pwc-archive/papers-with-abstracts huggingface dataset:
TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing
Authors: Zemin Sun, Geng Sun, Qingqing Wu, Long He, Shuang Liang, Hongyang Pan, Dusit Niyato, Chau Yuen, Victor C. M. Leung
Date: 2024-03-23 00:00:00
Tasks: Edge-computing

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem. In the short timescale, we propose a price-incentive model for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long timescale, we propose a convex optimization-based method for UAV trajectory control. Besides, we theoretically prove the stability, optimality, and polynomial complexity of TJCCT. Extended simulation results demonstrate that the proposed TJCCT outperforms the comparative algorithms in terms of the system utility, average processing rate, average completion delay, and average completion ratio.
Semi-on-Demand Hybrid Transit Route Design with Shared Autonomous Mobility Services
Authors: Max T. M. Ng, Florian Dandl, Hani S. Mahmassani, Klaus Bogenberger
Date: 2024-03-23 00:00:00

This study examines the route design of a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas with Shared Autonomous Mobility Service (SAMS). The study develops analytically tractable cost expressions that capture access, waiting, and riding costs for users, and distance-based operating and time-based vehicle costs for operators. Two formulations are presented for strategic and tactical decisions in flexible route portion, fleet size, headway, and vehicle size optimization, enabling the determination of route types between fixed, hybrid, and flexible routes based on demand, cost, and operational parameters. The practical applications and benefits of semi-on-demand feeders are demonstrated with numerical examples and a large-scale case study in the Chicago metropolitan area. Findings reveal scenarios in which flexible route portions serving passengers located further away reduce total costs, particularly user costs. Lower operating costs in lower-demand areas favor more flexible routes, whereas higher demand densities favor more traditional line-based operations. On two studied lines, a current cost forecast favors smaller vehicles with flexible routes, but operating constraints and higher operating costs would favor bigger vehicles with hybrid routes. The study provides an analytical tool to design SAMS as directional services and transit feeders, and tractable continuous approximation formulations for future research in transit network design.
Small Noise Analysis of Non-Parametric Closed-Loop Identification
Authors: Mohamed Abdalmoaty, Roy S. Smith
Date: 2024-03-23 00:00:00
Tasks: Sensitivity

We revisit the problem of non-parametric closed-loop identification in frequency domain; we give a brief survey of the literature and provide a small noise analysis of the direct, indirect, and joint input-output methods when two independent experiments with identical excitation are used. The analysis is asymptotic in the noise variance (i.e., as the standard deviation of the innovations $\sigma \to 0$), for a finite data record of length $N$. We highlight the relationship between the estimators accuracy and the loop shape via asymptotic variance expressions given in terms of the sensitivity function. The results are illustrated using a numerical simulation example.
Causal Tracking of Distributions in Wasserstein Space: A Model Predictive Control Scheme
Authors: Max Emerick, Jared Jonas, Bassam Bamieh
Date: 2024-03-23 00:00:00
Tasks: Model Predictive Control

We consider a problem of optimal swarm tracking which can be formulated as a tracking problem for distributions in the Wasserstein space. Optimal solutions to this problem are non-causal and require knowing the time-trajectory of the reference distribution in advance. We propose a scheme where these non-causal solutions can be used together with a predictive model for the reference to achieve causal tracking of a priori-unknown references. We develop a model-predictive control scheme built around the simple case where the reference is constant-in-time. A computational algorithm based on particle methods and discrete optimal mass transport is presented, and numerical simulations are provided for various classes of reference signals. The results demonstrate that the proposed control algorithm achieves reasonable performance even when using simple predictive models.
Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks
Authors: Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton, Jerome Henry
Date: 2024-03-23 00:00:00
Tasks: Clustering, Density Estimation

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of ``clustering by fast search and find of density peaks'' (CFSFDP) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft k-means clustering algorithm. Then, we utilize the flexibility of the soft-k-means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multi-cluster heads is employed to balance the energy consumption within clusters. {Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission}, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.
Motion Planning for Identification of Linear Classifiers
Authors: Aneesh Raghavan, Karl Henrik Johansson
Date: 2024-03-23 00:00:00
Tasks: Motion Planning

A given region in 2-D Euclidean space is divided by a unknown linear classifier in to two sets each carrying a label. The objective of an agent with known dynamics traversing the region is to identify the true classifier while paying a control cost across its trajectory. We consider two scenarios: (i) the agent is able to measure the true label perfectly; (ii) the observed label is the true label multiplied by noise. We present the following: (i) the classifier identification problem formulated as a control problem; (ii) geometric interpretation of the control problem resulting in one step modified control problems; (iii) control algorithms that result in data sets which are used to identify the true classifier with accuracy; (iv) convergence of estimated classifier to the true classifier when the observed label is not corrupted by noise; (iv) numerical example demonstrating the utility of the control algorithms.
Safe and Stable Formation Control with Autonomous Multi-Agents Using Adaptive Control
Authors: Jose A. Solano-Castellanos, Peter A. Fisher, Anuradha Annaswamy
Date: 2024-03-23 00:00:00

This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Adaptive Control, Control Barrier Functions (CBFs), and connected graphs. The main elements employed in the integrative approach are an adaptive control design that ensures stability, a CBF-based safety filter that generates safe commands based on a reference model dynamics, and a reference model that ensures formation control with multi-agent systems when no uncertainties are present. The overall control design is shown to lead to a closed-loop adaptive system that is stable, avoids unsafe regions, and converges to a desired formation of the multi-agents. Numerical examples are provided to support the theoretical derivations.
Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control
Authors: Nhat Minh Nguyen, Stephen McIlvanna, Jack Close, Mien Van
Date: 2024-03-23 00:00:00
Tasks: Model Predictive Control

Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a novel control scheme for creating multi-agent distributed formation control with limited communication between individual agents. In addition, the formation of the multi-agent can be reconfigured in real-time and the network connectivity can be maintained. The proposed use case for this scheme includes creating underwater mobile communication networks that can adapt to environmental or network conditions to maintain the quality of communication links for long-range exploration, seabed monitoring, or underwater infrastructure inspection. This work introduces a novel Distributed Nonlinear Model Predictive Control (DNMPC) strategy, integrating Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) with a relaxed decay rate, specifically tailored for 6-DOF underwater robotics. The effectiveness of our proposed DNMPC scheme was demonstrated through rigorous MATLAB simulations for trajectory tracking and formation reconfiguration in a dynamic environment. Our findings, supported by tests conducted using Software In The Loop (SITL) simulation, confirm the approach's applicability in real-time scenarios.
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
Authors: Eduardo Figueiredo, Andrea Patane, Morteza Lahijanian, Luca Laurenti
Date: 2024-03-22 00:00:00

Uncertainty propagation in non-linear dynamical systems has become a key problem in various fields including control theory and machine learning. In this work we focus on discrete-time non-linear stochastic dynamical systems. We present a novel approach to approximate the distribution of the system over a given finite time horizon with a mixture of distributions. The key novelty of our approach is that it not only provides tractable approximations for the distribution of a non-linear stochastic system, but also comes with formal guarantees of correctness. In particular, we consider the total variation (TV) distance to quantify the distance between two distributions and derive an upper bound on the TV between the distribution of the original system and the approximating mixture distribution derived with our framework. We show that in various cases of interest, including in the case of Gaussian noise, the resulting bound can be efficiently computed in closed form. This allows us to quantify the correctness of the approximation and to optimize the parameters of the resulting mixture distribution to minimize such distance. The effectiveness of our approach is illustrated on several benchmarks from the control community.
Adaptive Dual Covariance Steering with Active Parameter Estimation
Authors: Jacob W. Knaup, Panagiotis Tsiotras
Date: 2024-03-22 00:00:00
Tasks: parameter estimation

This work examines the optimal covariance steering problem for systems subject to unknown parameters that enter multiplicatively with the state and control, in addition to additive disturbances. In contrast to existing works, the unknown parameters are modeled as random variables and are estimated online. This work proposes the utilization of recursive least squares estimation for efficient parameter identification. A dual control problem is formulated in which the effect of the planned control policy on the parameter estimates is modeled and optimized for. The parameter estimates are then used to modify the pre-computed control policy online in an adaptive control fashion. Finally, the proposed approach is demonstrated in a vehicle control example with closed-loop parameter identification.
Control Designs for Critical-Continegency Responsible Grid-Following Inverters and Seamless Transitions To and From Grid-Forming Modes
Authors: Jaesang Park, Alireza Askarian, Srinivasa Salapaka
Date: 2024-03-22 00:00:00

This article introduces two control frameworks: one for Grid-Following (GFL) inverters aiding Grid-Forming (GFM) inverters in voltage regulation during large contingency events and optimizing power transactions under normal conditions; and another for seamless transitions between grid-tied and grid-isolated setups, managing voltage transient characteristics. In microgrids, GFM inverters regulate voltage, while GFL inverters handle power transactions. The proposed GFL control detects abrupt load/generation changes, adjusting power transactions using local storage to support GFM inverters during contingencies. Additionally, a transition control ensures smooth GFL-GFM shifts, reducing power and voltage fluctuations. Simulation results validate improved voltage regulation during contingencies and enhanced power tracking during slow changes, alongside minimized transient overshoot.
Optimal Data-Driven Prediction and Predictive Control using Signal Matrix Models
Authors: Roy S. Smith, Mohamed Abdalmoaty, Mingzhou Yin
Date: 2024-03-22 00:00:00
Tasks: LEMMA

Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system. Willems' lemma provides a data-based prediction model allowing a control designer to bypass the step of identifying a state-space or transfer function model. This paper provides a more parsimonious formulation of Willems' lemma that separates the model into initial condition matching and predictive control design parts. This avoids the need for regularisers in the predictive control problem that are found in other data-driven predictive control methods. It also gives a closed form expression for the optimal (minimum variance) unbiased predictor of the future output trajectory and applies it for predictive control. Simulation comparisons illustrate very good control performance.
Event-Triggered State Estimation Through Confidence Level
Authors: Wei Liu
Date: 2024-03-22 00:00:00
Tasks: State Estimation

This paper considers the state estimation problem for discrete-time linear systems under event-triggered scheme. In order to improve performance, a novel event-triggered scheme based on confidence level is proposed using the chi-square distribution and mild regularity assumption. In terms of the novel event-triggered scheme, a minimum mean squared error (MMSE) state estimator is proposed using some results presented in this paper. Two algorithms for communication rate estimation of the proposed MMSE state estimator are developed where the first algorithm is based on information with one-step delay, and the second algorithm is based on information with two-step delay. The performance and effectiveness of the proposed MMSE state estimator and the two communication rate estimation algorithms are illustrated using a target tracking scenario.
Control contraction metrics on Lie groups
Authors: Dongjun Wu, Bowen Yi, Ian R. Manchester
Date: 2024-03-22 00:00:00

In this paper, we extend the control contraction metrics (CCM) approach, which was originally proposed for the universal tracking control of nonlinear systems, to those that evolves on Lie groups. Our idea is to view the manifold as a constrained set that is embedded in Euclidean space, and then propose the sufficient conditions for the existence of a CCM and the associated controller design. Notably, we demonstrate that the search for CCM on Lie groups can be reformulated as convex conditions. The results extend the applicability of the CCM approach and provide a framework for analyzing the behavior of control systems with Lie group structures.
Forecasting the load of Parcel Pickup Points using a Markov Jump Process
Authors: Thi-Thu-Tam Nguyen, Adnane Cabani, Iyadh Cabani, Koen De Turck, Michel Kieffer
Date: 2024-03-22 00:00:00

The growth of e-commerce has resulted in a surge in parcel deliveries, increasing transportation costs and pollution issues. Alternatives to home delivery have emerged, such as the delivery to so-called parcel pick-up points (PUPs), which eliminates delivery failure due to customers not being at home. Nevertheless, parcels reaching overloaded PUPs may need to be redirected to alternative PUPs, sometimes far from the chosen ones, which may generate customer dissatisfaction. Consequently, predicting the PUP load is critical for a PUP management company to infer the availability of PUPs for future orders and better balance parcel flows between PUPs. This paper proposes a new approach to forecasting the PUP load evolution using a Markov jump process that models the parcel life cycle. The latest known status of each parcel is considered to estimate its contribution to the future load of its target PUP. This approach can account for the variability of activity, the various parcel preparation delays by sellers, and the diversity of parcel carriers that may result in different delivery delays. Here, results are provided for predicting the load associated with parcels ordered from online retailers by customers (Business-to-Customer, B2C). The proposed approach is generic and can also be applied to other parcel flows to PUPs, such as second-hand products (Customer-to-Customer, C2C) sent via a PUP network.
Hybrid integrator-gain system based integral resonant controllers for negative imaginary systems
Authors: Kanghong Shi, Ian R. Petersen
Date: 2024-03-22 00:00:00

We introduce a hybrid control system called a hybrid integrator-gain system (HIGS) based integral resonant controller (IRC) to stabilize negative imaginary (NI) systems. A HIGS-based IRC has a similar structure to an IRC, with the integrator replaced by a HIGS. We show that a HIGS-based IRC is an NI system. Also, for a SISO NI system with a minimal realization, we show there exists a HIGS-based IRC such that their closed-loop interconnection is asymptotically stable. Also, we propose a proportional-integral-double-integral resonant controller and a HIGS-based proportional-integral-double-integral resonant controller, and we show that both of them can be applied to asymptotically stabilize an NI system. An example is provided to illustrate the proposed results.
Direct and Indirect Hydrogen Storage: Dynamics and Interactions in the Transition to a Renewable Energy Based System for Europe
Authors: Zhiyuan Xie, Gorm Bruun Andresen
Date: 2024-03-22 00:00:00

To move towards a low-carbon society by 2050, understanding the intricate dynamics of energy systems is critical. Our study examines these interactions through the lens of hydrogen storage, dividing it into 'direct' and 'indirect' hydrogen storage. Direct hydrogen storage involves electrolysis-produced hydrogen being stored before use, while indirect storage first transforms hydrogen into gas via the Sabatier process for later energy distribution. Firstly, we utilize the PyPSA-Eur-Sec-30-path model to capture the interactions within the energy system. The model is an hour-level, one node per country system that encompasses a range of energy transformation technologies, outlining a pathway for Europe to reduce carbon emissions by 95 percent by 2050 compared to 1990, with updates every 5 years. Subsequently, we employ both quantitative and qualitative approaches to thoroughly analyze these complex relationships. Our research indicates that during the European green transition, cross-country flow of electricity will play an important role in Europe's rapid decarbonization stage before the large-scale introduction of energy storage. Under the paper cost assumptions, fuel cells are not considered a viable option. This research further identifies the significant impact of natural resource variability on the local energy mix, highlighting indirect hydrogen storage as a common solution due to the better economic performance and actively fluctuation pattern. Specifically, indirect hydrogen storage will contribute at least 60 percent of hydrogen storage benefits, reaching 100 percent in Italy. Moreover, its fluctuation pattern will change with the local energy structure, which is a distinct difference with the unchanged pattern of direct hydrogen storage and battery storage.
Implementation of Firm-Dispatchable Generation in South Africa
Authors: Stephen R. Clark, Craig McGregor
Date: 2024-03-22 00:00:00

South Africa is currently facing a critical situation in its power generation landscape, which is plagued by frequent power outages and the need to move from fossil fuels to renewable energy sources. This period emphasizes the importance of having firm-dispatchable power to balance out the intermittent nature of wind and solar energy sources. The paper proposes to repurpose old coal-fired power plants to generate firm-dispatchable energy in line with the principles of a Just Transition. Eskom's coal plants are approaching the end of their economic life, and their declining energy availability factor is becoming a challenge in meeting the country's energy needs. The study suggests that a comprehensive strategy that integrates wind, solar, and firm-dispatchable power can be cost-effective and reliable compared to the traditional coal-based approach or the nuclear alternative. The study emphasizes the necessity of a 25-year plan that would invest in flexible and modular dispatchable generation. It also highlights the strategic location of this generating capacity, including repurposing decommissioned coal plant sites. The proposed model integrates private investment, adheres to established best practices, and emphasizes adaptability to changing demand dynamics. The study provides a roadmap for enabling firm-dispatchable capacity for South Africa's energy transition, emphasizing economic prudence, environmental sustainability, and alignment with the principles of the Just Transition program.
On the Solution Uniqueness of Data-Driven Modeling of Flexible Loads (with Supplementary Material)
Authors: Shuai Lu, Jiayi Ding, Mingji Chen, Wei Gu, Junpeng Zhu, Yijun Xu, ZhaoYang Dong, Zezheng Sun
Date: 2024-03-22 00:00:00

This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural and practical identifiability of the PFL model, deriving the dataset condition that guarantees the solution uniqueness. Besides, we point out the practical implications of the results. Numerical tests validate this work.