pwc-archive/papers-with-abstracts huggingface dataset:
Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal Prediction
Authors: Xinyi Yu, Yiqi Zhao, Xiang Yin, Lars Lindemann
Date: 2023-12-07 00:00:00
Tasks: Conformal Prediction, Prediction, valid

The control of dynamical systems under temporal logic specifications among uncontrollable dynamic agents is challenging due to the agents' a-priori unknown behavior. Existing works have considered the problem where either all agents are controllable, the agent models are deterministic and known, or no safety guarantees are provided. We propose a predictive control synthesis framework that guarantees, with high probability, the satisfaction of signal temporal logic (STL) tasks that are defined over a controllable system in the presence of uncontrollable stochastic agents. We use trajectory predictors and conformal prediction to construct probabilistic prediction regions for each uncontrollable agent that are valid over multiple future time steps. Specifically, we construct a normalized prediction region over all agents and time steps to reduce conservatism and increase data efficiency. We then formulate a worst-case bilevel mixed integer program (MIP) that accounts for all agent realizations within the prediction region to obtain an open-loop controller that provably guarantee task satisfaction with high probability. To efficiently solve this bilevel MIP, we propose an equivalent MIP program based on KKT conditions of the original bilevel formulation. Building upon this, we design a closed-loop controller, where both recursive feasibility and task satisfaction can be guaranteed with high probability. We illustrate our control synthesis framework on two case studies.
Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models
Authors: Marcel Kettelgerdes, Tjorven Hillmann, Thomas Hirmer, Hüseyin Erdogan, Bernhard Wunderle, Gordon Elger
Date: 2023-12-07 00:00:00
Tasks: Sensor Modeling

In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.
Distributed Formation Maneuver Control Using Complex Laplacian
Authors: Xu Fang, Lihua Xie
Date: 2023-12-07 00:00:00

This paper studies the problem of distributed formation maneuver control of multi-agent systems via complex Laplacian. We will show how to change the translation, scaling, rotation, and also the shape of formation continuously by only tuning the positions of the leaders in both 2-D and 3-D spaces, where the rotation of formation in 3-D space is realized by changing the yaw angle, pitch angle, and roll angle of formation sequentially. Compared with real-Laplacian-based methods, the first advantage of the proposed complex-Laplacian-based approach is that each follower requires fewer neighbors and lesser communication. The second advantage is that non-convex and non-generic nominal configurations are allowed and the uniqueness of the complex-constraint-based target formation can be guaranteed by the non-collocated nominal agents. The third advantage is that more formation shapes can be realized by only tuning the positions of the leaders. Two simulation examples are given to illustrate the theoretical results.
Safety-Enhanced Self-Learning for Optimal Power Converter Control
Authors: Yihao Wan, Qianwen Xu, Tomislav Dragičević
Date: 2023-12-07 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL), Self-Learning

Data-driven learning-based control methods such as reinforcement learning (RL) have become increasingly popular with recent proliferation of the machine learning paradigm. These methods address the parameter sensitiveness and unmodeled dynamics in model-based controllers, such as finite control-set model predictive control. RL agents are typically utilized in simulation environments, where they are allowed to explore multiple "unsafe" actions during the learning process. However, this type of learning is not applicable to online self-learning of controllers in physical power converters, because unsafe actions would damage them. To address this, this letter proposes a safe online RL-based control framework to autonomously find the optimal switching strategy for the power converters, while ensuring system safety during the entire self-learning process. The proposed safe online RL-based control is validated in a practical testbed on a two-level voltage source converter system, and the results confirm the effectiveness of the proposed method.
Generalized Damping Torque Analysis of Ultra-Low Frequency Oscillation in the Jerk Space
Authors: Yichen Zhou, Yang Yang, Tao Zhou, Yonggang Li
Date: 2023-12-07 00:00:00

Ultra low frequency oscillation (ULFO) is significantly threatening the power system stability. Its unstable mechanism is mostly studied via generalized damping torque analysis method (GDTA). However, the analysis still adopts the framework established for low frequency oscillation. Hence, this letter proposes a GDTA approach in the jerk space for ULFO. A multi-information variable is constructed to transform the system into a new state space, where it is found that the jerk dynamics of the turbine-generator cascaded system is a second-order differential equation. Benefiting from this characteristic, we propose a new form for GDTA using jerk dynamics, which is established in the frequency-frequency acceleration phase space. Then, analytical expressions of all damping torque are provided. Finally, test results verified the proposed theoretical results. The negative damping mechanism is revealed, and parameter adjustment measures are concluded.
Improving the Load Flexibility of Stratified Electric Water Heaters: Design and Experimental Validation of MPC Strategies
Authors: Elizabeth Buechler, Aaron Goldin, Ram Rajagopal
Date: 2023-12-07 00:00:00
Tasks: energy management, Model Predictive Control

Residential electric water heaters have significant load shifting capabilities due to their thermal heat capacity and large energy consumption. Model predictive control (MPC) has been shown to be an effective control strategy to enable water heater load shifting in home energy management systems. In this work, we analyze how modeling tank stratification in an MPC formulation impacts control performance for stratified electric water heaters under time-of-use (TOU) rates. Specifically, we propose an MPC formulation based on a three-node thermal model that captures tank stratification, and compare it to a one-node formulation that does not capture stratification and a standard thermostatic controller. These strategies are compared through both real-time laboratory testing and simulation-based evaluation for different water use patterns. Laboratory experiments show cost reductions of 12.3-23.2% for the one-node MPC and 31.2-42.5% for the three-node MPC relative to the thermostatic controller. The performance of the one-node MPC is limited by significant plant-model mismatch, while the three-node formulation better approximates real-world dynamics and results in much more effective cost reduction and load shifting. A simple analysis of how each strategy performs under water use forecast errors is also provided.
Rate-splitting Multiple Access for Hierarchical HAP-LAP Networks under Limited Fronthaul
Authors: Jeongbin Kim, Seongah Jeong, Seonghoon Yoo, Woong Son, Joonhyuk Kang
Date: 2023-12-07 00:00:00
Tasks: Quantization

In this correspondence, we propose hierarchical high-altitude platform (HAP)-low-altitude platform (LAP) networks with the aim of maximizing the sum-rate of ground user equipments (UEs). The multiple aerial radio units (RUs) mounted on HAPs and LAPs are managed by the central unit (CU) via constrained fronthaul links. The limitation of fronthaul capacity can be addressed through quantization, employing the cloud radio access network (C-RAN) architecture. For spectral efficiency, we adopt the rate-splitting multiple access (RSMA), leveraging the advantages of both space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA). To achieve this, we jointly optimize rate splitting, transmit power allocation, quantization noise variance, and UAV placement using an alternating optimization (AO) approach coupled with successive convex approximation (SCA) and the weighted minimum mean square error (WMMSE) method. Numerical results validate the superior performance of the proposed method compared to benchmark schemes, including partial optimizations or those without the assistance of LAPs.
Modeling and Design Optimization of a Linear Motor with Halbach Array for Semiconductor Manufacturing Technology
Authors: Sajjad Mohammadi, Jeffrey H. Lang, James L. Kirtley, David L. Trumper
Date: 2023-12-07 00:00:00

This paper presents analytical modeling and design of a high-acceleration, low-vibration slotless double-sided linear motor with an arbitrary Halbach array for lithography machines used in semiconductor manufacturing technology. Amperian current and magnetic charge models of permanent magnets are integrated into a hybrid approach to develop comprehensive analytical modeling. Unlike conventional methods that treat magnets as sources for Poisson's equations, the solution is reduced to Laplace's equations, with magnets being represented as boundary conditions. The magnetic fields and potentials within distinct regions, along with machine quantities such as shear stress, force-angle characteristics, torque profile, attraction force, misalignment force, and back-EMF, are derived, comprehensively analyzed, and compared to FEM results for accuracy validation. In addition, two models based on Poisson's equations in terms of scalar and vector potentials are derived, compared, and analyzed. Finally, design optimization and sensitivity analysis of a linear stage for lithography applications are discussed.
An Actuator with Magnetic Restoration, Part II: Drive Circuit and Control Loops
Authors: Sajjad Mohammadi, William R. Benner, James L. Kirtley, Jeffrey H. Lang
Date: 2023-12-07 00:00:00
Tasks: Friction

In part II, an op-amp-based drive is proposed and designed. Subsequently, a very accurate model for the drive circuit and the current loop is developed as a simulation platform, while its simplified version is derived, tailored for efficient design purposes. Through a comprehensive evaluation, the accuracy and efficacy of both the actuator and drive circuit modeling is scrutinized, showcasing their superiorities over existing approaches. The importance of eddy current modeling is underscored. Also, the effectiveness of the designed current loop and its practical trade-offs are engineered and discussed. Then, three DSP-based position control techniques are implemented: pole placement with voltage drive, pole placement with current drive, and nonlinear control with feed linearization. Both full-order and reduced-order observers are leveraged to estimate the unmeasured states. The performance of control designs across various applications are evaluated through indices such as rise time, overshoot, steady-state error, and large-signal tracking in the step response as well as bandwidth, robustness, phase margin, sensitivity, disturbance rejection, and noise rejection in the frequency domain. The distinctive features of implemented control strategy are compared, offering a nuanced discussion of their respective advantages and drawbacks, shedding light on their potential applications.
An Actuator with Magnetic Restoration, Part I: Electromechanical Model and Identification
Authors: Sajjad Mohammadi, William R. Benner, James L. Kirtley, Jeffrey H. Lang
Date: 2023-12-07 00:00:00
Tasks: Friction

Electromechanical models are crucial in the design and control of motors and actuators. Modeling, identification, drive, and current control loop of a limited-rotation actuator with magnetic restoration is presented. New nonlinear and linearized electromechanical models are developed for the design of the drive as well as small and large signal controls of the actuator. To attain a higher accuracy and an efficient design, and the eddy-currents in the laminations and magnet are modeled. This involves analytically solving 1-D and 2-D diffusion equations, leading to the derivation of a lumped-element circuit for system-level analyses, such as control system design. Additionally, the study analyzes and incorporates the impact of pre-sliding friction. The actuator is prototyped, and the paper delves into the identification of the model, presenting a procedure for parameter extraction. A close agreement is observed between the results obtained from the model, finite element analysis, and experimental results. The superiority of the proposed model over previous approaches is highlighted. Part II of the paper is dedicated to the drive circuit, the current control, as well as linear and nonlinear position control system designs.
Resilience-Assuring Hydrogen-Powered Microgrids
Authors: Chaofan Lin, Peng Zhang, Xiaonan Lu
Date: 2023-12-07 00:00:00

Green hydrogen has shown great potential to power microgrids as a primary source, yet the operation methodology under extreme events is still an open area. To fill this gap, this letter establishes an operational optimization strategy towards resilient hydrogen-powered microgrids, where the frequency and voltage regulation characteristics of hydrogen sources under advanced controls are accurately represented by piecewise linear constraints. The results show that the new operation approach can output a safety-assured operation plan with rational power change distribution and reduced frequency and voltage variation.
Programmable Turbine Failsafe System for Pico-Hydroelectric Power in the Nepal Himalayas
Authors: Hsi-Jen James Yeh, Rick Sturdivant, Mark Stambaugh, Alex Zahnd
Date: 2023-12-07 00:00:00
Tasks: PICO

In this paper, we describe a novel turbine failsafe system designed for a pico-hydroelectric power generation. We designed and built a pico-hydroelectric system in a remote rural Himalayan village. We developed a Prioritized Load Control System (PLCS) that diverts excess electrical power to useful purposes such as heating up bath water and the air in the community center when the electrical demand from the village is low; instead of dumping the excess electricity back into the river in the form of heat. A critical part of the PLCS system is the failsafe mechanism where the turbines and electrical components on the grid are protected in case any part of the PLCS system stops functioning, possibly intermittently. Our novel failsafe system contains the following enhancements compared to previous systems: (1) adjustable threshold voltage, (2) controllable fractional power diversion with adjustable parameters, (3) automatic reset with adjustable parameters. In addition, the failsafe subsystem uses the widely available Arduino platform and programming environment, and JSON for human readable and writable communications, demonstrating their suitability for critical applications.
A fast numerical algorithm for finding all real solutions to a system of N nonlinear equations in a finite domain
Authors: Fernando Chueca-Diez, Alfonso M. Ganan-Calvo
Date: 2023-12-06 00:00:00
Tasks: All, Edge Detection

A highly recurrent traditional bottleneck in applied mathematics, for which the most popular codes (Mathematica and Matlab) do not offer a solution, is to find all the real solutions of a system of N nonlinear equations in a certain finite domain of the N-dimensional space of variables. We present an algorithm of minimum length and computational weight to solve this problem, resembling a graphical tool of edge detection in an image extended to N dimensions. Once the hypersurfaces (edges) defined by each nonlinear equation have been identified in a single, simultaneous step, the coincidence of the hypersurfaces in the vicinity of all the hyperpoints that constitute the solutions makes the final Newton-Raphson step rapidly convergent to all the solutions with the desired degree of accuracy. As long as N remains smaller than about five, which is often the case for physical systems that depend on fewer than five parameters, this approach demonstrates excellent effectiveness.
Uncertainty-Informed Renewable Energy Scheduling: A Scalable Bilevel Framework
Authors: Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou, Alberto J. Lamadrid L., Audun Botterud
Date: 2023-12-06 00:00:00
Tasks: Bilevel Optimization, Scheduling

This work proposes an uncertainty-informed bid adjustment framework for integrating variable renewable energy sources (VRES) into electricity markets. This framework adopts a bilevel model to compute the optimal VRES day-ahead bids. It aims to minimize the expected system cost across day-ahead and real-time stages and approximate the cost efficiency of the stochastic market design. However, solving the bilevel optimization problem is computationally challenging for large-scale systems. To overcome this challenge, we introduce a novel technique based on strong duality and McCormick envelopes, which relaxes the problem to a linear program, enabling large-scale applications. The proposed bilevel framework is applied to the 1576-bus NYISO system and benchmarked against a myopic strategy, where the VRES bid is the mean value of the probabilistic power forecast. Results demonstrate that, under high VRES penetration levels (e.g., 40%), our framework can significantly reduce system costs and market-price volatility, by optimizing VRES quantities efficiently in the day-ahead market. Furthermore, we find that when transmission capacity increases, the proposed bilevel model will still reduce the system cost, whereas the myopic strategy may incur a much higher cost due to over-scheduling of VRES in the day-ahead market and the lack of flexible conventional generators in real time.
Learning the Dynamics of Future Marine Microgrids Using Temporal Convolutional Neural Network
Authors: Xiaoyu Ge, Ali Hosseinipour, Saskia Putri, Faegheh Moazeni, Javad Khazaei
Date: 2023-12-06 00:00:00

Medium-voltage direct-current (MVDC) ship-board microgrids (SMGs) are the state-of-the-art architecture for onboard power distribution in navy. These systems are considered to be highly dynamic due to high penetration of power electronic converters and volatile load patterns such as pulsed-power load (PPL) and propulsion motors demand variation. Obtaining the dynamic model of an MVDC SMG is a challenging task due to the confidentiality of system components models and uncertainty in the dynamic models through time. In this paper, a dynamic identification framework based on a temporal convolutional neural network (TCN) is developed to learn the system dynamics from measurement data. Different kinds of testing scenarios are implemented, and the testing results show that this approach achieves an exceptional performance and high generalization ability, thus holding substantial promise for development of advanced data-driven control strategies and stability prediction of the system.
Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations
Authors: Siguo Bi, Kai Li, Shuyan Hu, Wei Ni, Cong Wang, Xin Wang
Date: 2023-12-06 00:00:00
Tasks: Position

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging. Traditional methods relying solely on individually reported positions and pairwise distance measurements are ineffective in identifying the misbehavior of malicious UAVs. This paper presents a novel systematic structure designed to detect and mitigate spoofing attacks in UAV swarms. We formulate the problem of detecting malicious UAVs as a localization feasibility problem, leveraging the reported positions and distance measurements. To address this problem, we develop a semidefinite relaxation (SDR) approach, which reformulates the non-convex localization problem into a convex and tractable semidefinite program (SDP). Additionally, we propose two innovative algorithms that leverage the proximity of neighboring UAVs to identify malicious UAVs effectively. Simulations demonstrate the superior performance of our proposed approaches compared to existing benchmarks. Our methods exhibit robustness across various swarm networks, showcasing their effectiveness in detecting and mitigating spoofing attacks. {\blue Specifically, the detection success rate is improved by up to 65\%, 55\%, and 51\% against distributed, collusion, and mixed attacks, respectively, compared to the benchmarks.
Tri-Level Model for Hybrid Renewable Energy Systems
Authors: Eghbal Hosseini
Date: 2023-12-06 00:00:00
Tasks: Decision Making, model

In practical scenarios, addressing real-world challenges often entails the incorporation of diverse renewable energy sources, such as solar, energy storage systems, and greenhouse gas emissions. The core purpose of these interconnected systems is to optimize a multitude of factors and objectives concurrently. Hence, it is imperative to formulate models that comprehensively cover all these objectives. This paper introduces tri-level mathematical models for Hybrid Renewable Energy Systems (HRESs), offering a framework to concurrently tackle diverse objectives and decision-making levels within the realm of renewable energy integration. The proposed model seeks to maximize the efficiency of solar PV, enhance the performance of energy storage systems, and minimize greenhouse gas emissions.