pwc-archive/papers-with-abstracts huggingface dataset:
Bounded-error constrained state estimation of LTV systems in presence of sporadic measurements
Authors: Yasmina Becis-Aubry
Date: 2022-02-28 00:00:00
Tasks: State Estimation

This contribution proposes a recursive set-membership method for the ellipsoidal state characterization for discrete-time linear time-varying models with additive unknown disturbances vectors, bounded by possibly degenerate zonotopes and polytopes, impacting respectively, the state evolution equation and the sporadic measurement vectors, which are expressed as linear inequality and equality constraints on the state vector. New algorithms are designed considering the unprecedented fact that, due to equality constraints, the shape matrix of the ellipsoid characterizing all possible values of the state vector is non invertible. The two main size minimizing criteria (volume and sum of squared axes lengths) are examined in the time update step and also in the observation updating, in addition to a third one, minimizing some error norm and ensuring the input-to-state stability of the estimation error. The author's papers [1] and [2] were combined into this longer, more comprehensive version. It includes all the proofs and a few images and is meant to be a support for the reader. There is no introduction, no conclusion, and no application examples.
A System Level Approach to Regret Optimal Control
Authors: Alexandre Didier, Jerome Sieber, Melanie N. Zeilinger
Date: 2022-02-28 00:00:00

We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial initial conditions. The dynamic regret is defined as the difference between the true incurred cost of the system and the cost which could have optimally been achieved under any input sequence having full knowledge of all future disturbances for a given disturbance energy. This problem formulation can be seen as an alternative to classical $\mathcal{H}_2$- or $\mathcal{H}_\infty$-control. The proposed controller synthesis is based on the system level parametrisation, which allows reformulating the dynamic regret problem as a semi-definite problem. This yields a new framework that allows to consider structured dynamic regret problems, which have not yet been considered in the literature. For known pointwise ellipsoidal bounds on the disturbance, we show that the dynamic regret bound can be improved compared to using only a bounded energy assumption and that the optimal dynamic regret bound differs by at most a factor of $\frac{2}{\pi}$ from the computed solution. Furthermore, the proposed framework allows guaranteeing state and input constraint satisfaction.
Red Light, Green Light Game of Multi-Robot Systems with Safety Barrier Certificates
Authors: Yun Ho Choi, Doik Kim
Date: 2022-02-28 00:00:00
Tasks: Collision Avoidance

In this paper, we propose the safety barrier certificates for uncertain multi-robot systems playing red light, green light game. According to the rule of the game, the robots are allowed to move forward after a doll shouts `green light' and must stop when it shouts `red light'. Following this rule, a two-mode nominal controller is designed where one mode is for moving forward and the other one is for slowing down and being motionless. Then, multiple exponential control barrier functions(ECBFs) are developed to handle safety constraints for limited playground, collision avoidance, and saturation of the velocity. While designing the nominal controller and ECBFs, an estimated braking time and robust inequality constraints are derived to deal with the system uncertainty. Consequently, a controller guaranteeing safety barrier certificates of each robot has been formulated by a quadratic programming with the nominal controller and the robust inequality constraints. Finally, red light, green light game is simulated to validate the proposed safety-critical control system.
A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms
Authors: Xiaoying Tang, Chenxi Sun, Suzhi Bi, Shuoyao Wang, Angela Yingjun Zhang
Date: 2022-02-28 00:00:00
Tasks: energy trading, Scheduling

The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission. The fast development of EVs and charging facilities is driving the evolution of Internet of Vehicles (IoV) to Internet of Electric Vehicles (IoEV). IoEV benefits from both smart grid and Internet of Things (IoT) technologies which provide advanced bi-directional charging services and real-time data processing capability, respectively. The major design challenges of the IoEV charging control lie in the randomness of charging events and the mobility of EVs. In this article, we present a holistic review on advanced bi-directional EV charging control algorithms. For Grid-to-Vehicle (G2V), we introduce the charging control problem in two scenarios: 1) Operation of a single charging station and 2) Operation of multiple charging stations in coupled transportation and power networks. For Vehicle-to-Grid (V2G), we discuss how EVs can perform energy trading in the electricity market and provide ancillary services to the power grid. Besides, a case study is provided to illustrate the economic benefit of the joint optimization of routing and charging scheduling of multiple EVs in the IoEV. Last but not the least, we will highlight some open problems and future research directions of charging scheduling problems for IoEVs.
Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
Authors: Young-Jin Kim
Date: 2022-02-28 00:00:00

The attainment of carbon neutrality requires a research agenda that addresses the technical and economic challenges that will be encountered as we progress toward 100% renewable electricity generation. Increasing proportions of variable renewable energy (VRE) sources (such as wind turbines and photovoltaic systems) render the supply-and-demand balance of VRE-dominated power grids difficult. The operational characteristics and effects of VRE inverters also require attention. Here, we examine the implications of the paradigm shift to carbon neutrality and summarize the associated research challenges in terms of system planning, operation, and sta-bility, and the need for energy storage integration, demand-side participation, distributed con-trol and estimation, and energy sector coupling. We also highlight the existing literature gaps, and our recent studies that can fill in the gaps, thereby facilitating the improvement of grid op-eration and estimation. The numerical results of comparative case studies are also provided on the operational stability and economics of power grids with a high level of VRE sources, assist-ing stakeholders in establishing specific roadmaps and making relevant decisions.
Pursuit-evasion differential games of players with different speeds in spaces of different dimensions
Authors: Shuai Li, Chen Wang, Guangming Xie
Date: 2022-02-28 00:00:00

We study pursuit-evasion differential games between a faster pursuer moving in 3D space and an evader moving in a plane. We first extend the well-known Apollonius circle to 3D space, by which we construct the isochron for the considered two players. Then both cases with and without a static target are considered and the corresponding optimal strategies are derived using the concept of isochron. In order to guarantee the optimality of the proposed strategies, the value functions are given and are further proved to be the solution of Hamilton-Jacobi-Isaacs equation. Simulations with comparison between the proposed strategies and other classical strategies are carried out and the results show the optimality of the proposed strategies.
Robust Control of Partially Specified Boolean Networks
Authors: Luboš Brim, Samuel Pastva, David Šafránek, Eva Šmijáková
Date: 2022-02-27 00:00:00

Regulatory networks (RNs) are a well-accepted modelling formalism in computational systems biology. The control of RNs is currently receiving a lot of attention because it provides a computational basis for cell reprogramming -- an attractive technology developed in regenerative medicine. By solving the control problem, we learn which parts of a biological system should be perturbed to stabilise the system in the desired phenotype. We allow the specification of the Boolean model representing a given RN to be incomplete. To that end, we utilise the formalism of partially specified Boolean networks which covers every possible behaviour of unspecified parts of the system. Such an approach causes a significant state explosion. This problem is addressed by using symbolic methods to represent both the unspecified model parts and all possible perturbations of the system. Additionally, to make the control design efficient and practically applicable, the optimal control should be minimal in terms of size. Moreover, in a partially specified model, a control may achieve the desired stabilisation only for a subset of the possible fully specified model instantiations. To address these aspects, we utilise several quantitative measures. Apart from the size of perturbation, we also examine its robustness -- a portion of instantiations for which the control is applicable. We show that proposed symbolic methods solving the control problem for partially specified BNs are efficient and scale well. We also evaluate the robustness metrics in cases of all three studied control types. The robustness metric tells us how big a proportion of fully defined systems the given perturbation works. Our experiments support the hypothesis that one-step perturbations may be less robust than temporary or permanent perturbations. This is a full version of a paper that is submitted to a journal.
Thermal Modelling and Controller Design of an Alkaline Electrolysis System under Dynamic Operating Conditions
Authors: Ruomei Qi, Jiarong Li, Jin Lin, Yonghua Song, Jiepeng Wang, Qiangqiang Cui, Yiwei Qiu, Ming Tang, Jian Wang
Date: 2022-02-27 00:00:00
Tasks: Management

Thermal management is vital for the efficient and safe operation of alkaline electrolysis systems. Traditional alkaline electrolysis systems use simple proportional-integral-differentiation (PID) controllers to maintain the stack temperature near the rated value. However, in renewable-to-hydrogen scenarios, the stack temperature is disturbed by load fluctuations, and the temperature overshoot phenomenon occurs which can exceed the upper limit and harm the stack. This paper focuses on the thermal modelling and controller design of an alkaline electrolysis system under dynamic operating conditions. A control-oriented thermal model is established in the form of a third-order time-delay process, which is used for simulation and controller design. Based on this model, we propose two novel controllers to reduce temperature overshoot: one is a current feed-forward PID controller (PID-I), the other is a model predictive controller (MPC). Their performances are tested on a lab-scale system and the experimental results are satisfying: the temperature overshoot is reduced by 2.2 degree with the PID-I controller, and no obvious overshoot is observed with the MPC controller. Furthermore, the thermal dynamic performance of an MW-scale alkaline electrolysis system is analyzed by simulation, which shows that the temperature overshoot phenomenon is more general in large systems. The proposed method allows for higher temperature set points which can improve system efficiency by 1%.
Application of Event-Triggered Sliding Mode Control of 2-DOF Humanoid's Lower-Limb Powered by Series Elastic Actuator
Authors: Anh Khoa Lanh Luu, Van Tu Duong, Huy Hung Nguyen, Tan Tien Nguyen
Date: 2022-02-27 00:00:00

This paper proposes an event-triggered sliding mode control (SMC) scheme combined with a backstepping algorithm for control of 2-DOF humanoid's lower-limb powered by Series elastic actuator (SEA). First, the modelling process for the lower-limb system is implemented by using the Euler-Lagrange theory. With the obtained dynamical equations of lower-limb, the model of the SEA is achieved in both mechanical and electrical perspectives. Then, the event-triggered SMC approach is utilized to ensure the system's stability and eliminate the effect of bounded external disturbance. Next, some assumptions and designed thresholds for the tracking error are given, together with the proof for the convergence of the inter-event time. The backstepping algorithm is applied in the end to determine the needed input control voltage signal. Finally, the results of this research are demonstrated through some simulations in order to prove the effi-ciency and appropriation of this method.
A Simple Discretization Scheme for Gain Matrix Conditioning
Authors: Daniel L. O'Connor, Lim C. Siang, Shams Elnawawi
Date: 2022-02-27 00:00:00

In industrial model predictive controllers (MPCs), models generated from regression-based system identification methods typically contain small or even physically non-existent degrees of freedom. Control issues can arise when the steady-state optimizer uses these small degrees of freedom to calculate targets for plant operation due to matrix ill-conditioning. Mathematical techniques like Relative Gain Array (RGA) and Singular Value Decomposition (SVD) are helpful for analyzing controller gain interactions and identifying conditioning issues, which can be corrected relatively easily in small models. However, these techniques are difficult and tedious to apply for larger, more complex models. This paper describes a novel, non-iterative, RGA-based, binning technique for discretizing the gain matrix and quickly solving 2x2 conditioning issues for any model size, while guaranteeing gain adjustments below a certain threshold. Higher order interactions are also discussed.
A Feasibility Study on Real-Time High Resolution Imaging of the Brain Using Electrical Impedance Tomography
Authors: Sebastien Martin
Date: 2022-02-26 00:00:00

Objective: The strengths of Electrical Impedance Tomography (EIT) are its capability of imaging the internal body by using a noninvasive, radiation safe technique, and the absence of known hazards. In this paper we introduce a novel idea of using EIT in microelectrodes during Deep Brain Stimulation (DBS) surgery in order to obtain an image of the electrical conductivities of the brain tissues surrounding the microelectrodes. DBS is a surgical treatment involving the implantation of a medical probe inside the brain. For such application, the EIT reconstruction method has to offer both high quality and robustness against noise. Methods: A post-processing method for open-domain EIT is introduced in this paper, which combines linear and nonlinear methods in order to use the advantages of both with limited drawbacks. The reconstruction method is a two-steps method, the first solves the inverse problem with a linear algorithm and the second brings the nonlinear aspects back into the image. Results: The proposed method is tested on both simulation and phantom data, and compared to three widely used method for EIT imaging. Resulting images and errors gives a strong advantage to the proposed solution. Conclusion: High quality reconstruction from phantom data validates the efficiency of the novel reconstruction method. Significance: This feasibility study presents an efficient method for open domain EIT and opens the way to clinical trials.
What ODE-Approximation Schemes of Time-Delay Systems Reveal about Lyapunov-Krasovskii Functionals
Authors: Tessina H. Scholl, Veit Hagenmeyer, Lutz Gröll
Date: 2022-02-26 00:00:00
Tasks: Numerical Integration

The article proposes an approach to complete-type and related Lyapunov-Krasovskii functionals that neither requires knowledge of the delay-Lyapunov matrix function nor does it involve linear matrix inequalities. The approach is based on ordinary differential equations (ODEs) that approximate the time-delay system. The ODEs are derived via spectral methods, e.g., the Chebyshev collocation method (also called pseudospectral discretization) or the Legendre tau method. A core insight is that the Lyapunov-Krasovskii theorem resembles a theorem for Lyapunov-Rumyantsev partial stability in ODEs. For the linear approximating ODE, only a Lyapunov equation has to be solved to obtain a partial Lyapunov function. The latter approximates the Lyapunov-Krasovskii functional. Results are validated by applying Clenshaw-Curtis and Gauss quadrature to a semi-analytical result of the functional, yielding a comparable finite-dimensional approximation. In particular, the article provides a formula for a tight quadratic lower bound, which is important in applications. Examples confirm that this new bound is significantly less conservative than known results.
Parameter estimation and model reduction for retinal laser treatment
Authors: Manuel Schaller, Mitsuru Wilson, Viktoria Kleyman, Mario Mordmüller, Ralf Brinkmann, Matthias A. Müller, Karl Worthmann
Date: 2022-02-25 00:00:00
Tasks: Model Predictive Control, parameter estimation, Sensitivity

Laser photocoagulation is one of the most frequently used treatment approaches for retinal diseases such as diabetic retinopathy and macular edema. The use of model-based control, such as Model Predictive Control (MPC), enhances a safe and effective treatment by guaranteeing temperature bounds. In general, real-time requirements for model-based control designs are not met since the temperature distribution in the eye fundus is governed by a heat equation with a nonlinear parameter dependency. This issue is circumvented by representing the model by a lower-dimensional system which well-approximates the original model, including the parametric dependency. We combine a global-basis approach with the discrete empirical interpolation method, tailor its hyperparameters to laser photocoagulation, and show its superiority in comparison to a recently proposed method based on Taylor-series approximation. Its effectiveness is measured in computation time for MPC. We further present a case study to estimate the range of absorption parameters in porcine eyes, and by means of a theoretical and numerical sensitivity analysis we show that the sensitivity of the temperature increase is higher with respect to the absorption coefficient of the retinal pigment epithelium (RPE) than of the choroid's.
Compilation of Hazardous and Benign Material Complex Dielectric Constants at Millimeter Wave Frequencies for Security Applications
Authors: Mahshid Asri, Elizabeth Wig, Carey Rappaport
Date: 2022-02-24 00:00:00
Tasks: Dielectric Constant

This paper compiles measured complex dielectric constants of benign and hazardous materials used for developing algorithms for personnel scanners at airports at 30 GHz. The materials are grouped into broad classifications of potential threat by mapping the materials to the complex dielectric constant plane.
Multimodal distribution of transient time of predator extinction in a three-species food chain
Authors: Debarghya Pattanayak, Arindam Mishra, Syamal K. Dana, Nandadulal Bairagi
Date: 2022-02-27 00:00:00

Studies of transient dynamics captures the time history of any dramatic changes in the dynamics of a system. The transient dynamics is investigated here in a classic ecological model of a bistable tri-trophic food chain. All the species in the food chain either coexist or undergoes a partial extinction with the loss of predator after a transient time that depends upon the initial density of species populations. The distribution of transient time of extinction of the predator in response to initial states shows interesting pattern of inhomogeneity and anisotropy in the basin of predator-fee state. Precisely, the distribution shows a multimodal character when the initial points are near the basin boundary, and unimodal at locations far from the border. The distribution is also anisotropic in the sense that the number of modes depends on the direction from the basin location. We define two new metrics, viz., homogeneity index and local isotropic index to characterize the distinctive features of the distribution. The inhomogeneity of distribution reduces significantly with resource enrichment, however, a similar change in anisotropy is relatively low. We try to explain the origin of such multimodal distributions from the dynamical system perspectives and present its ecological implications.
The political economy of big data leaks: Uncovering the skeleton of tax evasion
Authors: Pier Luigi Sacco, Alex Arenas, Manlio De Domenico
Date: 2022-02-27 00:00:00

After the leak of 11.5 million documents from the Panamanian corporation Mossack Fonseca, an intricate network of offshore business entities has been revealed. The emerging picture is that of legal entities, either individuals or companies, involved in offshore activities and transactions with several tax havens simultaneously which establish, indirectly, an effective network of countries acting on tax evasion. The analysis of this network quantitatively uncovers a strongly connected core (a rich-club) of countries whose indirect interactions, mediated by legal entities, form the skeleton for tax evasion worldwide. Intriguingly, the rich-club mainly consists of well-known tax havens such as British Virgin Islands and Hong Kong, and major global powers such as China, Russia, United Kingdom and United States of America. The analysis provides a new way to rank tax havens because of the role they play in this network, and the results call for an international coordination on taxation policies that take into account the complex interconnected structure of tax evaders in a globalized economy.
Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers
Authors: G. F. Fortino, J. C. Zamora, L. E. Tamayose, N. S. T. Hirata, V. Guimaraes
Date: 2022-02-14 00:00:00

An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with active target time projection chambers more efficient. The code is divided in three steps: baseline correction, signal deconvolution and peak detection and integration. The CNNs were able to learn the signal processing models with relative errors of less than 6\%. The analysis based on CNNs provides the same results as the traditional deconvolution algorithms, but considerably more efficient in terms of computing time (about 65 times faster). This opens up new possibilities to improve existing codes and to simplify the analysis of the large amount of data produced in active target experiments.
Fully-integrated multipurpose microwave frequency identification system on a single chip
Authors: Yuhan Yao, Yuhe Zhao, Yanxian Wei, Feng Zhou, Daigao Chen, Yuguang Zhang, Xi Xiao, Ming Li, Jianji Dong, Shaohua Yu, Xinliang Zhang
Date: 2022-02-17 00:00:00

We demonstrate a fully-integrated multipurpose microwave frequency identification system on silicon-on-insulator platform. Thanks to its multipurpose features, the chip is able to identify different types of microwave signals, including single-frequency, multiple-frequency, chirped and frequency-hopping microwave signals, as well as discriminate instantaneous frequency variation among the frequency-modulated signals. This demonstration exhibits fully integrated solution and fully functional microwave frequency identification, which can meet the requirements in reduction of size, weight and power for future advanced microwave photonic processor.