruifeng Yan, Kai Wang, Youbing Zhang, Shijie Cheng
Date: 2023-05-04 00:00:00
Tasks: Decision Making

With the continuous increase of photovoltaic (PV) penetration, the voltage control interactions between newly installed PV inverters and previously deployed on-load tap-changer (OLTC) transformers become ever more significant. To achieve coordinated voltage regulation, current methods often rely on a decision-making algorithm to fully take over the control of all devices, requiring OLTC to give up its existing tap switching logic and execute corresponding upgrades. Aiming at bridging this gap, a soft coordination framework is proposed in this paper. Specifically, the decision-making commands are only applied on inverters, and OLTC that retains its own operation rule will be indirectly controlled by the changed system voltage, which is a result of appropriately adjusting inverters' Var output. The proposed method achieves the soft coordination by establishing a modified actor-critic algorithm to train a proxy model of inverters. The well-trained proxy model can properly adjust inverters' Var output to "softly" coordinate OLTC's tap operations, which finally attains coordinated voltage regulation and line loss minimization. Simulation results verify the superiority of our proposed method over traditional ones in coordinating heterogeneous devices for voltage control.
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
Authors: Ziqing Zhu, Siqi Bu, Ka Wing Chan, Bin Zhou, Shiwei Xia
Date: 2023-05-04 00:00:00
Tasks: Philosophy, Reinforcement Learning (RL)

In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results.
Model-based and Data-based Dynamic Output Feedback for Externally Positive Systems
Authors: Abed AlRahman Al Makdah, Fabio Pasqualetti
Date: 2023-05-04 00:00:00

In this work, we derive dynamic output-feedback controllers that render the closed-loop system externally positive. We begin by expressing the class of discrete-time, linear, time-invariant systems and the class of dynamic controllers in the space of input-output behaviors, where a dynamic controller can be expressed as a static behavioral feedback gain. We leverage the static form of the controller to derive output-feedback controllers that achieve monotonic output tracking of a constant non-negative reference output. Further, we provide a direct data-driven approach to derive monotonic tracking output-feedback controllers for single-input-single-output (SISO) systems. Our approaches, model-based and data-based, allow us to obtain output-feedback controllers that render the closed-loop system externally positive. Finally, we validate our results numerically in a drone landing control problem.
Asynchronous Distributed Consensus with Minimum Communication
Authors: Vishal Sawant, Debraj Chakraborty, Debasattam Pal
Date: 2023-05-03 00:00:00

In this paper, the communication effort required in a multi-agent system (MAS) is minimized via an explicit optimization formulation. The paper considers a MAS of single-integrator agents with bounded inputs and a time-invariant communication graph. A new model of discrete asynchronous communication and a distributed consensus protocol based on it, are proposed. The goal of the proposed protocol is to minimize the aggregate number of communication instants of all agents, required to steer the state trajectories inside a pres-specified bounded neighbourhood within a pre-specified time. Due to information structure imposed by the underlying communication graph, an individual agent does not know the global parameters in the MAS, which are required for the above-mentioned minimization. To counter this uncertainty, the worst-case realizations of the global parameters are considered, which lead to min-max type optimizations. The control rules in the proposed protocol are obtained as the closed form solutions of these optimization problems. Hence, the proposed protocol does not increase the burden of run-time computation making it suitable for time-critical applications.
Renewable-Based Charging in Green Ride-Sharing
Authors: Elisabetta Perotti, Ana M. Ospina, Gianluca Bianchin, Andrea Simonetto, Emiliano Dall'Anese
Date: 2023-05-03 00:00:00

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users' willingness to ride-share affects the charging strategy and the quality of service.
An identification method for oscillators with response-dependent inertia
Authors: Yuval Harduf, Eyal Setter, Izhak Bucher
Date: 2023-05-03 00:00:00

This paper is concerned with identifying the instantaneous modal parameters of forced oscillatory systems with response-dependent generalized inertia (mass, inductance, or equivalent) based on their measured dynamics. An identification method is proposed, which is a variation of the "FORCEVIB" method. The method utilizes analytic signal representation and the properties of the Hilbert transform to obtain an analytic relationship between a system's natural frequency and damping coefficient to its response and excitation signals. The proposed method is validated by comparing the identification results to the asymptotic solution of a simple system with response-dependent inertia and is then demonstrated, numerically and experimentally, for other more complicated nonlinear systems.
Reducing End-to-End Latencies of Multi-Rate Cause-Effect Chains for the LET Model
Authors: Luiz Maia, Gerhard Fohler
Date: 2023-05-03 00:00:00
Tasks: Scheduling

The Logical Execution Time (LET) model has been gaining industrial attention because of its timing and data-flow deterministic characteristics, which simplify the computation of end-to-end latencies of multi-rate cause-effect chains at the cost of pessimistic latencies. In this paper, we propose a novel method to reduce the pessimism in the latencies introduced by LET, while maintaining its determinism. We propose a schedule-aware LET model that shortens the lengths and repositions LET's communication intervals resulting in less pessimistic end-to-end latencies. By adding dependencies between specific task instances, the method can further reduce the pessimism in the latency calculations of the LET model. If needed, e.g., for legacy reasons, our method can be applied to a subset of tasks only. We evaluate our work based on real world automotive benchmarks and randomly generated synthetic task sets. We compare our results with previous work and the LET model. The experiments show significant reductions of worst-case data age and worst-case reaction latency values.
Characterizing Compositionality of LQR from the Categorical Perspective
Authors: Baike She, Tyler Hanks, James Fairbanks, Matthew Hale
Date: 2023-05-02 00:00:00

Composing systems is a fundamental concept in modern control systems, yet it remains challenging to formally analyze how controllers designed for individual subsystems can differ from controllers designed for the composition of those subsystems. To address this challenge, we propose a novel approach to composing control systems based on resource sharing machines, a concept from applied category theory. We use resource sharing machines to investigate the differences between (i) the linear-quadratic regulator (LQR) designed directly for a composite system and (ii) the LQR that is attained through the composition of LQRs designed for each subsystem. We first establish novel formalisms to compose LQR control designs using resource sharing machines. Then we develop new sufficient conditions to guarantee that the LQR designed for a composite system is equal to the LQR attained through composition of LQRs for its subsystems. In addition, we reduce the developed condition to that of checking the controllability and observability of a certain linear, time-invariant system, which provides a simple, computationally efficient procedure for evaluating the equivalence of controllers for composed systems.
Learning Robust Data-based LQG Controllers from Noisy Data
Authors: Wenjie Liu, Jian Sun, Gang Wang, Francesco Bullo, Jie Chen
Date: 2023-05-02 00:00:00
Tasks: State Estimation

This paper addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign the linear quadratic Gaussian (LQG) controller based solely on data. The LQG controller comprises a linear quadratic regulator (LQR) and a steady-state Kalman observer; while the data-based LQR design problem has been previously studied, constructing the Kalman gain and the LQG controller from noisy data presents a novel challenge. In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data. Additionally, a data-based LQG controller is developed, which is shown to be equivalent to the model-based LQG controller. For cases where offline data are corrupted by noise, a robust data-based observer gain is constructed by tackling a relaxed SDP. The proposed controllers are proven to achieve robust global exponential stability (RGES) for state estimation and input-to-state practical stability (ISpS) under standard conditions. Finally, numerical tests are conducted to validate the proposed controllers' correctness and effectiveness.
Infrastructural Requirements and Regulatory Challenges of a Sustainable Urban Air Mobility Ecosystem
Authors: Árpád Takács, Tamás Haidegger
Date: 2023-05-02 00:00:00

The United Nations has long put on the discussion agenda the sustainability challenges of ur- banization, which have both direct and indirect effects on future regulation strategies. Undoubtedly, most initiatives target better quality of life, improved access to services & goods and environment pro- tection. As commercial aerial urban transportation may become a feasible research goal in the near future, the connection possibilities between cities and regions scale up. It is expected that the growing number of vertical takeoff & landing vehicles used for passenger and goods transportation will change the infrastructure of the cities, and will have a significant effect on the cityscapes as well. In addition to the widely discussed regulatory and safety issues, the introduction of elevated traffic also raises environmental concerns, which influences the existing and required service and control infrastructure, and thus significantly affects sustainability. This paper provides narrated overview of the most common aspects of safety, licensing and regulations for passenger vertical takeoff & landing vehicles, and highlights the most important aspects of infrastructure planning, design and operation, which should be taken into account to maintain and efficiently operate this new way of transportation, leading to a sustainable urban air mobility ecosystem.
Establishing a Learning Model for Correct Hand Hygiene Technique in a NICU
Authors: Irén A. Kopcsóné Németh, Csaba Nádor, László Szilágyi, Ákos Lehotsky, Tamás Haidegger
Date: 2023-05-02 00:00:00

The ability of healthcare workers to learn proper hand hygiene has been an understudied area of research. Generally, hand hygiene skills are regarded as a key contributor to reduce critical infections and healthcare-associated infections. In a clinical setup, at a Neonatal Intensive Care Unit (NICU), the outcome of a multi-modal training initiative was recorded, where objective feedback was provided to the staff. It was hypothesized that staff at the NICU are more sensitive towards applying increased patient safety measures. Outcomes were recorded as the ability to cover all hand surfaces with Alcohol-Based Handrub (ABHR), modelled as a time-series of measurements. The learning ability to rub in with 1.5 mL and with 3 mL was also assessed. As a secondary outcome, handrub consumption and infection numbers were recorded. It has been observed that some staff members were able to quickly learn the proper hand hygiene, even with the limited 1.5 mL, while others were not capable of acquiring the technique even with 3 mL. When analyzing the 1.5 mL group, it was deemed an insufficient ABHR amount, while with 3 mL, the critical necessity of skill training to achieve complete coverage was documented. Identifying these individuals helps the infection control staff to better focus their training efforts. The training led to a 157% increase in handrub consumption. The setting of the study did not allow to show a measurable reduction in the number of hospital infections. It has been concluded that the training method chosen by the staff greatly affects the quality of the outcomes.
Arax: A Runtime Framework for Decoupling Applications from Heterogeneous Accelerators
Authors: Manos Pavlidakis, Stelios Mavridis, Antony Chazapis, Giorgos Vasiliadis, Angelos Bilas
Date: 2023-05-02 00:00:00

Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as TensorFlow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available resources elastically during application execution, and (c) reducing the required programming effort. In this paper, we present Arax, a runtime system that decouples applications from heterogeneous accelerators within a server. First, Arax maps application tasks dynamically to available resources, managing all required task state, memory allocations, and task dependencies. As a result, Arax can share accelerators across applications in a server and adjust the resources used by each application as load fluctuates over time. dditionally, Arax offers a simple API and includes Autotalk, a stub generator that automatically generates stub libraries for applications already written for specific accelerator types, such as NVIDIA GPUs. Consequently, Arax applications are written once without considering physical details, including the number and type of accelerators. Our results show that applications, such as Caffe, TensorFlow, and Rodinia, can run using Arax with minimum effort and low overhead compared to native execution, about 12% (geometric mean). Arax supports efficient accelerator sharing, by offering up to 20% improved execution times compared to NVIDIA MPS, which supports NVIDIA GPUs only. Arax can transparently provide elasticity, decreasing total application turn-around time by up to 2x compared to native execution without elasticity support.
Stochastic Power Processing through Logic Operation of Power Packets
Authors: Shiu Mochiyama, Takashi Hikihara
Date: 2023-05-02 00:00:00

This article presents an application of the recently proposed logic operation of power based on power packetization. In a power packet dispatching system, the power supply can be considered as a sequence of power pulses, where the occurrence of pulses follows a probability that corresponds to the capacity of the power sources or power lines. In this study, we propose a processing scheme to reshape a stream of power packets from such stochastic sequences to satisfy the load demand. The proposed scheme is realized by extending the concept of stochastic computing to the power domain. We demonstrate the operation of the proposed scheme through experiments and numerical simulations by implementing it as a function of a power packet router, which forms a power packet dispatching network. The stochastic framework proposed in this study provides a new design foundation for low-power distribution networks as an embodiment of the close connection between the cyber and physical components.
Traction Control Allocation Employing Vehicle Motion Feedback Controller for Four-wheel-independent-drive Vehicle
Authors: David Vošahlík, Tomáš Haniš
Date: 2023-05-02 00:00:00

A novel vehicle traction algorithm solving the traction force allocation problem based on vehicle center point motion feedback controller is proposed in this paper. The center point motion feedback control system proposed utilizes individual wheel torque actuation assuming all wheels are individually driven. The approach presented is an alternative to the various direct optimization-based traction force/torque allocation schemes. The proposed system has many benefits, such as significant reduction of the algorithm complexity by merging most traction system functionalities into one. Such a system enables significant simplification, unification, and standardization of powertrain control design. Moreover, many signals needed by conventional traction force allocation methods are not required to be measured or estimated with the proposed approach, which are among others vehicle mass, wheel loading (normal force), and vehicle center of gravity location. Vehicle center point trajectory setpoints and measurements are transformed to each wheel, where the tracking is ensured using the wheel torque actuation. The proposed control architecture performance and analysis are shown using the nonlinear twin-track vehicle model implemented in Matlab $\&$ Simulink environment. The performance is then validated using high fidelity FEE CTU in Prague EFORCE formula model implemented in IPG CarMaker environment with selected test scenarios. Finally, the results of the proposed control allocation are compared to the state-of-the-art approach.