pwc-archive/papers-with-abstracts huggingface dataset:
Approximating arrival costs in distributed moving horizon estimation: A recursive method
Authors: Xiaojie Li, Xunyuan Yin
Date: 2024-06-10 00:00:00
Tasks: Chemical Process

In this paper, we present a new approach to distributed moving horizon estimation for constrained nonlinear processes. The method involves approximating the arrival costs of local estimators through a recursive framework. First, distributed full-information estimation for linear unconstrained systems is presented, which serves as the foundation for deriving the analytical expression of the arrival costs for the local estimators. Subsequently, we develop a recursive arrival cost design for linear distributed moving horizon estimation. Sufficient conditions are derived to ensure the stability of the estimation error for constrained linear systems. Next, we extend the arrival cost design derived for linear systems to account for nonlinear systems, and a partition-based constrained distributed moving horizon estimation algorithm for nonlinear systems is formulated. A benchmark chemical process is used to illustrate the effectiveness and superiority of the proposed method.
Economic and Environmental Sustainability Through Reshoring: A Case Study
Authors: MD Parvez Shaikh, MD Sarder
Date: 2024-06-10 00:00:00

Not too long ago, offshoring was considered a panacea for many U.S. companies to achieve economic sustainability. Offshoring also created an unnecessary movement of goods between the point of consumption and the point of sourcing and hence contributed to greenhouse gas emissions. With many things changed, hundreds of U.S. companies have started Reshoring. Due to supply chain disruptions and increased tax implications, including tariffs, there is a growing desire among companies to achieve economic and environmental sustainability through reshoring. This model case study highlighted the common offshoring challenges and demonstrated new methods/solutions for the companies to save their bottom line. Using the Reshorability Index (RI) and Total Cost of Ownership (TCO) we developed a model to show which products or components we should bring back to the U.S. instead of continuing offshoring. From this study, we have found out that reshoring is not only an economically profitable decision but also has a positive impact on reducing GHG (Greenhouse Gas) emissions. Our research found that the companies that currently offshore heavy products will benefit more from implementing our developed model. Leveraging this model, industries can identify, and compare the ownership cost of their purchased materials and take the decision on potential reshoring. Additionally, companies will be able to calculate the GHG emission and identify the reduction of such emissions due to reshoring.
Statistical Delay and Error-Rate Bounded QoS Provisioning for AoI-Driven 6G Satellite-Terrestrial Integrated Networks Using FBC
Authors: Jingqing Wang, Wenchi Cheng, H. Vincent Poor
Date: 2024-06-09 00:00:00

As one of the pivotal enablers for 6G, satellite-terrestrial integrated networks have emerged as a solution to provide extensive connectivity and comprehensive 3D coverage across the spatial-aerial-terrestrial domains to cater to the specific requirements of 6G massive ultra-reliable and low latency communications (mURLLC) applications, while upholding a diverse set of stringent quality-of-service (QoS) requirements. In the context of mURLLC satellite services, the concept of data freshness assumes paramount significance, as the use of outdated data may lead to unforeseeable or even catastrophic consequences. To effectively gauge the degree of data freshness for satellite-terrestrial integrated communications, the notion of age of information (AoI) has recently emerged as a novel dimension of QoS metrics to support time-sensitive applications. Nonetheless, the research efforts directed towards defining novel diverse statistical QoS provisioning metrics, including AoI, delay, and reliability, while accommodating the dynamic and intricate nature of satellite-terrestrial integrated environments, are still in their infancy. To overcome these problems, in this paper we develop analytical modeling formulations/frameworks for statistical QoS over 6G satellite-terrestrial integrated networks using hybrid automatic repeat request with incremental redundancy (HARQ-IR) in the finite blocklength regime. In particular, first we design the satellite-terrestrial integrated wireless network architecture model and AoI metric model. Second, we characterize the peak-AoI bounded QoS metric using HARQ-IR protocol. Third, we develop a set of new fundamental statistical QoS metrics in the finite blocklength regime. Finally, extensive simulations have been conducted to assess and analyze the efficacy of statistical QoS schemes for satellite-terrestrial integrated networks.
Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach
Authors: Akin Catak, Ege C. Altunkaya, Mustafa Demir, Emre Koyuncu, Ibrahim Ozkol
Date: 2024-06-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a flight envelope protection algorithm on a longitudinal axis that leverages reinforcement learning (RL). By considering limits on variables such as angle of attack, load factor, and pitch rate, the algorithm counteracts excessive pilot or control commands with restoring actions. Unlike traditional methods requiring manual tuning, RL facilitates the approximation of complex functions within the trained model, streamlining the design process. This study demonstrates the promising results of RL in enhancing flight envelope protection, offering a novel and easy-to-scale method for safety-ensured flight.
Adaptive Output Tracking Control with Reference Model System Uncertainties
Authors: Gang Tao
Date: 2024-06-08 00:00:00

This paper develops adaptive output tracking control schemes with the reference output signal generated from an unknown reference system whose output derivatives are also unknown. To deal with such reference system uncertainties, an expanded adaptive controller structure is developed to include a parametrized estimator of the equivalent reference input signal. Without using the knowledge of the reference system transfer function and equivalent input, both are the critical components of a traditional model reference adaptive control (MRAC) scheme, the developed new MRAC schemes designed for various cases plant and reference model uncertainties, ensure completely parametrized error systems and stable parameter adaptation, leading to the desired closed-loop system stability and asymptotic output tracking.
The Development of the Reproductive Healthcare Equity Algorithm (RHEA)
Authors: Shriya Karam, Lauren Shanos, Jessica Ford, Lorenzo Castaneda, Megan S. Ryerson, Rakesh Vohra
Date: 2024-06-08 00:00:00

After the repeal of Roe vs. Wade in June 2022, women face long-distance travel across state lines to access abortion care. For women who also face socioeconomic hardship, travel for abortion care is a significant burden. To ease this burden, abortion access nonprofits are funding and/or supplying transportation to abortion clinics. However, due to the uneven distribution of demand and supply for abortions, these nonprofits do not have efficient logistical operations. As a result, low-income, underserved women may not have access to adequate reproductive healthcare, thus widening healthcare inequity gaps. Nonprofits may also risk not serving the needs of vulnerable women without access to adequate reproductive healthcare, and in doing so, waste resources, money, and volunteer hours. To address these challenges, we create an interactive, web-based planning tool, the Reproductive Healthcare Equity Algorithm (RHEA), to guide nonprofits in strategically allocating resources and serving demand. RHEA leverages an optimization model to determine the maximum flow and minimum transportation cost to route women across a network of counties and abortion clinics, subject to transportation supply, budget, and time constraints for one day of operations for a nonprofit. In doing so, we collaborate with abortion access nonprofits to cater our model design and interface development to their needs and considerations. Ultimately, we seek to optimize resource allocation for nonprofits providing abortion care logistics and improve abortion access for low-income, underserved women.
A Generalized Pointing Error Model for FSO Links with Fixed-Wing UAVs for 6G: Analysis and Trajectory Optimization
Authors: Hyung-Joo Moon, Chan-Byoung Chae, Kai-Kit Wong, Mohamed-Slim Alouini
Date: 2024-06-08 00:00:00

Free-space optical (FSO) communication is a promising solution to support wireless backhaul links in emerging 6G non-terrestrial networks. At the link level, pointing errors in FSO links can significantly impact capacity, making accurate modeling of these errors essential for both assessing and enhancing communication performance. In this paper, we introduce a novel model for FSO pointing errors in unmanned aerial vehicles (UAVs) that incorporates three-dimensional (3D) jitter, including roll, pitch, and yaw angle jittering. We derive a probability density function for the pointing error angle based on the relative position and posture of the UAV to the ground station. This model is then integrated into a trajectory optimization problem designed to maximize energy efficiency while meeting constraints on speed, acceleration, and elevation angle. Our proposed optimization method significantly improves energy efficiency by adjusting the UAV's flight trajectory to minimize exposure to directions highly affected by jitter. The simulation results emphasize the importance of using UAV-specific 3D jitter models in achieving accurate performance measurements and effective system optimization in FSO communication networks. Utilizing our generalized model, the optimized trajectories achieve up to 11.8 percent higher energy efficiency compared to those derived from conventional Gaussian pointing error models.
Practical Explicit-time Stabilization of a Proportional Control System
Authors: Wen Yan, Tao Zhao
Date: 2024-06-08 00:00:00

Proportional control can be realized directly through the amplification of analog signals, and it also has the advantage of easy tuning parameters in digital signal control. However, it is difficult for the proportional control to preset the upper bound of settling time. To address this problem, a novel practical explicit-time control method is proposed. In bounded initial condition, this method makes this system error converge to a predefined neighborhood of zero within an explicit time. More specifically, the initial condition set and conditionally stable set are solved by practical explicit-time stabilization theorem. Based on that, a proportional feedback control is founded to achieve practical conditional fixed-time stability.
Compensation for reactive power and harmonic current drawn by a non-linear load in a pv-micro hydro grid
Authors: Raj Krishna Nepal, Bibek Khanal, Sanket Khatiwada, Nirajan Bhandari, Bishal Rijal, Raisha Karmacharya, Ajay Thapa
Date: 2024-06-08 00:00:00

This paper presents a simulation approach to enhance the power quality of a PV-micro hydro grid supplying both linear consumer load and non-linear industrial load by integrating Shunt Active Power Filter (SAPF), utilizing instantaneous PQ theory and hysteresis current control band logic. The non-linear load draws reactive power and harmonic current from the source thereby affecting the power quality. The integration of the SAPF at the point of common coupling (PCC) offers reactive power and harmonic current compensation, ensuring that the current supply to the grid remains nearly sinusoidal and proportional to the active power. By injecting equal and opposite harmonic components, the SAPF effectively reduces Total Harmonic Distortion (THD) from 7% to 2.96%, thereby enhancing the overall power quality of the PV-micro hydro grid system.
Active Islanding Detection Using Pulse Compression Probing
Authors: Nicholas Piaquadio, N. Eva Wu, Morteza Sarailoo
Date: 2024-06-08 00:00:00

An islanding detection scheme is developed using pulse compression probing (PCP). A state space system realization is taken from the probing output. The nu-gap metric is applied to compare the measured system to fully intact system and classify it as islanded, or grid-connected. The designed detector displays fast operation, accurate islanding detection results under varying grid condition, and is physically implementable at the terminals of an inverter. The method is verified via electro-magnetic transient (EMT) simulation on a modified IEEE 34 bus test system with randomized loads and simultaneous probing at three independent solar plants, with the probing signal directly implemented into the logic of a switching inverter model.
Risk-Aware Finite-Horizon Social Optimal Control of Mean-Field Coupled Linear-Quadratic Subsystems
Authors: Dhairya Patel, Margaret Chapman
Date: 2024-06-07 00:00:00

We formulate and solve an optimal control problem with cooperative, mean-field coupled linear-quadratic subsystems and additional risk-aware costs depending on the covariance and skew of the disturbance. This problem quantifies the variability of the subsystem state energy rather than merely its expectation. In contrast to related work, we develop an alternative approach that illuminates a family of matrices with many analytical properties, which are useful for effectively extracting the mean-field coupled solution from a standard LQR solution.
Structured physics-guided neural networks for electromagnetic commutation applied to industrial linear motors
Authors: Max Bolderman, Mircea Lazar, Hans Butler
Date: 2024-06-07 00:00:00
Tasks: Position

Mechatronic systems are described by an interconnection of the electromagnetic part, i.e., a static position-dependent nonlinear relation between currents and forces, and the mechanical part, i.e., a dynamic relation from forces to position. Commutation inverts a model of the electromagnetic part of the system, and thereby removes the electromagnetic part from the position control problem. Typical commutation algorithms rely on simplified models derived from physics-based knowledge, which do not take into account position dependent parasitic effects. In turn, these commutation related model errors translate into position tracking errors, which limit the system performance. Therefore, in this work, we develop a data-driven approach to commutation using physics-guided neural networks (PGNNs). A novel PGNN model is proposed which structures neural networks (NNs) to learn specific motor dependent parasitic effects. The PGNN is used to identify a model of the electromagnetic part using force measurements, after which it is analytically inverted to obtain a PGNN-based commutation algorithm. Motivated by industrial applications, we develop an input transformation to deal with systems with fixed commutation, i.e., when the currents cannot be controlled. Real-life experiments on an industrial coreless linear motor (CLM) demonstrate a factor 10 improvement in the commutation error in driving direction and a factor 4 improvement in the position error with respect to classical commutation in terms of the mean--squared error (MSE).
Asymptotic Analysis of Near-Field Coupling in Massive MISO and Massive SIMO Systems
Authors: Aniol Martí, Jaume Riba, Meritxell Lamarca, Xavier Gràcia
Date: 2024-06-07 00:00:00
Tasks: valid

This paper studies the receiver to transmitter antenna coupling in near-field communications with massive arrays. Although most works in the literature consider that it is negligible and approximate it by zero, there is no rigorous analysis on its relevance for practical systems. In this work, we leverage multiport communication theory to obtain conditions for the aforementioned approximation to be valid in MISO and SIMO systems. These conditions are then particularized for arrays with fixed inter-element spacing and arrays with fixed size.
Colored Petri Nets for Modeling and Simulation of a Green Supply Chain System
Authors: Daffa R. Kaiyandra, Farizal F, Naly Rakoto
Date: 2024-06-07 00:00:00
Tasks: Management

Green supply chain is an emerging approach in supply chain management to reduce environmental impact of the process concerning the flow of goods and materials. As a discrete-event system, supply chain can be modeled using Petri Nets. Colored Petri Nets (CPNs) extend the classical Petri net formalism with data, time and hierarchy. These extensions makes it possible to deal with the green aspects of the supply chain.This paper deals with the Colored Petri Net approach to model and simulate a green supply chain system. The forward supply chain network starts from the raw material suppliers, to the manufacturers, wholesalers, retailers and to the final customers. The reverse supply chain network is made of collecting points, recycling plants, disassembly plants,and secondary material market. A government environment agency plays the role of regulation of the recycling process. A colored Petri net model of the green supply chain is developed and simulated with CPN Tools, a dedicated software for CPN models. The simulation results as well as the state-space analysis results validate the correctness of the model.
Statistical AoI, Delay, and Error-Rate Bounded QoS Provisioning for Satellite-Terrestrial Integrated Networks
Authors: Jingqing Wang, Wenchi Cheng, H. Vincent Poor
Date: 2024-06-07 00:00:00

Massive ultra-reliable and low latency communications (mURLLC) has emerged to support wireless time/error-sensitive services, which has attracted significant research attention while imposing several unprecedented challenges not encountered before. By leveraging the significant improvements in space-aerial-terrestrial resources for comprehensive 3D coverage, satellite-terrestrial integrated networks have been proposed to achieve rigorous and diverse quality-of-services (QoS) constraints of mURLLC. To effectively measure data freshness in satellite communications, recently, age of information (AoI) has surfaced as a novel QoS criterion for ensuring time-critical applications. Nevertheless, because of the complicated and dynamic nature of network environments, how to efficiently model multi-dimensional statistical QoS provisioning while upper-bounding peak AoI, delay, and error-rate for diverse network segments is still largely open. To address these issues, in this paper we propose statistical QoS provisioning schemes over satellite-terrestrial integrated networks in the finite blocklength regime. In particular, first we establish a satellite-terrestrial integrated wireless network architecture model and an AoI metric model. Second, we derive a series of fundamental statistical QoS metrics including peak-AoI bounded QoS exponent, delay-bounded QoS exponent, and error-rate bounded QoS exponent. Finally, we conduct a set of simulations to validate and evaluate our proposed statistical QoS provisioning schemes over satellite-terrestrial integrated networks.
Building Hybrid B-Spline And Neural Network Operators
Authors: Raffaele Romagnoli, Jasmine Ratchford, Mark H. Klein
Date: 2024-06-06 00:00:00
Tasks: Inductive Bias

Control systems are indispensable for ensuring the safety of cyber-physical systems (CPS), spanning various domains such as automobiles, airplanes, and missiles. Safeguarding CPS necessitates runtime methodologies that continuously monitor safety-critical conditions and respond in a verifiably safe manner. A fundamental aspect of many safety approaches involves predicting the future behavior of systems. However, achieving this requires accurate models that can operate in real time. Motivated by DeepONets, we propose a novel strategy that combines the inductive bias of B-splines with data-driven neural networks to facilitate real-time predictions of CPS behavior. We introduce our hybrid B-spline neural operator, establishing its capability as a universal approximator and providing rigorous bounds on the approximation error. These findings are applicable to a broad class of nonlinear autonomous systems and are validated through experimentation on a controlled 6-degree-of-freedom (DOF) quadrotor with a 12 dimensional state space. Furthermore, we conduct a comparative analysis of different network architectures, specifically fully connected networks (FCNN) and recurrent neural networks (RNN), to elucidate the practical utility and trade-offs associated with each architecture in real-world scenarios.
Safe Sliding Mode Controllers for Nonlinear Uncertain Systems
Authors: Yazdan Batmani, Mohammadreza Davoodi
Date: 2024-06-06 00:00:00

In this study, we present a novel sliding mode safety-critical controller designed to address both stability and safety concerns in a class of nonlinear uncertain systems. The controller features two feedback loops: an inner loop designed by conventional sliding mode techniques and an outer safeguarding loop aimed at enhancing system safety. The inner loop, while ensuring asymptotic stability of the closed-loop system, may not guarantee compliance with safety constraints. To overcome this limitation and ensure both stability and safety, the outer loop introduces a correction term known as the safeguarding control signal. This signal is added to the unsafe control signal generated by the inner loop, effectively modifying it to meet the required safety constraints. To design the safeguarding control law, we integrate the system dynamics with an additional state variable. The dynamics of this augmented state are derived based on a stability constraint obtained from Lyapunov theory. By utilizing a control barrier function for the augmented system, we determine the safeguarding control signal, which ensures the system operates within the defined safety constraint. The proposed safety-critical controller exhibits finite-time convergence to the sliding manifold. To mitigate interference between the inner and outer loops, strategies such as defining risky sets are employed, limiting the impact of the safeguarding loop on the functionality of the inner loop. A closed-form solution for designing safeguarding control laws is derived to eliminate the necessity for solving any quadratic problems in real-time. Simulation case studies validate the effectiveness of the proposed controller in maintaining stability and safety.
Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior
Authors: Run-Xuan Tang
Date: 2024-06-06 00:00:00
Tasks: Time Series

The surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. By conducting behavioral analysis and mining usage patterns of new energy vehicles, particular patterns can be identified. For instance, overloading the battery, operating with low battery power, and driving at excessive speeds can all detrimentally affect the battery's performance. To assess the impact of such driving behavior on the urban ecology, an environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment. To extend the time series data of the vehicle's entire life cycle and the ecological environment within the model sequence data, the LSTM model with Bayesian optimizer is utilized for simulation. The analysis revealed the detrimental effects of poor driving behavior on the environment.