pwc-archive/papers-with-abstracts huggingface dataset:
Feasibility Study of Curvature Effect in Flexible Antenna Arrays for 2-Dimensional Beam Alignment of 6G Wireless Systems
Authors: Mahdi Alesheikh, Soheil Saadat, Hamidreza Aghasi
Date: 2024-09-15 00:00:00

This article investigates the influential role of flexible antenna array curvature on the performance of 6G communication systems with carrier frequencies above 100 GHz. It is demonstrated that the curvature of flexible antenna arrays can be leveraged for 2-dimensional beam alignment in phased arrays with relatively small insertion loss. The effect of antenna array bending on the radiation properties such as gain and antenna impedance are analytically studied and simulated for a 4x4 microstrip patch antenna array operating between 97.5-102.5 GHz. Moreover, the deployment of this flexible antenna array in conjunction with state-of-the-art flexible board packaging techniques is examined for 6G wireless transceivers based on 65nm CMOS technology and simulated for three variants of quadrature amplitude modulation (4QAM, 16 QAM, and 64 QAM). The communication performance in terms of signal-to-noise ratio (SNR) and bit error rate (BER) is evaluated using analytical derivations and simulation results which exhibit a relatively close match.
Ensuring System-Level Protection against Eavesdropping Adversaries in Distributed Dynamical Systems
Authors: Dipankar Maity, Van Sy Mai
Date: 2024-09-14 00:00:00

In this work, we address the objective of protecting the states of a distributed dynamical system from eavesdropping adversaries. We prove that state-of-the-art distributed algorithms, which rely on communicating the agents' states, are vulnerable in that the final states can be perfectly estimated by any adversary including those with arbitrarily small eavesdropping success probability. While existing literature typically adds an extra layer of protection, such as encryption or differential privacy techniques, we demonstrate the emergence of a fundamental protection quotient in distributed systems when innovation signals are communicated instead of the agents' states.
Improved Physics-Informed Neural Network based AC Power Flow for Distribution Networks
Authors: Victor Eeckhout, Hossein Fani, Md Umar Hashmi, Geert Deconinck
Date: 2024-09-14 00:00:00
Tasks: Graph Neural Network

Power flow analysis plays a critical role in the control and operation of power systems. The high computational burden of traditional solution methods led to a shift towards data-driven approaches, exploiting the availability of digital metering data. However, data-driven approaches, such as deep learning, have not yet won the trust of operators as they are agnostic to the underlying physical model and have poor performances in regimes with limited observability. To address these challenges, this paper proposes a new, physics-informed model. More specifically, a novel physics-informed loss function is developed that can be used to train (deep) neural networks aimed at power flow simulation. The loss function is not only based on the theoretical AC power flow equations that govern the problem but also incorporates real physical line losses, resulting in higher loss accuracy and increased learning potential. The proposed model is used to train a Graph Neural Network (GNN) and is evaluated on a small 3-bus test case both against another physics-informed GNN that does not incorporate physical losses and against a model-free technique. The validation results show that the proposed model outperforms the conventional physics-informed network on all used performance metrics. Even more interesting is that the model shows strong prediction capabilities when tested on scenarios outside the training sample set, something that is a substantial deficiency of model-free techniques.
Distribution network reconfiguration for operational objectives: reducing voltage violation incidents and network losses
Authors: Geert Mangelschots, Sari Kerckhove, Md Umar Hashmi, Dirk Van Hertem
Date: 2024-09-14 00:00:00

As the share of Distributed energy resources (DER) in the low voltage distribution network (DN) is expected to rise, a higher and more variable electric load and generation could stress the DNs, leading to increased congestion and power losses. To address these challenges, DSOs will have to invest in strengthening the network infrastructure in the coming decade. This paper looks to minimize the need for flexibility through dynamic DN reconfiguration. Typically, European DNs predominantly use manual switches. Hence, the network configuration is set for longer periods of time. Therefore, an opportunity is missed to benefit from more short-term dynamic switching. In this paper, a method is proposed which identifies the best manual switches to replace with remotely controlled switches based on their performance in terms of avoided voltage congestion incidents and DN power losses. The developed method is an exhaustive search algorithm which divides the problem into 3 subsequent parts, i.e. radial configuration identification, multi-period power flow and impact assessment for reconfigurable switch replacement on DN operation. A numerical evaluation shows that replacing the two top-ranked switches in the test case reduced the power losses by 4.51% and the voltage constraint violations by 38.17%. Thus, investing in only a few reconfigurable switches can substantially improve the operational efficiency of DNs.
Probabilistic Reachability of Discrete-Time Nonlinear Stochastic Systems
Authors: Zishun Liu, Saber Jafarpour, Yongxin Chen
Date: 2024-09-14 00:00:00

In this paper we study the reachability problem for discrete-time nonlinear stochastic systems. Our goal is to present a unified framework for calculating the probabilistic reachable set of discrete-time systems in the presence of both deterministic input and stochastic noise. By adopting a suitable separation strategy, the probabilistic reachable set is decoupled into a deterministic reachable set and the effect of the stochastic noise. To capture the effect of the stochastic noise, in particular sub-Gaussian noise, we provide a probabilistic bound on the distance between a stochastic trajectory and its deterministic counterpart. The key to our approach is a novel energy function called the Averaged Moment Generating Function, which we leverage to provide a high probability bound on this distance. We show that this probabilistic bound is tight for a large class of discrete-time nonlinear stochastic systems and is exact for linear stochastic dynamics. By combining this tight probabilistic bound with the existing methods for deterministic reachability analysis, we propose a flexible framework that can efficiently compute probabilistic reachable sets of stochastic systems. We also provide two case studies for applying our framework to Lipschitz bound reachability and interval-based reachability. Three numerical experiments are conducted to validate the theoretical results.
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
Authors: Jan-Hendrik Ewering, Björn Volkmann, Simon F. G. Ehlers, Thomas Seel, Michael Meindl
Date: 2024-09-14 00:00:00
Tasks: Bayesian Inference, State Estimation, State Space Models

Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, location, wear, or aging. To provide this information, online inference and learning attempts to perform state estimation and (partial) system identification simultaneously. Current works combine tailored estimation schemes with flexible learning-based models but suffer from convergence problems and computational complexity due to many degrees of freedom in the inference problem (i.e., parameters to determine). To resolve these issues, we propose a procedure for data-driven offline conditioning of a highly flexible Gaussian Process (GP) formulation such that online learning is restricted to a subspace, spanned by expressive basis functions. Due to the simplicity of the transformed problem, a standard particle filter can be employed for Bayesian inference. In contrast to most existing works, the proposed method enables online learning of target functions that are nested nonlinearly inside a first-principles model. Moreover, we provide a theoretical quantification of the error, introduced by restricting learning to a subspace. A Monte-Carlo simulation study with a nonlinear battery model shows that the proposed approach enables rapid convergence with significantly fewer particles compared to a baseline and a state-of-the-art method.
Discrete-time Indirect Adaptive Control for Systems with Disturbances via Directional Forgetting: Concurrent Learning Approach
Authors: Satoshi Tsuruhara, Kazuhisa Ito
Date: 2024-09-14 00:00:00

Recently, adaptive control systems with relaxed persistent excitation (PE) conditions have been proposed to guarantee true parameter convergence and improve the transient response. However, in some cases, sufficient control performance and parameter convergence cannot be easily achieved, with stability demonstrated only under ideal conditions, such as the absence of disturbances and matching conditions required. In this study, we propose a novel adaptive control method for discrete-time systems with disturbances, which is not under an ideal case, that combines directional forgetting and concurrent learning. The proposed method does not require the PE condition, information on disturbances, unknown parameters, or matching conditions, and it guarantees uniformly ultimately bounded (UUB). It was also theoretically demonstrated that the ultimate bound can be designed based on the forgetting factor, which is a design parameter. In addition, the upper bound decreases with time step, which is independent of the system order and/or target trajectory due to forgetting factor. This also implies stronger stability than a normal UUB. Numerical simulation results illustrate the effectiveness of the proposed method.
Heterogeneous Roles against Assignment Based Policies in Two vs Two Target Defense Game
Authors: Goutam Das, Violetta Rostobaya, James Berneburg, Zachary I. Bell, Michael Dorothy, Daigo Shishika
Date: 2024-09-14 00:00:00

In this paper, we consider a target defense game in which the attacker team seeks to reach a high-value target while the defender team seeks to prevent that by capturing them away from the target. To address the curse of dimensionality, a popular approach to solve such team-vs-team game is to decompose it into a set of one-vs-one games. Such an approximation assumes independence between teammates assigned to different one-vs-one games, ignoring the possibility of a richer set of cooperative behaviors, ultimately leading to suboptimality. In this paper, we provide teammate-aware strategies for the attacker team and show that they can outperform the assignment-based strategy, if the defenders still employ an assignment-based strategy. More specifically, the attacker strategy involves heterogeneous roles where one attacker actively intercepts a defender to help its teammate reach the target. We provide sufficient conditions under which such a strategy benefits the attackers, and we validate the results using numerical simulations.
Kernel-Based Regularized Continuous-Time System Identification from Sampled Data
Authors: Xiaozhu Fang, Biqiang Mu, Tianshi Chen
Date: 2024-09-14 00:00:00

The identification of continuous-time (CT) systems from discrete-time (DT) input and output signals, i.e., the sampled data, has received considerable attention for half a century. The state-of-the-art methods are parametric methods and thus subject to the typical issues of parametric methods. In the last decade, a major advance in system identification is the so-called kernel-based regularization method (KRM), which is free of the issues of parametric methods. It is interesting to test the potential of KRM on CT system identification. However, very few results have been reported, mainly because the estimators have no closed forms for general CT input signals, except for some very special cases. In this paper, we show for KRM that the estimators have closed forms when the DT input signal has the typical intersample behavior, i.e., zero-order hold or band-limited, and this paves the way for the application of KRM for CT system identification. Numerical Monte Carlo simulations show that the proposed method is more robust than the state-of-the-art methods and more accurate when the sample size is small.
Capability Augmentation for Heterogeneous Dynamic Teaming with Temporal Logic Tasks
Authors: Carter Berlind, Wenliang Liu, Alyssa Pierson, Calin Belta
Date: 2024-09-14 00:00:00

This paper considers how heterogeneous multi-agent teams can leverage their different capabilities to mutually improve individual agent performance. We present Capability-Augmenting Tasks (CATs), which encode how agents can augment their capabilities based on interactions with other teammates. Our framework integrates CAT into the semantics of Metric Temporal Logic (MTL), which defines individual spatio-temporal tasks for all agents. A centralized Mixed-Integer Program (MIP) is used to synthesize trajectories for all agents. We compare the expressivity of our approach to a baseline of Capability Temporal Logic Plus (CaTL+). Case studies demonstrate that our approach allows for simpler specifications and improves individual performance when agents leverage the capabilities of their teammates.
Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets
Authors: Hamid Davoudi, Fengyu Wang, Yonghong Chen, Di Shi, Alinson Xavier, Feng Qiu
Date: 2024-09-13 00:00:00

Pricing and settlement mechanisms are crucial for efficient re-source allocation, investment incentives, market competition, and regulatory oversight. In the United States, Regional Transmission Operators (RTOs) adopts a uniform pricing scheme that hinges on the marginal costs of supplying additional electricity. This study investigates the pricing and settlement impacts of alternative reserve constraint modeling, highlighting how even slight variations in the modeling of constraints can drastically alter market clearing prices, reserve quantities, and revenue outcomes. Focusing on the diverse market designs and assumptions in ancillary services by U.S. RTOs, particularly in relation to capacity sharing and reserve substitutions, the research examines four distinct models that combine these elements based on a large-scale synthetic power system test data. Our study provides a critical insight into the economic implications and the underlying factors of these alternative reserve constraints through market simulations and data analysis.
Stability Margins of Neural Network Controllers
Authors: Neelay Junnarkar, Murat Arcak, Peter Seiler
Date: 2024-09-13 00:00:00

We present a method to train neural network controllers with guaranteed stability margins. The method is applicable to linear time-invariant plants interconnected with uncertainties and nonlinearities that are described by integral quadratic constraints. The type of stability margin we consider is the disk margin. Our training method alternates between a training step to maximize reward and a stability margin-enforcing step. In the stability margin enforcing-step, we solve a semidefinite program to project the controller into the set of controllers for which we can certify the desired disk margin.
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces
Authors: Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini
Date: 2024-09-13 00:00:00
Tasks: Continual Learning, EEG, Transfer Learning

Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and signal variability. Classification models need periodic adaptation for real-life use, making an optimal re-training strategy essential to maximize user acceptance and maintain high performance. We propose TOR, a train-on-request workflow that enables user-specific model adaptation to novel conditions, addressing signal variability over time. Using continual learning, TOR preserves knowledge across sessions and mitigates inter-session variability. With TOR, users can refine, on demand, the model through on-device learning (ODL) to enhance accuracy adapting to changing conditions. We evaluate the proposed methodology on a motor-movement dataset recorded with a non-stigmatizing wearable BMI headband, achieving up to 92% accuracy and a re-calibration time as low as 1.6 minutes, a 46% reduction compared to a naive transfer learning workflow. We additionally demonstrate that TOR is suitable for ODL in extreme edge settings by deploying the training procedure on a RISC-V ultra-low-power SoC (GAP9), resulting in 21.6 ms of latency and 1 mJ of energy consumption per training step. To the best of our knowledge, this work is the first demonstration of an online, energy-efficient, dynamic adaptation of a BMI model to the intrinsic variability of EEG signals in real-time settings.
Thermal Modelling of Battery Cells for Optimal Tab and Surface Cooling Control
Authors: Godwin K. Peprah, Yicun Huang, Torsten Wik, Faisal Altaf, Changfu Zou
Date: 2024-09-13 00:00:00
Tasks: Computational Efficiency

Optimal cooling that minimises thermal gradients and the average temperature is essential for enhanced battery safety and health. This work presents a new modelling approach for battery cells of different shapes by integrating Chebyshev spectral-Galerkin method and model component decomposition. As a result, a library of reduced-order computationally efficient battery thermal models is obtained, characterised by different numbers of states. These models are validated against a high-fidelity finite element model and are compared with a thermal equivalent circuit (TEC) model under real-world vehicle driving and battery cooling scenarios. Illustrative results demonstrate that the proposed model with four states can faithfully capture the two-dimensional thermal dynamics, while the model with only one state significantly outperforms the widely-used two-state TEC model in both accuracy and computational efficiency, reducing computation time by 28.7%. Furthermore, our developed models allow for independent control of tab and surface cooling channels, enabling effective thermal performance optimisation. Additionally, the proposed model's versatility and effectiveness are demonstrated through various applications, including the evaluation of different cooling scenarios, closed-loop temperature control, and cell design optimisation.
Optimal Operation of a Building with Electricity-Heat Networks and Seasonal Storage
Authors: Eléa Prat, Pierre Pinson, Richard M. Lusby, Riwal Plougonven, Jordi Badosa, Philippe Drobinski
Date: 2024-09-13 00:00:00
Tasks: Model Predictive Control, Prediction

As seasonal thermal energy storage emerges as an efficient solution to reduce CO2 emissions of buildings, challenges appear related to its optimal operation. In a system including short-term electricity storage, long-term heat storage, and where electricity and heat networks are connected through a heat pump, it becomes crucial to operate the system on two time scales. Based on real data from a university building, we simulate the operation of such a system over a year, comparing different strategies based on model predictive control (MPC). The first objective of this paper is to determine the minimum prediction horizon to retrieve the results of the full-horizon operation problem with cost minimization. The second objective is to evaluate a method that combines MPC with setting targets on the heat storage level at the end of the prediction horizon, based on historical data. For a prediction horizon of 6 days, the suboptimality gap with the full-horizon results is 4.31%, compared to 11.42% when using a prediction horizon of 42 days and fixing the final level to be equal to the initial level, which is a common approach.
Robust Output Feedback of Nonlinear Systems through the Efficient Solution of Min-Max Optimization Problems
Authors: Jad Wehbeh, Eric C. Kerrigan
Date: 2024-09-13 00:00:00
Tasks: State Estimation

We examine robust output feedback control of discrete-time nonlinear systems with bounded uncertainties affecting the dynamics and measurements. Specifically, we demonstrate how to construct semi-infinite programs that produce gains to minimize some desired performance cost over a finite prediction horizon for the worst-case realization of the system's uncertainties, while also ensuring that any specified nonlinear constraints are always satisfied. The solution process relies on an implicit description of the feasible state space through prior measurements and the system dynamics, and assumes that the system is always in the subset of the feasible space that is most detrimental to performance. In doing so, we can guarantee that the system's true state will meet all of the chosen performance criteria without resorting to any explicit state estimation. Under some smoothness assumptions, we also discuss solving these semi-infinite programs through local reduction techniques, which generate optimal scenario sets for the uncertainty realizations to approximate the continuous uncertainty space and speed up the computation of optima. When tested on a two-dimensional nonlinear quadrotor, the developed method achieves robust constraint satisfaction and tracking despite dealing with highly uncertain measurements and system dynamics.
Observer-Based Control of Second-Order Multi-vehicle Systems in Bearing-Persistently Exciting Formations
Authors: Zhiqi Tang, Baris Fidan, Karl H. Johansson, Jonas Martensson, Tarek Hamel
Date: 2024-09-13 00:00:00
Tasks: Position, State Estimation

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the \textit{Bearing Persistently Exciting} (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers.