pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-temporal Patterns between ENSO and Weather-related Power Outages in the Continental United States
Authors: Long Huo, Xin Chen, Kaiwen Li, Fengying Cai, Jürgen Kurths
Date: 2024-06-20 00:00:00

El Ni\~no-Southern Oscillation (ENSO) exhibits significant impacts on the frequency of extreme weather events and its socio-economic implications prevail on a global scale. However, a fundamental gap still exists in understanding the relationship between the ENSO and weather-related power outages in the continental United States. Through 24-year (2000-2023) composite and statistical analysis, our study reveals that higher power outage numbers (PONs) are observed from the developing winter to the decaying summer of La Ni\~na phases. In particular, during the decaying spring, high La Ni\~na intensity favors the occurrences of power outage over the west coast and east of the United States, by modulating the frequency of extreme precipitations and heatwaves. Furthermore, projected increasing heatwaves from the Coupled Model Intercomparison Project Phase 6 (CMIP6) indicate that spring-time PONs over the eastern United States occur about 11 times higher for the mid-term future (2041-2060) and almost 26 times higher for the long-term future (2081-2100), compared with 2000-2023. Our study provides a strong recommendation for building a more climate-resilient power system.
Adaptive Deep Neural Network-Based Control Barrier Functions
Authors: Hannah M. Sweatland, Omkar Sudhir Patil, Warren E. Dixon
Date: 2024-06-20 00:00:00
Tasks: parameter estimation

Safety constraints of nonlinear control systems are commonly enforced through the use of control barrier functions (CBFs). Uncertainties in the dynamic model can disrupt forward invariance guarantees or cause the state to be restricted to an overly conservative subset of the safe set. In this paper, adaptive deep neural networks (DNNs) are combined with CBFs to produce a family of controllers that ensure safety while learning the system's dynamics in real-time without the requirement for pre-training. By basing the least squares adaptation law on a state derivative estimator-based identification error, the DNN parameter estimation error is shown to be uniformly ultimately bounded. The convergent bound on the parameter estimation error is then used to formulate CBF-constraints in an optimization-based controller to guarantee safety despite model uncertainty. Furthermore, the developed method is extended for use under intermittent loss of state-feedback. A switched systems analysis for CBFs is provided with a maximum dwell-time condition during which the feedback can be unavailable. Comparative simulation results demonstrate the ability of the developed method to ensure safety in an adaptive cruise control problem and when feedback is lost, unlike baseline methods. Results show improved performance compared to baseline methods and demonstrate the ability of the developed method to ensure safety in feedback-denied environments.
Ring-LWE based encrypted controller with unlimited number of recursive multiplications and effect of error growth
Authors: Yeongjun Jang, Joowon Lee, Seonhong Min, Hyesun Kwak, Junsoo Kim, Yongsoo Song
Date: 2024-06-20 00:00:00

In this paper, we propose an encrypted dynamic controller that executes an unlimited number of recursive homomorphic multiplications on a Ring Learning With Errors (Ring-LWE) based cryptosystem without bootstrapping. The proposed controller exhibits lower computational complexity compared to existing encrypted controllers implemented on LWE based schemes due to the polynomial structure of Ring-LWE. However, the structural difference introduces additional difficulties in analyzing the effect of error growth; Ring-LWE based schemes inject multiple error coefficients when encrypting a single message, which accumulate under recursive homomorphic multiplications. We show that their effect on the control performance can be arbitrarily bounded by the closed-loop stability, thus recovering the performance of the unencrypted controller. Furthermore, a novel method to ``pack'' a vector into a polynomial is presented, which enhances computational and memory efficiency when applied to the proposed encrypted controller. The effectiveness of the proposed design is demonstrated through numerical simulations.
Enhanced Optimal Power Flow Based Droop Control in MMC-MTDC Systems
Authors: Hongjin Du, Rashmi Prasad, Aleksandra Lekic, Pedro P. Vergara, Peter Palensky
Date: 2024-06-20 00:00:00

Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and DC voltage deviations while ensuring the stable operation of the MTDC grid by dynamically adjusting the system operation points. The modified Nordic 32 test system with an embedded 4-terminal DC grid is modeled in Julia and the proposed control strategy is applied to the power model. The results demonstrate the feasibility and effectiveness of the proposed droop control strategy, affirming its potential value in enhancing the performance and reliability of hybrid AC-DC power systems.
Reliable State Estimation in a Truck-Semitrailer Combination using an Artificial Neural Network-Aided Extended Kalman Filter
Authors: Jan-Hendrik Ewering, Zygimantas Ziaukas, Simon F. G. Ehlers, Thomas Seel
Date: 2024-06-20 00:00:00
Tasks: State Estimation

Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-based and learning-based methods exist, but both suffer from individual limitations. On the one hand, model-based estimation performance is often limited by the models' accuracy. On the other hand, learning-based estimators usually do not perform well in "unknown" conditions (bad generalization), which is particularly critical for semitrailers as their payload changes significantly in operation. To the best of the authors' knowledge, this work is the first to analyze the capability of state-of-the-art estimators for semitrailers to generalize across "unknown" loading states. Moreover, a novel hybrid Extended Kalman Filter (H-EKF) that takes advantage of accurate Artificial Neural Network (ANN) estimates while preserving reliable generalization capability is presented. It estimates the articulation angle between truck and semitrailer, lateral tire forces and the truck steering angle utilizing sensor data of a standard semitrailer only. An experimental comparison based on a full-scale truck-semitrailer combination indicates the superiority of the H-EKF compared to a state-of-the-art extended Kalman filter and an ANN estimator.
SkyGrid: Energy-Flow Optimization at Harmonized Aerial Intersections
Authors: Sahand Khoshdel, Fatemeh Afghah, Qi Luo
Date: 2024-06-19 00:00:00

The rapid evolution of urban air mobility (UAM) is reshaping the future of transportation by integrating aerial vehicles into urban transit systems. The design of aerial intersections plays a critical role in the phased development of UAM systems to ensure safe and efficient operations in air corridors. This work adapts the concept of rhythmic control of connected and automated vehicles (CAVs) at unsignalized intersections to address complex traffic control problems. This control framework assigns UAM vehicles to different movement groups and significantly reduces the computation of routing strategies to avoid conflicts. In contrast to ground traffic, the objective is to balance three measures: minimizing energy utilization, maximizing intersection flow (throughput), and maintaining safety distances. This optimization method dynamically directs traffic with various demands, considering path assignment distributions and segment-level trajectory coefficients for straight and curved paths as control variables. To the best of our knowledge, this is the first work to consider a multi-objective optimization approach for unsignalized intersection control in the air and to propose such optimization in a rhythmic control setting with time arrival and UAM operational constraints. A sensitivity analysis with respect to inter-platoon safety and straight/left demand balance demonstrates the effectiveness of our method in handling traffic under various scenarios.
COAC: Cross-layer Optimization of Accelerator Configurability for Efficient CNN Processing
Authors: Steven Colleman, Man Shi, Marian Verhelst
Date: 2024-06-19 00:00:00

To achieve high accuracy, convolutional neural networks (CNNs) are increasingly growing in complexity and diversity in layer types and topologies. This makes it very challenging to efficiently deploy such networks on custom processor architectures for resource-scarce edge devices. Existing mapping exploration frameworks enable searching for the optimal execution schedules or hardware mappings of individual network layers, by optimizing each layer's spatial (dataflow parallelization) and temporal unrolling (execution order). However, these tools fail to take into account the overhead of supporting different unrolling schemes within a common hardware architecture. Using a fixed unrolling scheme across all layers is also not ideal, as this misses significant opportunities for energy and latency savings from optimizing the mapping of diverse layer types. A balanced approach assesses the right amount of mapping flexibility needed across target neural networks, while taking into account the overhead to support multiple unrollings. This paper, therefore, presents COAC, a cross-layer design space exploration and mapping framework to optimize the flexibility of neural processing architectures by balancing configurability overhead against resulting energy and latency savings for end-to-end inference. COAC does not only provide a systematical analysis of the architectural overhead in function of the supported spatial unrollings, but also builds an automated flow to find the best unrolling combination(s) for efficient end-to-end inference with limited hardware overhead. Results demonstrate that architectures with carefully optimized flexibility can achieve up to 38% EDP (energy-delay-product) savings for a set of six neural networks at the expense of a relative area increase of 9.5%.
Advanced Maximum Adhesion Tracking Strategies in Railway Traction Drives
Authors: Ahmed Fathy Abouzeid, Juan Manuel Guerrero, Lander Lejarza, Iker Muniategui, Aitor Endemaño, Fernando Briz
Date: 2024-06-19 00:00:00

Modern railway traction systems are often equipped with anti-slip control strategies to comply with performance and safety requirements. A certain amount of slip is needed to increase the torque transferred by the traction motors onto the rail. Commonly, constant slip control is used to limit the slip velocity between the wheel and rail avoiding excessive slippage and vehicle derailment. This is at the price of not fully utilizing the train's traction and braking capabilities. Finding the slip at which maximum traction force occurs is challenging due to the non-linear relationship between slip and wheel-rail adhesion coefficient, as well as to its dependence on rail and wheel conditions. Perturb and observe (P\&O) and steepest gradient (SG) methods have been reported for the Maximum Adhesion Tracking (MAT) search. However, both methods exhibit weaknesses. Two new MAT strategies are proposed in this paper which overcome the limitations of existing methods, using Fuzzy Logic Controller (FLC) and Particle Swarm Optimization (PSO) respectively. Existing and proposed methods are first simulated and further validated experimentally using a scaled roller rig under identical conditions. The results show that the proposed methods improve the traction capability with lower searching time and oscillations compared to existing solutions. Tuning complexity and computational requirements will also be shown to be favorable to the proposed methods.
Measured-state conditioned recursive feasibility for stochastic model predictive control
Authors: Mirko Fiacchini, Martina Mammarella, Fabrizio Dabbene
Date: 2024-06-19 00:00:00
Tasks: Model Predictive Control

In this paper, we address the problem of designing stochastic model predictive control (MPC) schemes for linear systems affected by unbounded disturbances. The contribution of the paper is twofold. First, motivated by the difficulty of guaranteeing recursive feasibility in this framework, due to the nonzero probability of violating chance-constraints in the case of unbounded noise, we introduce the novel definition of measured-state conditioned recursive feasibility in expectation. Second, we construct a stochastic MPC scheme, based on the introduction of ellipsoidal probabilistic reachable sets, which implements a closed-loop initialization strategy, i.e., the current measured-state is employed for initializing the optimization problem. This new scheme is proven to satisfy the novel definition of recursive feasibility, and its superiority with respect to open-loop initialization schemes, arising from the fact that one never neglects the information brought by the current measurement, is shown through numerical examples.
Design of Phase Locked Loop in 180 nm Technology
Authors: Priyam Kumar, Akshada Khele, Aditee C. Joshi
Date: 2024-06-19 00:00:00

The presented paper introduces a design for a phase-locked loop (PLL) that is utilized in frequency synthesis and modulation-demodulation within communication systems and in VLSI applications. The CMOS PLL is designed using 180 nm Fabrication Technology on Cadence Virtuoso Tool with a supply voltage of 1.8 V. The performance is evaluated through simulations and measurements, which demonstrate its ability to track and lock onto the input frequency. The PLL is a frequency synthesizer implemented to generate 2.4 GHz frequency. The input reference clock from a crystal oscillator is 150 MHz square wave. Negative feedback is given by divide-by-16 frequency divider, ensuring the phase and frequency synchronization between the divided signal and the reference signal. The design has essential components such as a phase frequency detector, charge pump, loop filter, current-starved voltage-controlled oscillator (CSVCO), and frequency divider. Through their collaborative operation, the system generates an output frequency that is 16 times the input frequency. The centre frequency of the 3-stage CSVCO is 3.208 GHz at 900 mV input voltage. With an input voltage ranging from 0.4 V to 1.8 V, the VCO offers a tuning range that spans from 1.066 GHz to 3.731 GHz.PLL demonstrates a lock-in range spanning from 70.4 MHz to 173 MHz, with an output frequency range of 1.12 GHz to 2.78 GHz. It achieves a lock time of 260.03 ns and consumes a maximum power of 5.15 mW at 2.4 GHz.
The effect of control barrier functions on energy transfers in controlled physical systems
Authors: Federico Califano, Riccardo Zanella, Alessandro Macchelli, Stefano Stramigioli
Date: 2024-06-19 00:00:00

Using a port-Hamiltonian formalism, we show the qualitative and quantitative effect of safety-critical control implemented with control barrier functions (CBFs) on the power balance of controlled physical systems. The presented results will provide novel tools to design CBFs inducing desired energetic behaviors of the closed-loop system, including nontrivial damping injection effects and non-passive control actions, effectively injecting energy in the system in a controlled manner. Simulations validate the stated results.
Safe and Non-Conservative Trajectory Planning for Autonomous Driving Handling Unanticipated Behaviors of Traffic Participants
Authors: Tommaso Benciolini, Michael Fink, Nehir Güzelkaya, Dirk Wollherr, Marion Leibold
Date: 2024-06-19 00:00:00
Tasks: Autonomous Driving, Model Predictive Control, Trajectory Planning

Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative planning, but do not rule out a (small) probability of collision. We propose a control scheme that yields an efficient trajectory based on SMPC when the traffic scenario allows, still avoiding that the vehicle causes collisions with traffic participants if the latter move according to the prediction assumptions. If some traffic participant does not behave as anticipated, no safety guarantee can be given. Then, our approach yields a trajectory which minimizes the probability of collision, using Constraint Violation Probability Minimization techniques. Our algorithm can also be adapted to minimize the anticipated harm caused by a collision. We provide a thorough discussion of the benefits of our novel control scheme and compare it to a previous approach through numerical simulations from the CommonRoad database.
State Anti-windup: A New Methodology for Tackling State Constraints at Both Synthesis and Implementation Levels
Authors: Amir H. Abolmasoumi, Bogdan Marinescu
Date: 2024-06-19 00:00:00

The anti-windup compensation typically addresses strict control limitations in control systems. However, there is a clear need for an equivalent solution for the states/outputs of the system. This paper introduces a novel methodology for the state anti-windup compensator. Unlike state-constrained control methods, which often focus on incorporating soft constraints into the design or fail to react adequately to constraint violations in practical settings, the proposed methodology treats state constraints as implement-oriented soft-hard constraints. This is achieved by integrating a saturation block within the structure of the safety compensator, referred to as the state anti-windup (SANTW) compensator. Similar to input anti-windup schemes, the SANTW design is separated from the nominal controller design. The problem is formulated as a disturbance rejection one to directly minimize the saturation. The paper develops two Hinf optimization frameworks using frequency-domain solutions and linear matrix inequalities. It then addresses constraints on both inputs and states, resulting in a unified Input-State Anti-windup (IS-ANTW) compensator synthesized using non-smooth Hinf optimization. This method also offers the flexibility of having a fixed-order compensator, crucial in many practical applications. Additionally, the study evaluates the proposed compensator's performance in managing current fluctuations from renewable energy sources during grid faults, demonstrating its effectiveness through detailed Electromagnetic Transient (EMT) simulations of grid-connected DC-AC converters.
GPU-Accelerated DCOPF using Gradient-Based Optimization
Authors: Seide Saba Rafiei, Samuel Chevalier
Date: 2024-06-19 00:00:00
Tasks: CPU, GPU

DC Optimal Power Flow (DCOPF) is a key operational tool for power system operators, and it is embedded as a subproblem in many challenging optimization problems (e.g., line switching). However, traditional CPU-based solve routines (e.g., simplex) have saturated in speed and are hard to parallelize. This paper focuses on solving DCOPF problems using gradient-based routines on Graphics Processing Units (GPUs), which have massive parallelization capability. To formulate these problems, we pose a Lagrange dual associated with DCOPF (linear and quadratic cost curves), and then we explicitly solve the inner (primal) minimization problem with a dual norm. The resulting dual problem can be efficiently iterated using projected gradient ascent. After solving the dual problem on both CPUs and GPUs to find tight lower bounds, we benchmark against Gurobi and MOSEK, comparing convergence speed and tightness on the IEEE 2000, 4601, and 10000 bus systems. We provide reliable and tight lower bounds for these problems with, at best, 5.4x speedup over a conventional solver.
Temporal Complexity of a Hopfield-Type Neural Model in Random and Scale-Free Graphs
Authors: Marco Cafiso, Paolo Paradisi
Date: 2024-06-06 00:00:00

The Hopfield network model and its generalizations were introduced as a model of associative, or content-addressable, memory. They were widely investigated both as an unsupervised learning method in artificial intelligence and as a model of biological neural dynamics in computational neuroscience. The complexity features of biological neural networks have attracted the scientific community's interest for the last two decades. More recently, concepts and tools borrowed from complex network theory were applied to artificial neural networks and learning, thus focusing on the topological aspects. However, the temporal structure is also a crucial property displayed by biological neural networks and investigated in the framework of systems displaying complex intermittency. The Intermittency-Driven Complexity (IDC) approach indeed focuses on the metastability of self-organized states, whose signature is a power-decay in the inter-event time distribution or a scaling behaviour in the related event-driven diffusion processes. The investigation of IDC in neural dynamics and its relationship with network topology is still in its early stages. In this work, we present the preliminary results of an IDC analysis carried out on a bio-inspired Hopfield-type neural network comparing two different connectivities, i.e., scale-free vs. random network topology. We found that random networks can trigger complexity features similar to that of scale-free networks, even if with some differences and for different parameter values, in particular for different noise levels