pwc-archive/papers-with-abstracts huggingface dataset:
Towards Achieving Cooperation Compliance of Human Drivers in Mixed Traffic
Authors: Anni Li, Christos G. Cassandras
Date: 2024-05-27 00:00:00

We consider a mixed-traffic environment in transportation systems, where Connected and Automated Vehicles (CAVs) coexist with potentially non-cooperative Human-Driven Vehicles (HDVs). We develop a cooperation compliance control framework to incentivize HDVs to align their behavior with socially optimal objectives using a ``refundable toll'' scheme so as to achieve a desired compliance probability for all non-compliant HDVs through a feedback control mechanism combining global with local (individual) components. We apply this scheme to the lane-changing problem, where a ``Social Planner'' provides references to the HDVs, measures their state errors, and induces cooperation compliance for safe lane-changing through a refundable toll approach. Simulation results are included to show the effectiveness of our cooperation compliance controller in terms of improved compliance and lane-changing maneuver safety and efficiency when non-cooperative HDVs are present.
Revised Optimal design of power electronic transformer based on hybrid MMC under over-modulation operation
Authors: Yaqian Zhang, Xudong Zhang, Jianzhong Zhang, Fujin Deng
Date: 2024-05-27 00:00:00

The bridge arm of the hybrid modular multilevel converter (MMC) is composed of half-bridge and full-bridge sub-modules cascaded together. Compared with the half-bridge MMC, it can operate in the boost-AC mode, where the modulation index can be higher than 1, and the DC voltage and the AC voltage level are no longer mutually constrained; compared with the full-bridge MMC, it has lower switching device costs and losses. When the hybrid MMC boost-AC mode is used in the power electronic transformer, the degree of freedom in system design is improved, and the cost and volume of the power electronic transformer system can be further reduced. This paper analyzes how to make full use of the newly added modulation index of freedom introduced by the boost-AC hybrid MMC to optimize the power electronic transformer system, and finally gives the optimal modulation index selection scheme of the hybrid MMC for different optimization objectives.
Physically Consistent Modeling & Identification of Nonlinear Friction with Dissipative Gaussian Processes
Authors: Rui Dai, Giulio Evangelisti, Sandra Hirche
Date: 2024-05-27 00:00:00
Tasks: Friction, Gaussian Processes

Friction modeling has always been a challenging problem due to the complexity of real physical systems. Although a few state-of-the-art structured data-driven methods show their efficiency in nonlinear system modeling, deterministic passivity as one of the significant characteristics of friction is rarely considered in these methods. To address this issue, we propose a Gaussian Process based model that preserves the inherent structural properties such as passivity. A matrix-vector physical structure is considered in our approaches to ensure physical consistency, in particular, enabling a guarantee of positive semi-definiteness of the damping matrix. An aircraft benchmark simulation is employed to demonstrate the efficacy of our methodology. Estimation accuracy and data efficiency are increased substantially by considering and enforcing more structured physical knowledge. Also, the fulfillment of the dissipative nature of the aerodynamics is validated numerically.
Graphon Particle Systems, Part I: Spatio-Temporal Approximation and Law of Large Numbers
Authors: Yan Chen, Tao Li
Date: 2024-05-27 00:00:00

We study a class of graphon particle systems with time-varying random coefficients. In a graphon particle system, the interactions among particles are characterized by the coupled mean field terms through an underlying graphon and the randomness of the coefficients comes from the stochastic processes associated with the particle labels. By constructing two-level approximated sequences converging in 2-Wasserstein distance, we prove the existence and uniqueness of the solution to the system. Besides, by constructing two-level approximated functions converging to the graphon mean field terms, we establish the law of large numbers, which reveals that if the number of particles tends to infinity and the discretization step tends to zero, then the discrete-time interacting particle system over a large-scale network converges to the graphon particle system. As a byproduct, we discover that the graphon particle system can describe the limiting dynamics of the distributed stochastic gradient descent algorithm over the large-scale network and prove that if the gradients of the local cost functions are Lipschitz continuous, then the graphon particle system can be regarded as the spatio-temporal approximation of the discrete-time distributed stochastic gradient descent algorithm as the number of network nodes tends to infinity and the algorithm step size tends to zero.
Periodic Scenario Trees: A Novel Framework for Robust Periodic Invariance and Stabilization of Constrained Uncertain Linear Systems
Authors: Yehia Abdelsalam, Sankaranarayanan Subramanian, Sebastian Engell
Date: 2024-05-26 00:00:00

This work proposes a new a framework for determining robust periodic invariant sets and their associated control laws for constrained uncertain linear systems. Necessary and sufficient conditions for stabilizability by periodic controllers are stated and proven using finite step Lyapunov functions for the unconstrained case. We then introduce a scenario tree interpretation of finite step Lyapunov functions for uncertain systems and show that this interpretation results in useful criteria for the design of robust stabilizing controllers. In particular, novel convex feasibility criteria for the synthesis of simple static controllers and what we call linear interpolating tree periodic controllers with memory are derived. It is proven that for a sufficiently large length of the period, a stabilizing linear interpolating tree periodic controller can always be found using the proposed criterion provided that the uncertain system is stabilizable by such controllers. In this sense, the presented synthesis method is non-conservative. The results are then extended to constrained uncertain linear systems and conditions for controllers that realize robust periodic invariant sets which are less conservative than those that result from the known methods in the literature are derived.
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control
Authors: Congbo Bi, Lipeng Zhu, Di Liu, Chao Lu
Date: 2024-05-26 00:00:00
Tasks: Active Learning, Decision Making, reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

The high penetration of renewable energy and power electronic equipment bring significant challenges to the efficient construction of adaptive emergency control strategies against various presumed contingencies in today's power systems. Traditional model-based emergency control methods have difficulty in adapt well to various complicated operating conditions in practice. Fr emerging artificial intelligence-based approaches, i.e., reinforcement learning-enabled solutions, they are yet to provide solid safety assurances under strict constraints in practical power systems. To address these research gaps, this paper develops a safe reinforcement learning (SRL)-based pre-decision making framework against short-term voltage collapse. Our proposed framework employs neural networks for pre-decision formulation, security margin estimation, and corrective action implementation, without reliance on precise system parameters. Leveraging the gradient projection, we propose a security projecting correction algorithm that offers theoretical security assurances to amend risky actions. The applicability of the algorithm is further enhanced through the incorporation of active learning, which expedites the training process and improves security estimation accuracy. Extensive numerical tests on the New England 39-bus system and the realistic Guangdong Provincal Power Grid demonstrate the effectiveness of the proposed framework.
A Prudent Framework for Understanding Risk-Awareness in Demand Response
Authors: Liudong Chen, Bolun Xu
Date: 2024-05-25 00:00:00
Tasks: Decision Making

We show that risk-aware behaviors in demand response originate from superquadratic state-dependent cost functions and price uncertainty with skewed distributions. We obtain such results through developing a novel theoretical demand response framework that combines non-anticipatory multi-stage decision-making with superquadratic cost functions. We introduce the concept of prudent demand, defined by a positive third-order derivative of the cost function, which is the first principle for risk-averse behavior despite a risk-neutral objective. Our analysis establishes that future price uncertainty affects immediate consumption decisions, and the extent of this response scales proportionally with the skewness of the price distribution. We visualize our theoretical findings through numerical simulations and illustrate their practical implications using a real-world case study.
On the damping of power systems during inverter-based blackstart
Authors: Adolfo Anta, Diego Cifelli
Date: 2024-05-25 00:00:00

The massive deployment of inverter-based generation poses several challenges to system operators but also offers new opportunities. In the context of grid recovery, inverter-based generation is expected to take over new responsibilities, as conventional generation that are blackstart capable are being decommissioned. Inverter-based facilities, being very flexible and controllable, have the ability to contribute during grid restoration, although it is still unclear how to deal with the limited ratings of batteries, PV, etc. and how to address nonlinear effects such as inrush currents or harmonics. In this direction, the requirements of grid-forming inverters to comply with black start capabilities are still to be defined. During the first steps of a grid restoration process the dynamics of the grid largely vary from the grid behaviour under normal operation. In particular, the grid is nearly unloaded and therefore poorly damped. Indeed, overvoltages and resonances are a common concern during the energization of long transmission lines. In this work we analyse the spectral properties of unloaded transmission lines, and describe the impact of voltage controllers on the damping of the system. As a byproduct, this analysis can be leveraged to propose high level requirements for grid-forming devices, in order to provide adequate damping during the early stages of a grid restoration process.
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection
Authors: M. Saeid HaghighiFard, Sinem Coleri
Date: 2024-05-25 00:00:00
Tasks: Anomaly Detection, Federated Learning

Hierarchical Federated Learning (HFL) faces the significant challenge of adversarial or unreliable vehicles in vehicular networks, which can compromise the model's integrity through misleading updates. Addressing this, our study introduces a novel framework that integrates dynamic vehicle selection and robust anomaly detection mechanisms, aiming to optimize participant selection and mitigate risks associated with malicious contributions. Our approach involves a comprehensive vehicle reliability assessment, considering historical accuracy, contribution frequency, and anomaly records. An anomaly detection algorithm is utilized to identify anomalous behavior by analyzing the cosine similarity of local or model parameters during the federated learning (FL) process. These anomaly records are then registered and combined with past performance for accuracy and contribution frequency to identify the most suitable vehicles for each learning round. Dynamic client selection and anomaly detection algorithms are deployed at different levels, including cluster heads (CHs), cluster members (CMs), and the Evolving Packet Core (EPC), to detect and filter out spurious updates. Through simulation-based performance evaluation, our proposed algorithm demonstrates remarkable resilience even under intense attack conditions. Even in the worst-case scenarios, it achieves convergence times at $63$\% as effective as those in scenarios without any attacks. Conversely, in scenarios without utilizing our proposed algorithm, there is a high likelihood of non-convergence in the FL process.
From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications
Authors: Wanli Ni, Ailing Zheng, Wen Wang, Dusit Niyato, Naofal Al-Dhahir, Merouane Debbah
Date: 2024-05-25 00:00:00

Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage. In this article, we delve into the progressive development from single to multi-functional RIS (MF-RIS) that enables simultaneous signal amplification, reflection, and refraction. We begin by detailing the hardware design and signal model that distinguish MF-RIS from traditional RISs. Subsequently, we introduce the key technologies underpinning MF-RIS-aided communications, along with the fundamental issues and challenges inherent to its deployment. We then outline the promising applications of MFRIS in the realm of communication, sensing, and computation systems, highlighting its transformative impact on these domains. Lastly, we present simulation results to demonstrate the superiority of MF-RIS in enhancing network performance in terms of spectral efficiency.
Conformal Robust Control of Linear Systems
Authors: Yash Patel, Sahana Rayan, Ambuj Tewari
Date: 2024-05-25 00:00:00
Tasks: Conformal Prediction

End-to-end engineering design pipelines, in which designs are evaluated using concurrently defined optimal controllers, are becoming increasingly common in practice. To discover designs that perform well even under the misspecification of system dynamics, such end-to-end pipelines have now begun evaluating designs with a robust control objective in place of the nominal optimal control setup. Current approaches of specifying such robust control subproblems, however, rely on hand specification of perturbations anticipated to be present upon deployment or margin methods that ignore problem structure, resulting in a lack of theoretical guarantees and overly conservative empirical performance. We, instead, propose a novel methodology for LQR systems that leverages conformal prediction to specify such uncertainty regions in a data-driven fashion. Such regions have distribution-free coverage guarantees on the true system dynamics, in turn allowing for a probabilistic characterization of the regret of the resulting robust controller. We then demonstrate that such a controller can be efficiently produced via a novel policy gradient method that has convergence guarantees. We finally demonstrate the superior empirical performance of our method over alternate robust control specifications, such as $H_{\infty}$ and LQR with multiplicative noise, across a collection of engineering control systems.
Integrated Design for Wave Energy Converter Farms: Assessing Plant, Control, Layout, and Site Selection Coupling in the Presence of Irregular Waves
Authors: Saeed Azad, Suraj Khanal, Daniel R. Herber, Gaofeng Jia
Date: 2024-05-24 00:00:00

A promising direction towards reducing the levelized cost of energy for wave energy converter (WEC) farms is to improve their performance. WEC design studies generally focus on a single design domain (e.g., geometry, control, or layout) to improve the farm's performance under simplifying assumptions, such as regular waves. This strategy, however, has resulted in design recommendations that are impractical or limited in scope because WEC farms are complex systems that exhibit strong coupling among geometry, control, and layout domains. In addition, the location of the candidate site, which has a large impact on the performance of the farm, is often overlooked. Motivated by some of the limitations observed in WEC literature, this study uses an integrated design framework, based on simultaneous control co-design (CCD) principles, to discuss the impact of site selection and wave type on WEC farm design. Interactions among plant, control, and layout are also investigated and discussed using a wide range of simulations and optimization studies. All of the studies were conducted using frequency-domain heaving cylinder WEC devices within a farm with a linear reactive controller in the presence of irregular probabilistic waves. The results provide high-level guidelines to help the WEC design community move toward an integrated design perspective.
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
Authors: Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P. A. Bordas, Andreas G. Boudouvis
Date: 2024-05-24 00:00:00
Tasks: Sensitivity

This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through this work, we address three key objectives. Firstly, our methodology relies on process outcomes, derived by a detailed CFD model, to identify clusters of "outcomes" corresponding to distinct process regimes, wherein the relative influence of input variables undergoes notable shifts. This phenomenon is experimentally validated through Arrhenius plot analysis, affirming the efficacy of our approach. Secondly, we demonstrate the development of an efficient surrogate model, based on Polynomial Chaos Expansion (PCE), that maintains accuracy, facilitating streamlined computational analyses. Finally, as a result of PCE, sensitivity analysis is made possible by means of Sobol' indices, that quantify the impact of process inputs across identified regimes. The insights gained from our analysis contribute to the formulation of hypotheses regarding phenomena occurring beyond the transition regime. Notably, the significance of temperature even in the diffusion-limited regime, as evidenced by the Arrhenius plot, suggests activation of gas phase reactions at elevated temperatures. Importantly, our proposed methods yield insights that align with experimental observations and theoretical principles, aiding decision-making in process design and optimization. By circumventing the need for costly and time-consuming experiments, our approach offers a pragmatic pathway towards enhanced process efficiency. Moreover, this study underscores the potential of data-driven computational methods for innovating reactor design paradigms.
Design and Implementation of DC-DC Buck Converter based on Deep Neural Network Sliding Mode Control
Authors: Liu Zhiwei, Yu Wangbing
Date: 2024-05-24 00:00:00

In order to address the challenge of traditional sliding mode controllers struggling to balance between suppressing system jitter and accelerating convergence speed, a deep neural network (DNN)-based sliding mode control strategy is proposed in this paper. The strategy achieves dynamic adjustment of parameters by modelling and learning the system through deep neural networks, which suppresses the system jitter while ensuring the convergence speed of the system. To demonstrate the stability of the system, a Lyapunov function is designed to prove the stability of the mathematical model of the DNN-based sliding mode control strategy for DC-DC buck switching power supply. We adopt a double closed-loop control mode to combine the sliding mode control of the voltage inner loop with the PI control of the current outer loop. Simultaneously, The DNN performance is evaluated through simulation and hardware experiments and compared with conventional control methods. The results demonstrate that the sliding mode controller based on the DNN exhibits faster system convergence speed, enhanced jitter suppression capability, and greater robustness.
An input-output continuous-time version of Willems' lemma
Authors: Victor G. Lopez, Matthias A. Müller, Paolo Rapisarda
Date: 2024-05-24 00:00:00
Tasks: LEMMA

We illustrate a novel version of Willems' lemma for data-based representation of continuous-time systems. The main novelties compared to previous works are two. First, the proposed framework relies only on measured input-output trajectories from the system and no internal (state) information is required. Second, our system representation makes use of exact system trajectories, without resorting to orthogonal bases representations and consequent approximations. We first establish sufficient and necessary conditions for data-based generation of system trajectories in terms of suitable latent variables. Subsequently, we reformulate these conditions using measured input-output data and show how to span the full behavior of the system. Furthermore, we show how to use the developed framework to solve the data-based continuous-time simulation problem.
An Audio-Based Iterative Controller for Soft Landing of Electromechanical Relays
Authors: Eloy Serrano-Seco, Edgar Ramirez-Laboreo, Eduardo Moya-Lasheras, Carlos Sagues
Date: 2024-05-24 00:00:00

Electromechanical relays and contactors suffer from strong collisions at the end of the switching operations. This causes several undesirable phenomena, such as clicking, mechanical wear and contact bounce. Thus, there is great interest in mitigating these switching impacts while keeping the advantageous features of these devices. This paper proposes a complete control strategy for soft landing. The control structure includes three main components. The first one is a real-time flux-tracking feedback controller, which presents several advantages over voltage or current control. The second one is a feedforward controller, which computes the flux reference signal based on a proposed dynamical model and the desired position trajectory for the switching operations. Lastly, the third control component is a learning-type run-to-run adaptation law that iteratively adapts the model parameters based on an audio signal. It exploits the repetitive nature of these devices in order to circumvent modeling discrepancies due to unit-to-unit variability or small changes between operations. The effectiveness of the proposed control is demonstrated through various experiments.