pwc-archive/papers-with-abstracts huggingface dataset:
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models
Authors: Defne E. Ozan, Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Date: 2022-03-30 00:00:00
Tasks: parameter estimation

Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories. Systems controlled in a closed loop along a periodic trajectory can also be modelled as systems experiencing limit cycle behavior. The goal of this work is to identify from data, the local dynamics around the limit cycle using linear periodically parameter-varying models. Using a coordinate transformation onto transversal surfaces, the dynamics are decomposed into two parts: one along the limit cycle, and one on the transversal surfaces. Then, the model is identified from trajectory data using kernel-based methods with a periodic kernel design. The kernel-based model is extended to also account for variations in system parameters associated with different operating conditions. The performance of the proposed identification method is demonstrated on a benchmark nonlinear system and on a simplified airborne wind energy model. The method provides accurate model parameter estimation, compared to the analytical linearization, and good prediction capability.
Analog cross coupled controller for oscillations: modeling and design via dominant system theory
Authors: Weiming Che, Thomas Chaffey, Fulvio Forni
Date: 2022-03-30 00:00:00

We propose a new analog feedback controller based on the classical cross coupled electronic oscillator. The goal is to drive a linear passive plant into oscillations. We model the circuit as Lur'e system and we derive a new graphical condition to certify oscillations (inverse circle criterion for dominance theory). These conditions are then specialized to minimal control architectures like RLC and RC networks, and are illustrated with an example based on a DC motor model.
Online Monitoring of Dynamic Systems for Signal Temporal Logic Specifications with Model Information
Authors: Xinyi Yu, Weijie Dong, Xiang Yin, ShaoYuan Li
Date: 2022-03-30 00:00:00

Online monitoring aims to evaluate or to predict, at runtime, whether or not the behaviors of a system satisfy some desired specification. It plays a key role in safety-critical cyber-physical systems. In this work, we propose a new model-based approach for online monitoring for specifications described by Signal Temporal Logic (STL) formulae. Specifically, we assume that the observed state traces are generated by an underlying dynamic system whose model is known. The main idea is to consider the dynamic of the system when evaluating the satisfaction of the STL formulae. To this end, effective approaches for the computation of feasible sets for STL formulae are provided. We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used. We also demonstrate the proposed monitoring algorithm by case studies.
An Efficiency-Based Power Management Strategy for an Isolated Microgrid Project
Authors: Francesco Conte, Fabio D'Agostino, Samuele Grillo, Gabriele Mosaico, Federico Silvestro
Date: 2022-03-30 00:00:00
Tasks: Management

The microgrids design for remote locations represents one of the most important and critical applications of the microgrid concept. It requires the correct sizing and the proper utilization of the different sources to guarantee the economical feasibility and the reliability of the supply. This study illustrates an efficiency-based power management strategy, designed for an undergoing microgrid project, where the sizing process of the resources (diesel generators, battery energy storage system, and PV plant) is obtained using a mixed-integer optimization algorithm. The proposed power management strategy guarantees the efficient exploitation of the power sources, which is one of the key elements of the optimal sizing process, being naturally included in the definition of the energy cost functions. The effectiveness of the power control strategy is validated by means of quasi-dynamic simulations on the complete microgrid model, where sources are defined by the optimal problem solution, while the cabling (size and length) and the main switchboards location reflect the expected system layout. Results obtained from the simulation of the microgrid electrical system include losses, and allow to verify and to highlight the desired quantities, such as the quality of supply at each busbar (voltage magnitude), and the state of charge of the energy storage system.
Kalman Filter Design for Intermittent Optical Wireless Communication Systems on Time Scales
Authors: WenQi Cai, Bacem Ben Nasser, Mohamed Djemai, Taous Meriem Laleg-Kirati
Date: 2022-03-30 00:00:00
Tasks: State Estimation

Time-scale theory, due to its ability to unify the continuous and discrete cases, allows handling intractable non-uniform measurements, such as intermittent received signals. In this work, we address the state estimation problem of a vibration-induced intermittent optical wireless communication (OWC) system by designing a Kalman filter on time scales. First, the algorithm of the time-scale Kalman filter is introduced and a numerical example is given for illustration. Then the studied intermittent OWC system is presented, and experimental data are collected to determine the time scale's form, which has bounded graininess (a.k.a, bounded time jumps). Finally, we design a Kalman filter on the previously defined time scale for the intermittent OWC system and critically analyzed its estimation performance. Moreover, the obtained conclusions are further validated on a reference system. The simulation results corroborate that the time-scale Kalman filtering technique is considerably promising to solve the state estimation problem with non-uniform measurements. This study reveals for the first time the feasibility of applying the time-scale Kalman filter theory to practical applications.
Kron-based Model-order Reduction of Open Mass-action Kinetics Chemical Reaction Networks
Authors: Mohamad Agung Prawira Negara, Azka Muji Burohman, Bayu Jayawardhana
Date: 2022-03-30 00:00:00

We propose a Kron-based model-order reduction method for mass-action kinetics chemical reaction networks (CRN) with constant inflow and proportional outflow. The reduced-order models preserve the CRN structure and we establish that the resulting reduced-order models have the same DC-gain or zero-moment as that of the full-order ones. Subsequently, we present the spectrum interlacing property of the Kron-reduced open CRN and propose the use of Gramians-based approach for single-species single-substrate chemical network to get the upper-bound of approximation error and to use it in determining a good set of nodes to be removed systematically. Finally, we evaluate the applicability and efficacy of our results in two well-known biochemical kinetic models: the activated sludge model (ASM) 1 and McKeithan's T-cell receptor model.
Optimal Energy Management of Series Hybrid Electric Vehicles with Engine Start-Stop System
Authors: Boli Chen, Xiao Pan, Simos A. Evangelou
Date: 2022-03-30 00:00:00
Tasks: energy management, Management

This paper develops energy management (EM) control for series hybrid electric vehicles (HEVs) that include an engine start-stop system (SSS). The objective of the control is to optimally split the energy between the sources of the powertrain and achieve fuel consumption minimization. In contrast to existing works, a fuel penalty is used to characterize more realistically SSS engine restarts, to enable more realistic design and testing of control algorithms. The paper first derives two important analytic results: a) analytic EM optimal solutions of fundamental and commonly used series HEV frameworks, and b) proof of optimality of charge sustaining operation in series HEVs. It then proposes a novel heuristic control strategy, the hysteresis power threshold strategy (HPTS), by amalgamating simple and effective control rules extracted from the suite of derived analytic EM optimal solutions. The decision parameters of the control strategy are small in number and freely tunable. The overall control performance can be fully optimized for different HEV parameters and driving cycles by a systematic tuning process, while also targeting charge sustaining operation. The performance of HPTS is evaluated and benchmarked against existing methodologies, including dynamic programming (DP) and a recently proposed state-of-the-art heuristic strategy. The results show the effectiveness and robustness of the HPTS and also indicate its potential to be used as the benchmark strategy for high fidelity HEV models, where DP is no longer applicable due to computational complexity.
A simple suboptimal moving horizon estimation scheme with guaranteed robust stability
Authors: Julian D. Schiller, Boyang Wu, Matthias A. Müller
Date: 2022-03-30 00:00:00
Tasks: valid

We propose a suboptimal moving horizon estimation (MHE) scheme for a general class of nonlinear systems. To this end, we consider an MHE formulation that optimizes over the trajectory of a robustly stable observer. Assuming that the observer admits a Lyapunov function, we show that this function is an M-step Lyapunov function for suboptimal MHE. The presented sufficient conditions can be easily verified in practice. We illustrate the practicability of the proposed suboptimal MHE scheme with a standard nonlinear benchmark example. Here, performing a single iteration is sufficient to significantly improve the observer's estimation results under valid theoretical guarantees.
Event-triggered Observability: A Set-membership Perspective
Authors: Kaikai Zheng, Dawei Shi, Tongwen Chen
Date: 2022-03-30 00:00:00

This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements. A new notion of observability, namely, $\epsilon$-observability is defined with parameter $\epsilon$, which relates to the worst-case performance of inferring the initial state based on not only the received measurement but also the implicit information in the event-triggering conditions at no-event instants. A criterion is developed to test the proposed $\epsilon$-observability of discrete-time linear systems, based on which an iterative event-triggered set-membership observer is designed to evaluate a set containing all possible values of the state. The proposed set-membership observer is designed as the outer approximation of the ellipsoids predicted based on previous state estimates and the ellipsoids inferred by fusing the received measurement and communication conditions, which is optimal in the sense of trace at each step and is proved to be asymptotically bounded. The efficiency of the proposed event-triggered set-membership state observer is verified by numerical experiments.
Current Implicit Policies May Not Eradicate COVID-19
Authors: Ali Jadbabaie, Arnab Sarker, Devavrat Shah
Date: 2022-03-29 00:00:00

Successful predictive modeling of epidemics requires an understanding of the implicit feedback control strategies which are implemented by populations to modulate the spread of contagion. While this task of capturing endogenous behavior can be achieved through intricate modeling assumptions, we find that a population's reaction to case counts can be described through a second order affine dynamical system with linear control which fits well to the data across different regions and times throughout the COVID-19 pandemic. The model fits the data well both in and out of sample across the 50 states of the United States, with comparable $R^2$ scores to state of the art ensemble predictions. In contrast to recent models of epidemics, rather than assuming that individuals directly control the contact rate which governs the spread of disease, we assume that individuals control the rate at which they vary their number of interactions, i.e. they control the derivative of the contact rate. We propose an implicit feedback law for this control input and verify that it correlates with policies taken throughout the pandemic. A key takeaway of the dynamical model is that the "stable" point of case counts is non-zero, i.e. COVID-19 will not be eradicated under the current collection of policies and strategies, and additional policies are needed to fully eradicate it quickly. Hence, we suggest alternative implicit policies which focus on making interventions (such as vaccinations and mobility restrictions) a function of cumulative case counts, for which our results suggest a better possibility of eradicating COVID-19.
Towards a Hybrid RF/Optical Lunar Communication System (LunarComm)
Authors: Waseem Raza, Ethan Abele, John OHara, Behnaz Sadr, Peter LoPresti, Ali Imran, Wooyeol Choi, Ickhyun Song, Serhat Altunc, Obadiah Kegege, Sabit Ekin
Date: 2022-03-29 00:00:00

The prospect of mankind returning to the Moon has garnered a great amount of attention in recent years. Dozens of lunar missions are planned for the coming decade which will require the development of a sustainable communication infrastructure with high data rates and minimal latency. Space communication systems thus far have relied on Radio Frequency (RF) links alone, but recent developments in laser communications have demonstrated that Free Space Optical (FSO) links can achieve much higher data rates. Upon considering the respective benefits and drawbacks of RF and FSO links, we make a case for the integration of these two technologies into a hybrid RF/FSO lunar communications architecture which leverages small satellites in a Low Earth Orbit (LEO) constellation. We include a case study for this technology designed in Analytical Graphics Systems Tool Kit (STK) software. Results are presented in terms of chain access duration, propagation delay, transmission loss, Signal-to-Noise Ratio (SNR), and Bit Error Rate (BER). This architecture shows potential to revolutionize extraterrestrial communications and pave the way for highly ambitious future missions in space.
Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions
Authors: Andrew Singletary, Mohamadreza Ahmadi, Aaron D. Ames
Date: 2022-03-29 00:00:00

Guaranteeing safety for robotic and autonomous systems in real-world environments is a challenging task that requires the mitigation of stochastic uncertainties. Control barrier functions have, in recent years, been widely used for enforcing safety related set-theoretic properties, such as forward invariance and reachability, of nonlinear dynamical systems. In this paper, we extend this rich framework to nonlinear discrete-time systems subject to stochastic uncertainty and propose a framework for assuring risk-sensitive safety in terms of coherent risk measures. To this end, we introduce risk control barrier functions (RCBFs), which are compositions of barrier functions and dynamic, coherent risk measures. We show that the existence of such barrier functions implies invariance in a coherent risk sense. Furthermore, we formulate conditions based on finite-time RCBFs to guarantee finite-time reachability to a desired set in the coherent risk. Conditions for risk-sensitive safety and finite-time reachability of sets composed of Boolean compositions of multiple RCBF are also formulated. We show the efficacy of the proposed method through its application to a cart-pole system in a safety-critical scenario.
Protective Mission against a Highly Maneuverable Rogue Drone Using Defense Margin Strategy
Authors: Minjun Sung, Christophe Johannes Hiltebrandt-McIntosh, Hunmin Kim, Naira Hovakimyan
Date: 2022-03-29 00:00:00

The current paper studies a protective mission to defend a domain called the safe zone from a rogue drone invasion. We consider a one attacker and one defender drone scenario where only a noisy observation of the attacker at every time step is accessible to the defender. Directly applying strategies used in existing problems such as pursuit-evasion games are shown to be insufficient for our mission. We introduce a new concept of defense margin to complement an existing strategy and construct a control strategy that successfully solves our problem. We provide analytical proofs to point out the limitations of the existing strategy and how our defense margin strategy can be used to enhance performance. Simulation results show that our suggested strategy outperforms that of the existing strategy at least by 36.0 percentage points in terms of mission success.
Fault Detection and Isolation of Uncertain Nonlinear Parabolic PDE Systems
Authors: Jingting Zhang, Chengzhi Yuan, Wei Zeng, Cong Wang
Date: 2022-03-29 00:00:00
Tasks: Decision Making, Fault Detection

This paper proposes a novel fault detection and isolation (FDI) scheme for distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with nonlinear uncertain dynamics. A key feature of the proposed FDI scheme is its capability of dealing with the effects of system uncertainties for accurate FDI. Specifically, an approximate ordinary differential equation (ODE) system is first derived to capture the dominant dynamics of the original PDE system. An adaptive dynamics identification approach using radial basis function neural network is then proposed based on this ODE system, so as to achieve locally-accurate identification of the uncertain system dynamics under normal and faulty modes. A bank of FDI estimators with associated adaptive thresholds are finally designed for real-time FDI decision making. Rigorous analysis on the FDI performance in terms of fault detectability and isolatability is provided. Simulation study on a representative transport-reaction process is conducted to demonstrate the effectiveness and advantage of the proposed approach.
Incorporating Gradient Similarity for Robust Time Delay Estimation in Ultrasound Elastography
Authors: Md Ashikuzzaman, Timothy J. Hall, Hassan Rivaz
Date: 2022-03-30 00:00:00

Energy-based ultrasound elastography techniques minimize a regularized cost function consisting of data and continuity terms to obtain local displacement estimates based on the local time-delay estimation (TDE) between radio-frequency (RF) frames. The data term associated with the existing techniques takes only the amplitude similarity into account and hence is not sufficiently robust to the outlier samples present in the RF frames under consideration. This drawback creates noticeable artifacts in the strain image. To resolve this issue, we propose to formulate the data function as a linear combination of the amplitude and gradient similarity constraints. We estimate the adaptive weight concerning each similarity term following an iterative scheme. Finally, we optimize the non-linear cost function in an efficient manner to convert the problem to a sparse system of linear equations which are solved for millions of variables. We call our technique rGLUE: robust data term in GLobal Ultrasound Elastography. rGLUE has been validated using simulation, phantom, in vivo liver, and breast datasets. In all of our experiments, rGLUE substantially outperforms the recent elastography methods both visually and quantitatively. For simulated, phantom, and in vivo datasets, respectively, rGLUE achieves 107%, 18%, and 23% improvements of signal-to-noise ratio (SNR) and 61%, 19%, and 25% improvements of contrast-to-noise ratio (CNR) over GLUE, a recently-published elastography algorithm.
Testing the identification of causal effects in observational data
Authors: Martin Huber, Jannis Kueck
Date: 2022-03-29 00:00:00
Tasks: valid

This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument. Under a causal structure commonly found in empirical applications, the testable conditional independence of the suspected instrument and the outcome given the treatment and the covariates has two implications. First, the instrument is valid, i.e. it does not directly affect the outcome (other than through the treatment) and is unconfounded conditional on the covariates. Second, the treatment is unconfounded conditional on the covariates such that the treatment effect is identified. We suggest tests of this conditional independence based on machine learning methods that account for covariates in a data-driven way and investigate their asymptotic behavior and finite sample performance in a simulation study. We also apply our testing approach to evaluating the impact of fertility on female labor supply when using the sibling sex ratio of the first two children as supposed instrument, which by and large points to a violation of our testable implication for the moderate set of socio-economic covariates considered.
Labour by Design: Contributions of David Card, Joshua Angrist, and Guido Imbens
Authors: Peter Hull, Michal Kolesár, Christopher Walters
Date: 2022-03-30 00:00:00

The 2021 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to David Card "for his empirical contributions to labour economics" and to Joshua Angrist and Guido Imbens "for their methodological contributions to the analysis of causal relationships." We survey these contributions of the three laureates, and discuss how their empirical and methodological insights transformed the modern practice of applied microeconomics. By emphasizing research design and formalizing the causal content of different econometric procedures, the laureates shed new light on key questions in labour economics and advanced a robust toolkit for empirical analyses across many fields.
Some New Views on Product Space and Related Diversification
Authors: Önder Nomaler, Bart Verspagen
Date: 2022-03-30 00:00:00

We aim to contribute to the literature on product space and diversification by proposing a number of extensions of the current literature: (1) we propose that the alternative but related idea of a country space also has empirical and theoretical appeal; (2) we argue that the loss of comparative advantage should be an integral part of (testing the empirical relevance of) the product space idea; (3) we propose several new indicators for measuring relatedness in product space; and (4) we propose a non-parametric statistical test based on bootstrapping to test the empirical relevance of the product space idea.
Core and stability notions in many-to-one matching markets with indifferences
Authors: Agustín G. Bonifacio, Noelia Juarez, Pablo Neme, Jorge Oviedo
Date: 2022-03-30 00:00:00

In a many-to-one matchingmodel with responsive preferences in which indifferences are allowed, we study three notions of core, three notions of stability, and their relationships. We show that (i) the core contains the stable set, (ii) the strong core coincides with the strongly stable set, and (iii) the super core coincides with the super stable set. We also show how the core and the strong core in markets with indifferences relate to the stable matchings of their associated tie-breaking strict markets.
Put-Call Parities, absence of arbitrage opportunities and non-linear pricing rules
Authors: Lorenzo Bastianello, Alain Chateauneuf, Bernard Cornet
Date: 2022-03-30 00:00:00

If prices of assets traded in a financial market are determined by non-linear pricing rules, different versions of the Call-Put Parity have been considered. We show that, under monotonicity, parities between call and put options and discount certificates characterize ambiguity-sensitive (Choquet and/or Sipos) pricing rules, i.e., pricing rules that can be represented via discounted expectations with respect to non-additive probability measures. We analyze how non-additivity relates to arbitrage opportunities and we give necessary and sufficient conditions for Choquet and Sipos pricing rules to be arbitrage-free. Finally, we identify violations of the Call-Put Parity with the presence of bid-ask spreads.