pwc-archive/papers-with-abstracts huggingface dataset:
Multipolar opinion evolution in biased networks
Authors: Luka Baković, David Ohlin, Giacomo Como, Emma Tegling
Date: 2024-03-06 00:00:00

Motivated by empirical research on bias and opinion formation, we formulate a multidimensional nonlinear opinion-dynamical model where agents have individual biases, which are fixed, as well as opinions, which evolve. The dimensions represent competing options, of which each agent has a relative opinion, and are coupled through normalization of the opinion vector. This can capture, for example, an individual's relative trust in different media. In special cases including where biases are uniform across agents our model achieves consensus, but in general, behaviors are richer and capture multipolar opinion distributions. We examine general fixed points of the system, as well as special cases such as zero biases toward certain options or partitioned decision sets. Lastly, we demonstrate that our model exhibits polarization when biases are spatially correlated across the network, while, as empirical research suggests, a mixed community can mediate biases.
A Unified Model for Active Battery Equalization Systems
Authors: Quan Ouyang, Nourallah Ghaeminezhad, Yang Li, Torsten Wik, Changfu Zou
Date: 2024-03-06 00:00:00

Lithium-ion battery packs demand effective active equalization systems to enhance their usable capacity and lifetime. Despite numerous topologies and control schemes proposed in the literature, conducting quantitative analyses, comprehensive comparisons, and systematic optimization of their performance remains challenging due to the absence of a unified mathematical model at the pack level. To address this gap, we introduce a novel, hypergraph-based approach to establish the first unified model for various active battery equalization systems. This model reveals the intrinsic relationship between battery cells and equalizers by representing them as the vertices and hyperedges of hypergraphs, respectively. With the developed model, we identify the necessary condition for all equalization systems to achieve balance through controllability analysis, offering valuable insights for selecting the number of equalizers. Moreover, we prove that the battery equalization time is inversely correlated with the second smallest eigenvalue of the hypergraph's Laplacian matrix of each equalization system. This significantly simplifies the selection and optimized design of equalization systems, obviating the need for extensive experiments or simulations to derive the equalization time. Illustrative results demonstrate the efficiency of the proposed model and validate our findings.
Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Authors: Nan Lu, Quan Ouyang, Yang Li, Changfu Zou
Date: 2024-03-06 00:00:00
Tasks: Load Forecasting, Time Series

Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead electrical load forecasting. Firstly, four types of features are extracted from the original electrical load dataset, including the historical time series, time index features, historical statistical features, and similarity features. Then, a hybrid LSTM-based electrical load forecasting model is designed, where an LSTM neural network block and a fully-connected neural network block are integrated that can model both temporal features (historical time series) and non-temporal features (the rest features). A gradient regularization-based offline training algorithm and an output layer parameter fine-tuning-based online model correction method are developed to enhance the model's capabilities to defend against disturbance and adapt to the latest load data distribution, thus improving the forecasting accuracy. At last, extensive experiments are carried out to validate the effectiveness of the proposed electrical load forecasting strategy with superior accuracy compared with commonly used forecasting models.
Robust Safety-Critical Control for Systems with Sporadic Measurements and Dwell Time Constraints
Authors: Joseph Breeden, Luca Zaccarian, Dimitra Panagou
Date: 2024-03-06 00:00:00

This paper presents extensions of control barrier function (CBF) theory to systems with disturbances wherein a controller only receives measurements infrequently and operates open-loop between measurements, while still satisfying state constraints. The paper considers both impulsive and continuous actuators, and models the actuators, measurements, disturbances, and timing constraints as a hybrid dynamical system. We then design an open-loop observer that bounds the worst-case uncertainty between measurements. We develop definitions of CBFs for both actuation cases, and corresponding conditions on the control input to guarantee satisfaction of the state constraints. We apply these conditions to simulations of a satellite rendezvous in an elliptical orbit and autonomous orbit stationkeeping.
3D-Printed Dielectric Image Lines towards Chip-to-Chip Interconnects for subTHz-Applications
Authors: Leonhard Hahn, Tim Pfahler, Tobias Bader, Gerald Gold, Martin Vossiek, Christian Carlowitz
Date: 2024-03-06 00:00:00

This paper reports on 3D-printed dielectric image lines for low-loss subTHz applications between 140 and 220 GHz. In contrast to conventional dielectric waveguides, a conductive copper substrate is used to achieve robust routing and increased mechanical stability. For easy integration and characterization of the dielectric image line within a waveguide measurement setup, a low-loss mode-converter for flexible mounting is further designed. The characterized overall system exhibits a broadband match of at least 20 dB over the entire frequency band, with minimal losses of below 0.35 dB/cm. Furthermore, multi-line characterization is performed for de-embedding the propagation parameters {\alpha} and \b{eta} of both the dielectric transmission line and the mode-converter, and finally, the influence of discontinuities such as bending radii on the transmission behavior is evaluated. Due to the simplicity of the underlying 3D-printing technology, the proposed concept features extremely low cost and complexity, yet offers high flexibility and outperforms the losses of conventional transmission lines.
A hybrid dynamical system approach to the impulsive control of spacecraft rendezvous (extended version)
Authors: Alexandre Seuret, Rafael Vazquez, Luca Zaccarian
Date: 2024-03-06 00:00:00

This paper introduces a hybrid dynamical system methodology for managing impulsive control in spacecraft rendezvous and proximity operations under the Hill-Clohessy-Wiltshire model. We address the control design problem by isolating the out-of-plane from the in-plane dynamics and present a feedback control law for each of them. This law is based on a Lyapunov function tailored to each of the dynamics, capable of addressing thruster saturation and also a minimum impulse bit. These Lyapunov functions were found by reformulating the system's dynamics into coordinates that more intuitively represent their physical behavior. The effectiveness of our control laws is then shown through numerical simulation. This is an extended version of an ECC24 article of the same name, which includes the proofs omitted for lack of space.
CNN-based End-to-End Adaptive Controller with Stability Guarantees
Authors: Myeongseok Ryu, Kyunghwan Choi
Date: 2024-03-06 00:00:00

This letter proposes a convolutional neural network (CNN)-based adaptive controller wtih three notable features: 1) it determines control input directly from historical sensor data (in an end-to-end process); 2) it learns the desired control policy during real-time implementation without using a pretrained network (in an online adaptive manner); and 3) the asymptotic tracking error convergence is proven during the learning process (to deliver a stability guarantee). An adaptive law for learning the desired control policy is derived using the gradient descent optimization method, and its stability is analyzed based on the Lyapunov approach. A simulation study using a control-affine nonlinear system demonstrated that the proposed controller exhibits these features, and its performance can be tuned by manipulating the design parameters. In addition, it is shown that the proposed controller has a superior tracking performance to that of a deep neural network (DNN)-based adaptive controller.
On discrete-time polynomial dynamical systems on hypergraphs
Authors: Shaoxuan Cui, Guofeng Zhang, Hildeberto Jardón-Kojakhmetov, Ming Cao
Date: 2024-03-06 00:00:00

This paper studies the stability of discrete-time polynomial dynamical systems on hypergraphs by utilizing the Perron-Frobenius theorem for nonnegative tensors with respect to the tensors Z-eigenvalues and Z-eigenvectors. Firstly, for a multilinear polynomial system on a uniform hypergraph, we study the stability of the origin of the corresponding systems. Next, we extend our results to non-homogeneous polynomial systems on non-uniform hypergraphs. We confirm that the local stability of any discrete-time polynomial system is in general dominated by pairwise terms. Assuming that the origin is locally stable, we construct a conservative (but explicit) region of attraction from the system parameters. Finally, we validate our results via some numerical examples.
On the computation of stable coupled state-space models for dynamic substructuring applications
Authors: R. S. O. Dias, M. Martarelli, P. Chiariotti
Date: 2024-03-05 00:00:00
Tasks: State Space Models

This paper aims at introducing a methodology to compute stable coupled state-space models for dynamic substructuring applications by introducing two novel approaches targeted to accomplish this task: a) a procedure to impose Newtons's second law without relying on the use of undamped RCMs (residual compensation modes) and b) a novel approach to impose stability on unstable coupled state-space models. The enforcement of stability is performed by dividing the unstable model into two different models, one composed by the stable poles (stable model) and the other composed by the unstable ones (unstable model). Then, the poles of the unstable state-space model are forced to be stable, leading to the computation of a stabilized state-space model. Afterwards, to make sure that the Frequency Response Functions (FRFs) of the stabilized model well match the FRFs of the unstable model, the Least-Squares Frequency Domain (LSFD) method is exploited to update the modal parameters of the stabilized model composed by the pairs of complex conjugate poles. The validity of the proposed methodologies is presented and discussed by exploiting experimental data. Indeed, by exploiting the FRFs of a real system, accurate state-space models respecting Newton's second law are computed. Then, decoupling and coupling operations are performed with the identified state-space models, no matter the models resultant from the decoupling/coupling operations are unstable. Stability is then imposed on the computed unstable coupled model by following the approach proposed in this paper. The methodology proved to work well on these data. Moreover, the paper also shows that the coupled state-space models obtained using this methodology are suitable to be exploited in time-domain analyses and simulations.
A Federated Deep Learning Approach for Privacy-Preserving Real-Time Transient Stability Predictions in Power Systems
Authors: Maeshal Hijazi, Payman Dehghanian
Date: 2024-03-05 00:00:00
Tasks: Privacy Preserving

Maintaining the privacy of power system data is essential for protecting sensitive information and ensuring the operation security of critical infrastructure. Therefore, the adoption of centralized deep learning (DL) transient stability assessment (TSA) frameworks can introduce risks to electric utilities. This is because these frameworks make utility data susceptible to cyber threats and communication issues when transmitting data to a central server for training a single TSA model. Additionally, the centralized approach demands significant computational resources, which may not always be readily available. In light of these challenges, this paper introduces a federated DL-based TSA framework designed to identify the operating states of the power system. Instead of local utilities transmitting their data to a central server for centralized model training, they independently train their own TSA models using their respective datasets. Subsequently, the parameters of each local TSA model are sent to a central server for model aggregation, and the resulting model is shared back with the local clients. This approach not only preserves the integrity of local utility data, making it resilient against cyber threats but also reduces the computational demands for local TSA model training. The proposed approach is tested on four local clients each having the IEEE 39-bus test system.
Input-Output Extension of Underactuated Nonlinear Systems
Authors: Mirko Mizzoni, Amr Afifi, Antonio Franchi
Date: 2024-03-05 00:00:00
Tasks: Pose Tracking

This letter addresses the challenge of enhancing the task-space capabilities of commercial underactuated systems through the integration of auxiliary actuators, while preserving their internal control architecture. We propose a method where additional actuators are combined with a feedback-linearizing outer loop controller, enabling full-pose tracking without modifying the platform's certified low-level controller. The key contribution is a theoretical framework establishing conditions under which legacy high-level commands and new actuator inputs can be cohesively coordinated to achieve decoupled control of all degrees of freedom. These conditions are validated through numerical simulations on a quadrotor retrofitted with two additional propellers. The results demonstrate stable 6D trajectory tracking, highlighting the method's ability to reconcile enhanced dexterity with industrial constraints such as certification and safety.
Tracking-in-range Formulations for Numerical Optimal Control
Authors: Nikilesh Ramesh, Eric C. Kerrigan, Yuanbo Nie
Date: 2024-03-05 00:00:00
Tasks: Point Tracking

In contrast to set-point tracking which aims to reduce the tracking error between the tracker and the reference, tracking-in-range problems only focus on whether the tracker is within a given range around the reference, making it more suitable for the mission specifications of many practical applications. In this work, we present novel optimal control formulations to solve tracking-in-range problems, for both problems requiring the tracker to be always in range, and problems allowing the tracker to go out of range to yield overall better outcomes. As the problem naturally involves discontinuous functions, we present alternative formulations and regularisation strategies to improve the performance of numerical solvers. The extension to in-range tracking with multiple trackers and in-range tracking in high dimensional space are also discussed and illustrated with numerical examples, demonstrating substantial increases in mission duration in comparison to traditional set-point tracking.
A Convex Optimization Framework for Computing Robustness Margins of Kalman Filters
Authors: Himanshu Prabhat, Raktim Bhattacharya
Date: 2024-03-05 00:00:00

This paper proposes a novel convex optimization framework for designing robust Kalman filters that guarantee a user-specified steady-state error while maximizing process and sensor noise. The proposed framework simultaneously determines the Kalman gain and the robustness margin in terms of the process and sensor noise. This is the first paper to present such a joint formulation for Kalman filtering. The proposed methodology is validated through two distinct examples: the Clohessy-Wiltshire-Hill equations for a chaser spacecraft in an elliptical orbit and the longitudinal motion model of an F-16 aircraft.
Single-level Robust Bidding of Renewable-only Virtual Power Plant in Energy and Ancillary Service Markets for Worst-case Profit
Authors: Hadi Nemati, Pedro Sánchez-Martín, Ana Baringo, Álvaro Ortega
Date: 2024-03-05 00:00:00
Tasks: Computational Efficiency

This paper proposes a novel single-level robust mathematical approach to model the RES-only Virtual Power Plant (RVPP) bidding problem in the simultaneous Day Ahead Market (DAM) and Secondary Reserve Market (SRM). The worst-case profit of RVPP due to uncertainties related to electricity prices, Non-dispatchable Renewable Energy Sources (ND-RES) production, and flexible demand is captured. In order to find the worst-case profit in a single-level model, the relationship between price and energy uncertainties leads to some non-linear constraints, which are appropriately linearized. The simulation results show the superiority of the proposed robust model compared to those in the literature, as well as its computational efficiency.
Privacy in Multi-agent Systems
Authors: Yongqiang Wang
Date: 2024-03-05 00:00:00

With the increasing awareness of privacy and the deployment of legislations in various multi-agent system application domains such as power systems and intelligent transportation, the privacy protection problem for multi-agent systems is gaining increased traction in recent years. This article discusses some of the representative advancements in the filed.
Time-Reversal of Stochastic Maximum Principle
Authors: Amirhossein Taghvaei
Date: 2024-03-04 00:00:00

Stochastic maximum principle (SMP) specifies a necessary condition for the solution of a stochastic optimal control problem. The condition involves a coupled system of forward and backward stochastic differential equations (FBSDE) for the state and the adjoint processes. Numerical solution of the FBSDE is challenging because the boundary condition of the adjoint process is specified at the terminal time, while the solution should be adaptable to the forward in time filtration of a Wiener process. In this paper, a "time-reversal" of the FBSDE system is proposed that involves integration with respect to a backward in time Wiener process. The time-reversal is used to propose an iterative Monte-Carlo procedure to solves the FBSDE system and its time-reversal simultaneously. The procedure involves approximating the {F\"ollmer's drift} and solving a regression problem between the state and its adjoint at each time. The procedure is illustrated for the linear quadratic (LQ) optimal control problem with a numerical example.
A Frequency-Domain Approach for Enhanced Performance and Task Flexibility in Finite-Time ILC
Authors: Max van Haren, Kentaro Tsurumoto, Masahiro Mae, Lennart Blanken, Wataru Ohnishi, Tom Oomen
Date: 2024-03-04 00:00:00

Iterative learning control (ILC) is capable of improving the tracking performance of repetitive control systems by utilizing data from past iterations. The aim of this paper is to achieve both task flexibility, which is often achieved by ILC with basis functions, and the performance of frequency-domain ILC, with an intuitive design procedure. The cost function of norm-optimal ILC is determined that recovers frequency-domain ILC, and consequently, the feedforward signal is parameterized in terms of basis functions and frequency-domain ILC. The resulting method has the performance and design procedure of frequency-domain ILC and the task flexibility of basis functions ILC, and are complimentary to each other. Validation on a benchmark example confirms the capabilities of the framework.
Progressive Smoothing for Motion Planning in Real-Time NMPC
Authors: Rudolf Reiter, Katrin Baumgärtner, Rien Quirynen, Moritz Diehl
Date: 2024-03-04 00:00:00
Tasks: Autonomous Driving, Model Predictive Control, Motion Planning

Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications. Non-smooth obstacle shapes, such as rectangles, introduce additional local minima in the underlying optimization problem. Smooth over-approximations, e.g., ellipsoidal shapes, limit the performance due to their conservativeness. We propose to vary the smoothness and the related over-approximation by a homotopy. Instead of varying the smoothness in consecutive sequential quadratic programming iterations, we use formulations that decrease the smooth over-approximation from the end towards the beginning of the prediction horizon. Thus, the real-time iterations algorithm is applicable to the proposed NMPC formulation. Different formulations are compared in simulation experiments and shown to successfully improve performance indicators without increasing the computation time.
Tuning and Testing an Online Feedback Optimization Controller to Provide Curative Distribution Grid Flexibility
Authors: Lukas Ortmann, Fabian Böhm, Florian Klein-Helmkamp, Andreas Ulbig, Saverio Bolognani, Florian Dörfler
Date: 2024-03-04 00:00:00

Due to more volatile generation, flexibility will become more important in transmission grids. One potential source of this flexibility can be distribution grids. A flexibility request from the transmission grid to a distribution grid then needs to be split up onto the different flexibility providing units (FPU) in the distribution grid. One potential way to do this is Online Feedback Optimization (OFO). OFO is a new control method that steers power systems to the optimal solution of an optimization problem using minimal model information and computation power. This paper will show how to choose the optimization problem and how to tune the OFO controller. Afterward, we test the resulting controller on a real distribution grid laboratory and show its performance, its interaction with other controllers in the grid, and how it copes with disturbances. Overall, the paper makes a clear recommendation on how to phrase the optimization problem and tune the OFO controller. Furthermore, it experimentally verifies that an OFO controller is a powerful tool to disaggregate flexibility requests onto FPUs while satisfying operational constraints inside the flexibility providing distribution grid.
Cooperative and Interaction-aware Driver Model for Lane Change Maneuver
Authors: Jemin Woo, Changsun Ahn
Date: 2024-03-04 00:00:00
Tasks: Autonomous Vehicles, Decision Making

To achieve complete autonomous vehicles, it is crucial for autonomous vehicles to communicate and interact with their surrounding vehicles. Especially, since the lane change scenarios do not have traffic signals and traffic rules, the interactions between vehicles need to be considered for the autonomous vehicles. To address this issue, we propose a cooperative and interaction-aware decision-making algorithm for autonomous vehicles that stochastically considers the future behavior of surrounding vehicles based on actual driving data. The algorithm is designed for both lane changing and lane keeping vehicles, and effectively considers interaction by using an interaction model based on relative information between vehicles with fewer states. To design the decision-making, the interaction model is defined as Markov decision process, and stochastic dynamic programming is used to solve the Markov decision process. We validate the effectiveness of our proposed algorithm in lane change scenarios that require interaction. Our results demonstrate that the proposed algorithm enables cooperative and interaction-aware decision-making while accommodating various driving styles. Additionally, by comparing it with other methods, such as the intelligent driver model and game theory-based decision-making, we validate the safety and comfortable decision-making of our proposed algorithm. Furthermore, through driving with a human-driven vehicle, it is confirmed that the proposed decision-making enables to cooperatively and effectively drive with humans.