pwc-archive/papers-with-abstracts huggingface dataset:
Offline Uncertainty Sampling in Data-driven Stochastic MPC
Authors: Johannes Teutsch, Sebastian Kerz, Tim Brüdigam, Dirk Wollherr, Marion Leibold
Date: 2023-04-06 00:00:00

In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-parametric system representation and does not require any prior model identification. The approximation of chance constraints using uncertainty sampling leads to efficient constraint tightening. Under mild assumptions, robust recursive feasibility and closed-loop constraint satisfaction is shown. In a simulation example, we provide evidence for the improved control performance of the proposed control scheme in comparison to a purely robust data-driven predictive control approach.
Generalized uncertain Nash games: Reformulation and robust equilibrium seeking -- Extended version
Authors: Marta Fochesato, Filippo Fabiani, John Lygeros
Date: 2023-04-06 00:00:00

We consider generalized Nash equilibrium problems (GNEPs) with linear coupling constraints affected by both local (i.e., agent-wise) and global (i.e., shared resources) disturbances taking values in polyhedral uncertainty sets. By making use of traditional tools borrowed from robust optimization, for this class of problems we derive a tractable, finite-dimensional reformulation leading to a deterministic ''extended game'', and we show that this latter still amounts to a GNEP featuring generalized Nash equilibria ''in the worst-case''. We then design a fully-distributed, accelerated algorithm based on monotone operator theory, which enjoys convergence towards a Nash equilibrium of the original, uncertain game under weak structural assumptions. Finally, we illustrate the effectiveness of the proposed distributed scheme through numerical simulations
Coverage Analysis and Trajectory Optimization for Aerial Users with Dedicated Cellular Infrastructure
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-04-06 00:00:00

In this paper, we consider a novel cellular network for aerial users, which is composed of dedicated base stations (BSs), whose antennas are directed towards aerial users, and traditional terrestrial BSs (TBSs). Besides, the dedicated BSs are deployed on roadside furniture, such as lampposts and traffic lights, to achieve multiple features while occupying less space. Therefore, the locations of dedicated BSs and TBSs are modeled by a Poisson-line-Cox-process (PLCP) and Poisson point process (PPP), respectively. For the proposed network, we first compute the aerial coverage probability and show that the deployment of dedicated BSs improves the coverage probability in both high dense areas and rural areas. We then consider a cellular-connected UAV that has a flying mission and optimize its trajectory to maximize the minimal achievable signal-to-interference-plus-noise ratio (SINR) (Max-Min SINR). To obtain the Max-Min SINR and minimal time trajectory that satisfies the Max-Min SINR, we proposed two algorithms that are practical in large-scale networks. Finally, our results show that the optimal density of dedicated BSs which maximizes Max-Min SINR decreases with the increase of the road densities.
Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems
Authors: Matthias Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2023-04-06 00:00:00
Tasks: Model Predictive Control

We consider multi-agent systems with heterogeneous, nonlinear agents subject to individual constraints that want to achieve a periodic, dynamic cooperative control goal which can be characterised by a set and a suitable cost. We propose a sequential distributed model predictive control (MPC) scheme in which agents sequentially solve an individual optimisation problem to track an artificial periodic output trajectory. The optimisation problems are coupled through these artificial periodic output trajectories, which are communicated and penalised using the cost that characterises the cooperative goal. The agents communicate only their artificial trajectories and only once per time step. We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it. We illustrate the scheme with a simulation example.
Angular Stability Analysis of Parallel Connected Grid-following PV Inverters
Authors: Ramkrishna Mishan
Date: 2023-04-06 00:00:00

High penetration of distributed generators (DG) in modern power grids creates angle, voltage, and frequency instabilities. Most of the work in the literature has focused on small-signal stability analysis of single grid-connected inverters without thoroughly investigating their transient stability for large disturbances and interactions between parallel-connected inverters. To address these challenges, this paper examines the transient angular stability of a cluster of grid-following current source inverters. In a low inertia weak grid environment, grid-following inverters may lose synchronism due to faults and large-signal disturbances. A comparison of the voltage injection angle for pre-fault, fault-on trajectory, and post-fault conditions is performed, and an evaluation of the critical fault clearing time is directly related to the uniformity of the aggregated grid-following inverters. The results show that some inverters lose synchronism more quickly than others due to considerable variances in line impedances and apparent power values. Additionally, inverters with high apparent power are more susceptible to losing synchronism due to the maximum current limit, causing the voltage at the point of common coupling to decrease.
Enhanced Grid Following Inverter (E-GFL): A Unified Control Framework for Stiff and Weak Grids
Authors: Alireza Askarian, Jaesang Park, Srinivasa Salapaka
Date: 2023-04-05 00:00:00

This paper presents an extensive framework focused on the control design, along with stability and performance analysis, of Grid-Following Inverters (GFL). It aims to ensure their effective operation under both stiff and weak grid conditions. The proposed framework leverages the coupled algebraic structure of the transmission line dynamics in the $dq$ frame to express and then mitigate the effect of coupled dynamics on the GFL inverter's stability and performance. Additionally, we simplify the coupled multi-input, multi-output (MIMO) closed-loop system of the GFL into two separate single-input, single-output (2-SISO) closed-loops for easier analysis and control design. We present the stability, robust stability, and performance of the original GFL MIMO closed-loop system through our proposed 2-SISO closed-loop framework. This approach simplifies both the control design and its analysis. Our framework effectively achieves grid synchronization and active damping of filter resonance via feedback control. This eliminates the need for separate phase-locked loop (PLL) and virtual impedance subsystems. We also utilize the Bode sensitivity integral to define the limits of GFL closed-loop stability margin and performance. These fundamental limits reveal the necessary trade-offs between various performance goals, including reference tracking, closed-loop bandwidth, robust synchronization, and the ability to withstand grid disturbances. Finally, we demonstrate the merits of our proposed framework through detailed simulations and experiments. These showcase its effectiveness in handling challenging scenarios, such as asymmetric grid faults, low voltage operation, and the balance between harmonic rejection and resonance suppression.
A Robust Observer with Gyroscopic Bias Correction for Rotational Dynamics
Authors: Erjen Lefeber, Marcus Greiff, Anders Robertsson
Date: 2023-04-05 00:00:00

We propose an observer for rotational dynamics subject to directional and gyroscopic measurements, which simultaneously estimates the gyroscopic biases and attitude rates. We show uniform almost global asymptotic and local exponential stability of the resulting error dynamics, implying robustness against bounded disturbances. This robustness is quantified with respect to a popular nonlinear complementary filter in quantitative simulation studies, and we explore how the measurement noise propagates to the asymptotic errors as a function of tuning. This is an extended version of a paper with the same title (to appear at IFAC WC 2023). Additional mathematical details are provided in this extended version.
On Continuous Full-Order Integral-Terminal Sliding Mode Control with Unknown A Priori Bound on Uncertainty
Authors: Jit Koley, Dinesh Patra, Binoy Krishna Roy
Date: 2023-04-05 00:00:00

This study aims at providing a solution to the problem of designing a continuous and finite-time control for a class of nonlinear systems in the presence of matched uncertainty with an unknown apriori bound. First, we propose a Full-Order Integral-Terminal Sliding Manifold (FOITSM) with a conventional (discontinuous) sliding mode to show that it provides the combined attributes of the nonsingular terminal and integral sliding mode algorithms. Secondly, an Adaptive Disturbance Observer (ADO) has been designed to alleviate the effect of the uncertainty acting on the system. On application of the ADO-based Full-Order Integral-Terminal Sliding Mode Control (FOITSMC), the chattering phenomenon in control input has been reduced substantially in the presence of conditionally known matched disturbances. Moreover, the adaptive gains of ADO are updated non-monotonically without over-bounding the acting disturbance, yet sustain the global boundedness of state trajectories within a specific bound. %Finally, an application of the proposed algorithm for attitude stabilization of a rigid spacecraft has been successively shown.
STRV -- A radiation hard RISC-V microprocessor for high-energy physics applications
Authors: Alexander Walsemann, Michael Karagounis, Alexander Stanitzki, Dietmar Tutsch
Date: 2023-04-05 00:00:00

While microprocessors are used in various applications, they are precluded from the use in high-energy physics applications due to the harsh radiation present. To overcome this limitation a microprocessor design must withstand high doses of radiation and mitigate radiation induced soft errors. A TMR protection scheme is applied to protect a RISC-V microprocessor core against these faults. The protection of the integrated SRAM by an independent scrubbing algorithm is discussed. Initial irradiation results and power consumption measurements of the radiation-resistant RISC-V microprocessor implemented in 65 nm CMOS technology are presented.
Robust Performance Analysis for Time-Varying Multi-Agent Systems with Stochastic Packet Loss
Authors: Christian Hespe, Herbert Werner
Date: 2023-04-05 00:00:00

Recently, a scalable approach to system analysis and controller synthesis for homogeneous multi-agent systems with Bernoulli distributed packet loss has been proposed. As a key result of that line of work, it was shown how to obtain upper bounds on the $H_2$-norm that are robust with respect to uncertain interconnection topologies. The main contribution of the current paper is to show that the same upper bounds hold not only for uncertain but also time-varying topologies that are superimposed with the stochastic packet loss. Because the results are formulated in terms of linear matrix inequalities that are independent of the number of agents, multi-agent systems of any size can be analysed efficiently. The applicability of the approach is demonstrated on a numerical first-order consensus example, on which the obtained upper bounds are compared to estimates from Monte-Carlo simulations.
Network-Aware Electric Vehicle Coordination for Vehicle-to-Anything Value Stacking Considering Uncertainties
Authors: Canchen Jiang, Ariel Liebman, Hao Wang
Date: 2023-04-05 00:00:00
Tasks: energy trading

The increased adoption of electric vehicles (EVs) has led to the development of vehicle-to-anything (V2X) technologies, including vehicle-to-home (V2H), vehicle-to-grid (V2G), and energy trading of EVs in the local grid. The EV coordination can provide value to the grid and generate benefits for EVs. However, network constraints and uncertainties in renewable energy and demand pose significant challenges to EV coordination and restrict the realization of these benefits. This paper develops a rolling-horizon optimization problem for V2X value stacking to fully unlock the value of EV coordination, considering power network constraints (such as voltage limits) and uncertainties in the energy system. By coordinating EVs to perform V2H, V2G, and energy trading, our approach exploits the most valuable services in real-time. We also analyze the expected extra costs caused by the prediction errors to evaluate the impact of uncertainties on V2X value stacking. We validate our value-stacking model using real data from Australia's National Electricity Market (NEM), ISO New England (ISO-NE), and New York ISO (NY-ISO) in the US. The results show that V2X value stacking achieves significant benefits to EVs through energy cost reduction. The uncertainty in the load has a higher impact on the value-stacking performance than PV generation, indicating the importance of load prediction.
Direct data-driven control with signal temporal logic specifications
Authors: Birgit C. van Huijgevoort, Chris Verhoek, Roland Tóth, Sofie Haesaert
Date: 2023-04-05 00:00:00

Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task. In this work, we develop a direct data-driven control synthesis method for temporal logic specifications, which does not require this explicit modeling step, capable of providing certificates for the general class of linear systems. After collecting a single sequence of input-output data from the system, we construct a data-driven characterization of the behavior. Using this characterization, we synthesize a controller, such that the controlled system satisfies a (possibly unbounded) temporal logic specification. The underlying optimization problem is solved by mixed-integer linear programming. We demonstrate the applicability of the results through simulation examples.
Fairness-Aware Optimization of Vehicle-to-Vehicle Interaction for Smart EV Charging Coordination
Authors: Aditya Khele, Canchen Jiang, Hao Wang
Date: 2023-04-05 00:00:00
Tasks: Fairness

As the number of electric vehicles (EVs) continues to grow, there is an increasing need for smart charging strategies. This paper exploits the vehicle-to-vehicle (V2V) concept to leverage EVs' diverse charging patterns and unlock the value of flexibility by enabling energy transfer among EVs. We formulate a cost minimization problem for an EV charging station to optimize the V2V schedule together with vehicle-to-grid (V2G), grid-to-vehicle (G2V) charging, as well as the use of renewable energy. When EVs perform V2V to transfer energy to charge EVs, the traditional cost-minimizing approach may overuse some EVs with lower costs to perform V2V. We address the fairness issue by developing fair V2V energy transfer strategies to avoid the excess discharge from individual EVs. We introduced three kinds of fairness metrics in the V2V optimization problem to demonstrate the fair energy transfers. In addition, we formulate baseline optimization problems without V2G or V2V and compare the results to prove the potency of V2V concept along with V2G method. The simulation results demonstrate that V2V can significantly reduce EV charging costs and highlight the trade-off between fairness enforcement and charging cost minimization.
Risk-based Security Measure Allocation Against Injection Attacks on Actuators
Authors: Sribalaji C. Anand, André M. H. Teixeira
Date: 2023-04-04 00:00:00

This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact is a random variable, due to system uncertainty, we use Conditional Value-at-Risk (CVaR) to characterize the risk associated with the attack. We then consider the problem of allocating the security measures which minimize the risk. We assume that there are only a limited number of security measures available. Under this constraint, we observe that the allocation problem is a mixed-integer optimization problem. Thus we use relaxation techniques to approximate the security allocation problem into a Semi-Definite Program (SDP). We also compare our allocation method $(i)$ across different risk measures: the worst-case measure, the average (nominal) measure, and $(ii)$ across different search algorithms: the exhaustive and the greedy search algorithms. We depict the efficacy of our approach through numerical examples.
A Two-Phase Model of Early Atherosclerotic Plaque Development with LDL Toxicity Effects
Authors: Abdush Salam Pramanik, Bibaswan Dey, G. P. Raja Sekhar
Date: 2023-04-05 00:00:00
Tasks: Vocal Bursts Valence Prediction

Atherosclerosis is a chronic inflammatory cardiovascular disease in which fatty plaque is built inside an artery wall. Early atherosclerotic plaque development is typically characterized by inflammatory tissues primarily consisting of foam cells and macrophages. We present a biphasic model that explores early plaque growth to emphasize the role of cytokines (particularly, Monocyte Chemoattractant Protein-1) and oxidized low-density lipoprotein (oxLDL) in monocyte recruitment and foam cell production, respectively. The plaque boundary is assumed to move at the same speed as the inflammatory tissues close to the periphery. This study discusses the oxLDL cholesterols recruitment inside intima and their internalization by the inflammatory cells. Excessive intracellular cholesterol accumulation becomes toxic to macrophage foam cells, leading to cell death beyond a threshold. Our findings reveal that initially, the plaque evolves rapidly, and the growth rate eventually reduces because of the cholesterol-induced toxicity. The present study manifests that higher oxLDL cholesterol flux reduces plaque growth rate, while elevated cytokines flux promotes the corresponding plaque growth behaviour. Between oxLDL cholesterol and intracellular cholesterol, the second one is much more effective towards the growth of inflammatory tissue. The cholesterol-based toxicity-induced cell death parameters are crucial in flattening the plaque growth profile. A detailed analysis of the model presented in this article provides critical insights into the various biochemical and cellular mechanisms behind early plaque development.
Phase transitions in the Prisoner's Dilemma game on scale-free networks
Authors: Jacek Miękisz, Javad Mohamadichamgavi, Jakub Łącki
Date: 2023-04-06 00:00:00

We study stochastic dynamics of the Prisoner's Dilemma game on random Erd\"{o}s-R\'{e}nyi and Barab\'{a}si-Albert networks with a cost of maintaining a link between interacting players. Stochastic simulations show that when the cost increases, the population of players located on Barab\'{a}si-Albert network undergoes a sharp transition from an ordered state, where almost all players cooperate, to a state in which both cooperators and defectors coexist. At the critical cost, the population oscillates in time between these two states. Such a situation is not present in the Erd\"{o}s-R\'{e}nyi network. We provide some heuristic analytical arguments for the phase transition and the value of the critical cost in the Barab\'{a}si-Albert network.
Sharp Bounds in the Latent Index Selection Model
Authors: Philip Marx
Date: 2020-12-04 00:00:00
Tasks: model

A fundamental question underlying the literature on partial identification is: what can we learn about parameters that are relevant for policy but not necessarily point-identified by the exogenous variation we observe? This paper provides an answer in terms of sharp, analytic characterizations and bounds for an important class of policy-relevant treatment effects, consisting of marginal treatment effects and linear functionals thereof, in the latent index selection model as formalized in Vytlacil (2002). The sharp bounds use the full content of identified marginal distributions, and analytic derivations rely on the theory of stochastic orders. The proposed methods also make it possible to sharply incorporate new auxiliary assumptions on distributions into the latent index selection framework. Empirically, I apply the methods to study the effects of Medicaid on emergency room utilization in the Oregon Health Insurance Experiment, showing that the predictions from extrapolations based on a distribution assumption (rank similarity) differ substantively and consistently from existing extrapolations based on a parametric mean assumption (linearity). This underscores the value of utilizing the model's full empirical content in combination with auxiliary assumptions.
Faster estimation of dynamic discrete choice models using index invertibility
Authors: Jackson Bunting, Takuya Ura
Date: 2023-04-05 00:00:00
Tasks: Discrete Choice Models

Many estimators of dynamic discrete choice models with persistent unobserved heterogeneity have desirable statistical properties but are computationally intensive. In this paper we propose a method to quicken estimation for a broad class of dynamic discrete choice problems by exploiting semiparametric index restrictions. Specifically, we propose an estimator for models whose reduced form parameters are invertible functions of one or more linear indices (Ahn, Ichimura, Powell and Ruud 2018), a property we term index invertibility. We establish that index invertibility implies a set of equality constraints on the model parameters. Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains. Our main result shows that the proposed estimator is asymptotically equivalent to the unconstrained, computationally heavy estimator. In addition, we provide a series of results on the number of independent index restrictions on the model parameters, providing theoretical guidance on the extent of computational gains. Finally, we demonstrate the advantages of our approach via Monte Carlo simulations.