pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Newton Raphson Controller Based on Koopman Operator Theory
Authors: Mi Zhou
Date: 2023-09-29 00:00:00

Newton-Raphson controller is a powerful prediction-based variable gain integral controller. Basically, the classical model-based Newton-Raphson controller requires two elements: the prediction of the system output and the derivative of the predicted output with respect to the control input. In real applications, the model may not be known and it is infeasible to predict the system sometime ahead and calculate the derivative by finite difference method as done in simulation. To solve these problems, in this work, we utilize the Koopman operator framework to reconstruct a linear model of the original nonlinear dynamical system and then utilize the output of the new linear system as the predictor of the Newton-Raphson controller. This method is only based on collected data within some time instant thus more practical. Three examples related to highly nonlinear systems are provided to verify the effectiveness of our proposed method.
Data-Driven Min-Max MPC for Linear Systems
Authors: Yifan Xie, Julian Berberich, Frank Allgower
Date: 2023-09-29 00:00:00
Tasks: Model Predictive Control

Designing data-driven controllers in the presence of noise is an important research problem, in particular when guarantees on stability, robustness, and constraint satisfaction are desired. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme to design state-feedback controllers from noisy data for unknown linear time-invariant (LTI) system. The considered min-max problem minimizes the worst-case cost over the set of system matrices consistent with the data. We show that the resulting optimization problem can be reformulated as a semidefinite program (SDP). By solving the SDP, we obtain a state-feedback control law that stabilizes the closed-loop system and guarantees input and state constraint satisfaction. A numerical example demonstrates the validity of our theoretical results.
Distributed Resilient Control of DC Microgrids Under Generally Unbounded FDI Attacks
Authors: Yichao Wang, Mohamadamin Rajabinezhad, Omar A. Beg, Shan Zuo
Date: 2023-09-29 00:00:00

Due to the nature of distributed secondary control paradigm, DC microgrids are prone to malicious cyber-physical attacks, which could be unbounded to maximize their damage. Existing resilient secondary control methods addressing unbounded attacks require that the first time derivatives of cyber-physical attack signals be bounded. The secondary defense strategy presented in this letter relax such a strict constraint by addressing more generally unbounded attack signals and hence, enhance the resilience of DC microgrids in adversarial environments. Rigorous proofs, based on Lyapunov techniques, show that the proposed method guarantees the uniformly ultimately bounded convergence for both voltage regulation and proportional load sharing under generally unbounded attacks. Comparative case studies further validate the enhanced resilience of the proposed attack-resilient control strategy.
A Framework and a python-package for Real-time NMPC parameters settings
Authors: Mazen Alamir
Date: 2023-09-29 00:00:00

This paper presents a framework that enables a systematic and rational choice of NMPC design components such as control updating period, down-sampling period for prediction, control parameterization, prediction horizon's length, the maximum number of iterations as well as penalties on the terminal cost and the soft constraints. The rationale that underlines the design choices is based on real-time implementability, convergence and constraints satisfaction for a given computational device and a specific optimization algorithm. Moreover, a freely available associated Python-based implementation is also described with a fully developed illustrative example implementing a nonlinear MPC controller for a Planar Vertical Take-Off and Landing (PVTOL) aircraft under control saturation and state constraints.
Convex Optimization of Bearing Formation Control of Rigid bodies on Lie Group
Authors: Sara Mansourinasab, Mahdi Sojoodi, Seyed Reza Moghadasi
Date: 2023-09-29 00:00:00

In this paper, the problem of reaching formation for a network of rigid agents over a special orthogonal group is investigated by considering bearing-only constraints as the desired formation. Each agent is able to gather the measurements with respect to other agents in its own body frame. So, the agents are coordinated-free concerning a global reference frame. Attracting to the desired formation is founded on solving an optimization problem for minimizing the difference between the instantaneous bearing between agents and their desired bearing. In order to have a unique global solution, the convex optimization method is implemented. Since the rotation matrices are not convex, the method of convex relaxation of rotation matrices space is used to embed the rotation matrices on the convex hull of the Lie group. Then the control law is designed to achieve the desired bearing with minimum energy consumption. Finally, a simulation example is provided to verify the results.
Latent Dynamic Networked System Identification with High-Dimensional Networked Data
Authors: Jiaxin Yu, Yanfang Mo, S. Joe Qin
Date: 2023-09-29 00:00:00
Tasks: Dimensionality Reduction

Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems. These systems produce high-dimensional data that reflect the complex interactions among the network nodes with rich sensor measurements. In this paper, we propose a novel algorithm for latent dynamic networked system identification that leverages the network structure and performs dimension reduction for each node via dynamic latent variables (DLVs). The algorithm assumes that the DLVs of each node have an auto-regressive model with exogenous input and interactions from other nodes. The DLVs of each node are extracted to capture the most predictable latent variables in the high dimensional data, while the residual factors are not predictable. The advantage of the proposed framework is demonstrated on an industrial process network for system identification and dynamic data analytics.
UXsim: An open source macroscopic and mesoscopic traffic simulator in Python -- a technical overview
Authors: Toru Seo
Date: 2023-09-29 00:00:00

This note describes a technical overview of UXsim, an open source macro/mesoscopic traffic simulator in pure Python programming language. UXsim is based on Kinematic Wave model (more specifically, mesoscopic version of Newell's simplified car-following model) and dynamic user optimum-like route choice principle, which are well established methodology in the transportation research field. It can compute dynamical network traffic flow and have basic visualization and analysis capability. Furthermore, users can implement their own models and control methods into the simulator by using Python, thanks to the flexibility of the language. The simulator and its codes are freely available at https://github.com/toruseo/UXsim under the MIT license.
Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors
Authors: Max van Meer, Rodrigo A. González, Gert Witvoet, Tom Oomen
Date: 2023-09-29 00:00:00
Tasks: Position

Switched Reluctance Motors (SRMs) enable power-efficient actuation with mechanically simple designs. This paper aims to identify the nonlinear relationship between torque, rotor angle, and currents, to design commutation functions that minimize torque ripple in SRMs. This is achieved by conducting specific closed-loop experiments using purposely imperfect commutation functions and identifying the nonlinear dynamics via Bayesian estimation. A simulation example shows that the presented method is robust to position-dependent disturbances, and experiments suggest that the identification method enables the design of commutation functions that significantly increase performance. The developed approach enables accurate identification of the torque-current-angle relationship in SRMs, without the need for torque sensors, an accurate linear model, or an accurate model of position-dependent disturbances, making it easy to implement in production.
Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement
Authors: Doyoung Kim, Seongah Jeong, Jinkyu Kang
Date: 2023-09-29 00:00:00
Tasks: Disaster Response, Edge-computing

With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response. This paper investigates a secure offloading system, where the UAV-mounted IRS assists the offloading procedures between ground users and an access point (AP) acting as an edge cloud. In this system, the users except the intended recipients in the offloading process are considered as potential eavesdroppers. The system aims to achieve the minimum total energy consumption of battery-limited ground user devices under constraints for secure offloading accomplishment and operability of UAV-mounted IRS, which is done by optimizing the transmit power of ground user devices, the trajectory and phase shift matrix of UAV-mounted IRS, and the offloading ratio between local execution and edge computing based on the successive convex approximation (SCA) algorithms. Numerical results show that the proposed algorithm can provide the considerable energy savings compared with local execution and partial optimizations.
Scalable Neural Dynamic Equivalence for Power Systems
Authors: Qing Shen, Yifan Zhou, Huanfeng Zhao, Peng Zhang, Qiang Zhang, Slava Maslenniko, Xiaochuan Luo
Date: 2023-09-29 00:00:00
Tasks: Physics-informed machine learning

Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components. However, acquiring thorough power system models can be impractical in real operation due to inaccessible system parameters and privacy of consumers, which necessitate data-driven dynamic equivalencing of unknown subsystems. Learning reliable dynamic equivalent models for the external systems from SCADA and PMU data, however, is a long-standing intractable problem in power system analysis due to complicated nonlinearity and unforeseeable dynamic modes of power systems. This paper advances a practical application of neural dynamic equivalence (NeuDyE) called Driving Port NeuDyE (DP-NeuDyE), which exploits physics-informed machine learning and neural-ordinary-differential-equations (ODE-NET) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The new contributions are threefold: A NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems, saving the effort and expense of acquiring inaccessible system; An introduction of a Physics-Informed NeuDyE learning (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE; and A DP-NeuDyE to reduce the number of inputs required for the training. We conduct extensive case studies on the NPCC system to validate the generalizability and accuracy of both PI-NeuDyE and DP-NeuDyE, which span a multitude of scenarios, differing in the time required for fault clearance, the specific fault locations, and the limitations of data. Test results have demonstrated the scalability and practicality of NeuDyE, showing its potential to be used in ISO and utility control centers for online transient stability analysis and for planning purposes.
Disturbance Observer-based Robust Integral Control Barrier Functions for Nonlinear Systems with High Relative Degree
Authors: Vrushabh Zinage, Rohan Chandra, Efstathios Bakolas
Date: 2023-09-29 00:00:00

In this paper, we consider the problem of safe control synthesis of general controlled nonlinear systems in the presence of bounded additive disturbances. Towards this aim, we first construct a governing augmented state space model consisting of the equations of motion of the original system, the integral control law and the nonlinear disturbance observer. Next, we propose the concept of Disturbance Observer based Integral Control Barrier Functions (DO-ICBFs) which we utilize to synthesize safe control inputs. The characterization of the safe controller is obtained after modifying the governing integral control law with an additive auxiliary control input which is computed via the solution of a quadratic problem. In contrast to prior methods in the relevant literature which can be unnecessarily cautious due to their reliance on the worst case disturbance estimates, our DO-ICBF based controller uses the available control effort frugally by leveraging the disturbance estimates computed by the disturbance observer. By construction, the proposed DO-ICBF based controller can ensure state and input constraint satisfaction at all times. Further, we propose Higher Order DO-ICBFs that extend our proposed method to nonlinear systems with higher relative degree with respect to the auxiliary control input. Finally, numerical simulations are provided to validate our proposed approach.
Physics-Aware Neural Dynamic Equivalence of Power Systems
Authors: Qing Shen, Yifan Zhou, Qiang Zhang, Slava Maslennikov, Xiaochuan Luo, Peng Zhang
Date: 2023-09-29 00:00:00

This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models. Extensive case studies in the NPCC system validate the efficacy of NeuDyE, and, in particular, its capability under various contingencies.
Analytically Computation of Sensitivity Coefficients in Hybrid AC/DC Micro-Grid
Authors: Willem Lambrichts, Mario Paolone
Date: 2023-09-28 00:00:00
Tasks: Sensitivity

In this paper, we present a closed-form model for the analytical computation of the power flow sensitivity coefficients (SCs) for hybrid AC/DC networks. The SCs are defined as the partial derivates of the nodal voltages with respect to the active and reactive power injections. The proposed method is inspired by an existing SC computation process proposed for AC networks and here extended to include both the DC grid and the relevant AC/DC Interfacing Converters (ICs). The ICs can operate under different control modes i.e. voltage or power. Additionally, the model is able to compute the SCs for three-phase networks subjected to unbalanced loading conditions. The proposed method is numerically validated by means of a comparison with a detailed time-domain simulation model solved within the EMTP-RV simulation environment. Furthermore, we provide a formal proof regarding the uniqueness of the proposed SCs computational model for hybrid AC/DC networks.
General and Unified Model of the Power Flow Problem in Multiterminal AC/DC Networks
Authors: Willem Lambrichts, Mario Paolone
Date: 2023-09-28 00:00:00

This paper proposes a generic and unified model of the power flow (PF) problem for multiterminal hybrid AC/DC networks. The proposed model is an extension of the standard AC-PF. The DC network is treated as an AC one and, in addition to the Slack, PV and PQ nodes, four new node types are introduced to model the DC buses and the buses connecting the AC/DC interfacing converters (IC). The unified model is solved using the Newton-Raphson method. The extended PF equations can be used in the presence of multiple ICs operating under different control modes. Compared to other recent works, the proposed method allows multiple ICs to regulate the DC voltage simultaneously. This corresponds to more realistic operational conditions that ensure redundancy and allow for more flexible control of the hybrid grid. The proposed model can be used for networks under unbalanced conditions and allows for an intentionally negative sequence power injection. In addition to the operational advantages of this method, it is shown that the computational performance of the proposed method is one order of magnitude better than that of other methods presented in the existing recent literature while having the same accuracy.
Energy Optimal Control of a Harmonic Oscillator with a State Inequality Constraint
Authors: Mi Zhou, Erik I Verriest, Chaouki Abdallah
Date: 2023-09-28 00:00:00

In this article, the optimal control problem for a harmonic oscillator with an inequality constraint is considered. The applied energy of the oscillator during a fixed final time period is used as the performance criterion. The analytical solution with both small and large terminal time is found for a special case when the undriven oscillator system is initially at rest. For other initial states of the Harmonic oscillator, the optimal solution is found to have three modes: wait-move, move-wait, and move-wait-move given a longer terminal time.
Agent Coordination via Contextual Regression (AgentCONCUR) for Data Center Flexibility
Authors: Vladimir Dvorkin
Date: 2023-09-28 00:00:00
Tasks: regression

A network of spatially distributed data centers can provide operational flexibility to power systems by shifting computing tasks among electrically remote locations. However, harnessing this flexibility in real-time through the standard optimization techniques is challenged by the need for sensitive operational datasets and substantial computational resources. To alleviate the data and computational requirements, this paper introduces a coordination mechanism based on contextual regression. This mechanism, abbreviated as AgentCONCUR, associates cost-optimal task shifts with public and trusted contextual data (e.g., real-time prices) and uses regression on this data as a coordination policy. Notably, regression-based coordination does not learn the optimal coordination actions from a labeled dataset. Instead, it exploits the optimization structure of the coordination problem to ensure feasible and cost-effective actions. A NYISO-based study reveals large coordination gains and the optimal features for the successful regression-based coordination.
Income Mobility and Mixing in North Macedonia
Authors: Viktor Stojkoski, Sonja Mitikj, Marija Trpkova-Nestorovska, Dragan Tevdovski
Date: 2023-09-29 00:00:00

This study presents the inaugural analysis of income mobility in North Macedonia from 1995-2021 using the Mixing Time and Mean First Passage Time (MFPT) metrics. We document larger mobility (in terms of Mixing Time) during the '90s, with and decreasing trend (in terms of mobility) until 1999. After this year the Mixing time has been consistent with a value of around 4 years. Using the MFPT, we highlight the evolving challenges individuals face when aspiring to higher income tiers. Namely, we show that there was a noticeable upward trend in MFPT from 1995 to 2006, a subsequent decline until 2017, and then an ascent again, peaking in 2021. These findings provide a foundational perspective on the income mobility in North Macedonia.
Machine Learning for Practical Quantum Error Mitigation
Authors: Haoran Liao, Derek S. Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, Zlatko K. Minev
Date: 2023-09-29 00:00:00

Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle. The key to overcoming errors on near-term devices has emerged through the field of quantum error mitigation, enabling improved accuracy at the cost of additional run time. Here, through experiments on state-of-the-art quantum computers using up to 100 qubits, we demonstrate that without sacrificing accuracy machine learning for quantum error mitigation (ML-QEM) drastically reduces the cost of mitigation. We benchmark ML-QEM using a variety of machine learning models -- linear regression, random forests, multi-layer perceptrons, and graph neural networks -- on diverse classes of quantum circuits, over increasingly complex device-noise profiles, under interpolation and extrapolation, and in both numerics and experiments. These tests employ the popular digital zero-noise extrapolation method as an added reference. Finally, we propose a path toward scalable mitigation by using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency. Our results show that classical machine learning can extend the reach and practicality of quantum error mitigation by reducing its overheads and highlight its broader potential for practical quantum computations.
The long-term impact of (un)conditional cash transfers on labour market outcomes in Ecuador
Authors: Juan Ponce, José-Ignacio Antón, Mercedes Onofa, Roberto Castillo
Date: 2023-09-29 00:00:00

Despite the popularity of conditional cash transfers in low- and middle-income countries, evidence on their long-term effects remains scarce. This study assesses the impact of Ecuador's Human Development Grant on the formal sector labour market outcomes of children in eligible households. This grant, one of the first of its kind, is characterized by weak enforcement of its eligibility criteria. Using a regression discontinuity design, we find that the programme had no overall impact on formal employment rates and labour income earned in the formal sector around 15 years after exposure, and thus not affecting the intergenerational transmission of poverty. We only document a positive effect on the non-mestizo population (mainly consisting of indigenous and Afro-Ecuadorians).
Normalized insured losses caused by windstorms in Quebec and Ontario, Canada, in the period 2008-2021
Authors: Mohammad Hadavi, Lutong Sun, Djordje Romanic
Date: 2023-08-10 00:00:00

Severe windstorms pose threats to people, human-made structures, and the environment. An investigation of insured losses caused by windstorms is a multipurpose study that serves to advance the resilience and sustainability of modern communities. The present study proposes a systematic analysis of insured losses imposed by different types of windstorms in two Canadian provinces, Ontario (ON) and Quebec (QC), during the period 2008-2021. Actual wind damage data from the Canadian insurance market were considered in this study. Our calculations show that ON and QC received half of all wind catastrophes across Canada, and nearly three-quarters of all types of catastrophes in ON and QC were wind-related ones. The total windstorm loss of over CA$5.2 billion was not evenly distributed between QC and ON, but rather had a QC:ON ratio of 1:3.1. We attributed this discrepancy in the inflicted damage between two provinces to the predominantly eastward and northeastward storm trajectories and the higher density of wealth and population in ON. Convective storms were the most devastating wind type comprising nearly 65% and 67% of the total number of events and associated damage, respectively. Finally, tornadoes had the highest average loss per event in two provinces combined. Future prospects and the implication of this research are also discussed.