pwc-archive/papers-with-abstracts huggingface dataset:
Novel Features for Time Series Analysis: A Complex Networks Approach
Authors: Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro, Fernando Silva
Date: 2021-10-11 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.
Time Series Analysis via Network Science: Concepts and Algorithms
Authors: Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro, Fernando Silva
Date: 2021-10-11 00:00:00
Tasks: Time Series, Time Series Analysis

There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature and solid field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
Model Predictive Control for Automotive Climate Control Systems via Value Function Approximation
Authors: Dennis Kibalama, Yuxing Liu, Stephanie Stockar, Marcello Canova
Date: 2021-10-19 00:00:00
Tasks: energy management, Management, Model Predictive Control

Among the auxiliary loads in light-duty vehicles, the air conditioning system is the single largest energy consumer. For electrified vehicles, the impact of heating and cooling loads becomes even more significant, as they compete with the powertrain for battery energy use and can significantly reduce the range or performance. While considerable work has been made in the field of optimal energy management for electrified vehicles and optimization of vehicle velocity for eco-driving, few contributions have addressed the application of energy-optimal control for heating and cooling loads. This paper proposes an energy management strategy for the thermal management system of an electrified powertrain, based on Model Predictive Control. Starting from a nonlinear model of the vapor compression refrigeration system that captures the dynamics of the refrigerant in the heat exchangers and the power consumption of the system, a constrained multi-objective optimal control problem is formulated to reduce energy consumption while tracking a desired thermal set point. An efficient implementation of MPC is proposed for real-time applications by introducing a terminal cost obtained from the approximation of the global optimal solution.
Electricity Tariff Design via Lens of Energy Justice
Authors: Hafiz Anwar Ullah Khan, Burcin Unel, Yury Dvorkin
Date: 2021-10-19 00:00:00
Tasks: Fairness

Distributed Energy Resources (DERs) can significantly affect the net social benefit in power systems, raising concerns pertaining to distributive justice, equity, and fairness. Electricity tariff and DERs share a symbiotic relationship whereby the design of the former directly impacts the economic efficiency and equity in the system. Current tariff design approaches suffer from opaque efficiency-equity trade-offs and are also agnostic of the externalities that affect both economic efficiency and equity. Therefore, this paper develops a justice-cognizant tariff design framework that improves the economic efficiency of tariff without sacrificing its distributional equity, and encompasses economic welfare, social costs of environmental and public health impacts, and socio-economic and demographic characteristics of electricity consumers. The proposed framework is based on a Single Leader Single Follower (SLSF) game incorporating a multi-objective optimization problem, and is evaluated on four different tariff structures. The SLSF game is reformulated as a Multi-Objective Problem with Equilibrium Constraints (MOPEC) and is solved by integrating the objective sum method for multi-objective optimization and Scholtes's relaxation technique for equilibrium constraints. We compare the economic efficiency and equity of the proposed framework using the 11-zone New York ISO and 7-bus Manhattan power networks. The results demonstrate that spatially- and temporally-granular tariffs ensure equity and economic efficiency at a lower energy burden to consumers.
Subframework-Based Rigidity Control in Multirobot Networks
Authors: Juan F. Presenza, J. Ignacio Alvarez-Hamelin, Ignacio Mas, Juan I. Giribet
Date: 2021-10-19 00:00:00

This paper presents an alternative approach to the study of distance rigidity in networks of mobile agents, based on a subframework scheme. The advantage of the proposed strategy lies in expressing framework rigidity, which is inherently global, as a set of local properties. Also, we show that a framework's normalized rigidity eigenvalue degrades as the graph's diameter increases. Thus, the rigidity eigenvalue associated to each subframework arise naturally as a local rigidity metric. A decentralized subframework-based controller for maintaining rigidity using only range measurements is developed, which is also aimed to minimize the network's communication load. Finally, we show that the information exchange required by the controller is completed in a finite number of iterations, indicating the convenience of the proposed scheme.
Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic
Authors: Jiawei Wang, Yang Zheng, Qing Xu, Keqiang Li
Date: 2021-10-19 00:00:00
Tasks: Autonomous Vehicles

Cooperative control of Connected and Autonomous Vehicles (CAVs) promises great benefits for mixed traffic. Most existing research focuses on model-based control strategies, assuming that car-following dynamics of human-driven vehicles are explicitly known. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic. We first present a linearized dynamical model for mixed traffic systems, and investigate its controllability and observability. Based on these control-theoretic properties, we then propose a novel DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) strategy for CAVs based on measurable driving data to smooth mixed traffic. Our method is implemented in a receding horizon manner, in which input/output constraints are incorporated to achieve collision-free guarantees. Nonlinear traffic simulations reveal its saving of up to 24.96% fuel consumption during a braking scenario of Extra-Urban Driving Cycle while ensuring safety.
Energy Management System for Resilience-Oriented Operation of Ship Power Systems
Authors: Thai-Thanh Nguyen, Bang Le-Huy Nguyen, Tuyen Vu
Date: 2021-10-19 00:00:00
Tasks: energy management, Management

This paper proposes an original energy management methodology for enhancing the resilience of ship power systems considering multiple types of energy storage systems, including battery energy storage systems (BESS) and supercapacitor energy storage systems (SCESS). The primary function of the proposed EMS is to maximize the load operability while taking ramp-rate characteristics of energy storage systems (ESS) and generators into account innovatively. Balancing state-of-charge (SoC) of BESS and prioritizing the SoC level of SCESS are two additional objectives of the proposed EMS to manage energy storage systems. The receding horizon optimization (RHO) technique is proposed to reduce the computational burden, making the proposed method feasible for real-time applications. An all-electric MVDC ship power system is used to evaluate the performance of the proposed methodology. Simulation studies and results demonstrate the effectiveness of the proposed method in managing the ESS to ensure the system resilience under generation power shortage. In addition, the proposed RHO technique significantly reduces the computation burden seen in the FHO technique while maintaining an acceptable resilience performance.
Optimal Grid-Forming Control of Battery Energy Storage Systems Providing Multiple Services: Modelling and Experimental Validation
Authors: Francesco Gerini, Yihui Zuo, Rahul Gupta, Elena Vagnoni, Rachid Cherkaoui, Mario Paolone
Date: 2021-10-19 00:00:00
Tasks: Model Predictive Control, Scheduling

This paper proposes and experimentally validates a joint control and scheduling framework for a grid-forming converter-interfaced BESS providing multiple services to the electrical grid. The framework is designed to dispatch the operation of a distribution feeder hosting heterogeneous prosumers according to a dispatch plan and provide frequency containment reserve and voltage control as additional services. The framework consists of three phases. In the day-ahead scheduling phase, a robust optimization problem is solved to compute the optimal dispatch plan and frequency droop coefficient, accounting for the uncertainty of the aggregated prosumption. In the intra-day phase, a model predictive control algorithm is used to compute the power set-point for the BESS to achieve the tracking of the dispatch plan. Finally, in a real-time stage, the power setpoint originated by the dispatch tracking is converted into a feasible frequency set-point for the grid forming converter by means of a convex optimisation problem accounting for the capability curve of the power converter. The proposed framework is experimentally validated by using a grid-scale 720 kVA/560 kWh BESS connected to a 20 kV distribution feeder of the EPFL hosting stochastic prosumption and PV generation.
Formal Power Series Approach to Nonlinear Systems with Additive Static Feedback
Authors: G. S. Venkatesh, W. Steven Gray
Date: 2021-10-19 00:00:00

The goal of this paper is to compute the generating series of a closed-loop system when the plant is described in terms of a Chen-Fliess series and an additive static output feedback is applied. The first step is to consider the so called Wiener-Fliess connection consisting of a Chen-Fliess series followed by a memoryless function. Of particular importance will be the contractive nature of this map, which is needed to show that the closed-loop system has a Chen-Fliess series representation. To explicitly compute the generating series, two Hopf algebras are needed, the existing output feedback Hopf algebra used to describe dynamic output feedback, and the Hopf algebra of the shuffle group. These two combinatorial structures are combined to compute what will be called the Wiener-Fliess feedback product. It will be shown that this product has a natural interpretation as a transformation group acting on the plant and preserves the relative degree of the plant. The convergence of the Wiener-Fliess composition product and the additive static feedback product are completely characterized.
Switched Control Applied to a Totem-Pole Bridgeless Rectifier for Power Factor Correction
Authors: L. V. Fiorio, T. J. M. Dezuo, Y. R. de Novaes
Date: 2021-10-19 00:00:00

The wide range of operation of bridgeless rectifiers requires a control technique that guarantee robustness. Linear Power Factor Correction (PFC) control techniques, although effective, cannot guarantee such robustness. Nonlinear techniques such as one cycle control are more robust, but other options should be explored. In this work, an affine model is obtained for a Totem-Pole Bridgeless Rectifier (TPBR). An extension to an existing switched control design technique is presented in order to achieve PFC in a robust fashion for the TPBR. Simulations with nonideal components and distorted grid voltage show a precise, fast and robust performance of the switched controller. The effective reference following of the proposed method allows the user to define a current reference waveform that prioritize THD or power factor, depending on the application and norm requirements.
Multipath-based Localization and Tracking considering Off-Body Channel Effects
Authors: Thomas Wilding, Erik Leitinger, Klaus Witrisal
Date: 2021-10-19 00:00:00
Tasks: Position

This paper deals with multipath-based positioning and tracking in off-body channels. An analysis of the effects introduced by the human body and the implications on positioning and tracking is presented based on channel measurements obtained in an indoor scenario. It shows the influence of the radio signal bandwidth on the human body induced field of view (FOV) and the number of multipath components (MPCs) detected and estimated by a deterministic maximum likelihood (ML) algorithm. A multipath-based positioning and tracking algorithm is proposed that associates these estimated MPC parameters with floor plan features and exploits a human body-dependent FOV function. The proposed algorithm is able to provide accurate position estimates even for an off-body radio channel in a multipath-prone environment with the signal bandwidth found to be a limiting factor.
A Novel Recurrent Adaptive Backstepping Optimal Control Strategy for a Single Inverted Pendulum System
Authors: Mohammad Sarbaz
Date: 2021-10-19 00:00:00

In this paper, a novel recurrent adaptive backstepping optimal control strategy for a single inverted pendulum system is studied. By this method, an inverted pendulum is stabilized using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The inverted pendulum is a popular nonlinear system that is used in both industry and academic and is applied various control approaches since it has many applications. Here, first of all, the backstepping control laws are investigated based on the nonlinear dynamic model of the system. Second, by considering control constrains and performance index, the constrained optimization problem is formulated. Later, the optimization problem will be converted to a constrained quadratic problem (QP). To study the recurrent neural network (RNN) according to the Karush- Kuhn-Tucker (KKT) optimization conditions and the variational inequality, the dynamic model of the RNN will be derived. At last, the stability analysis of the system is studied using Lyapunov function.
Distributed order estimation of ARX model under cooperative excitation condition
Authors: Die Gan, Zhixin Liu
Date: 2021-10-19 00:00:00

In this paper, we consider the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs (ARX) when both the system orders and parameters are unknown. We design distributed algorithms to estimate the unknown orders and parameters by combining the proposed local information criterion (LIC) with the distributed least squares method. The simultaneous estimation for both the system orders and parameters brings challenges for the theoretical analysis. Some analysis techniques, such as double array martingale limit theory, stochastic Lyapunov functions, and martingale convergence theorems are employed. For the case where the upper bounds of the true orders are available, we introduce a cooperative excitation condition, under which the strong consistency of the estimation for the orders and parameters is established. Moreover, for the case where the upper bounds of true orders are unknown, similar distributed algorithm is proposed to estimate both the orders and parameters, and the corresponding convergence analysis for the proposed algorithm is provided. We remark that our results are obtained without relying on the independency or stationarity assumptions of regression vectors, and the cooperative excitation conditions can show that all sensors can cooperate to fulfill the estimation task even though any individual sensor can not.
Event-Triggered Tracking Control of Networked and Quantized Control Systems
Authors: Wei Ren, Dimos V. Dimarogonas, Raphael Jungers
Date: 2021-10-19 00:00:00

This paper studies the tracking control problem of networked and quantized control systems under both multiple networks and event-triggered mechanisms. Multiple networks are to connect the plant and reference system with decentralized controllers to guarantee their information transmission, whereas event-triggered mechanisms are to reduce the information transmission via multiple networks. In this paper, all networks are independent and asynchronous and have local event-triggered mechanisms, which are based on local measurements and determine whether the local measurements need to be transmitted. We first implement an emulation-based approach to develop a novel hybrid model for tracking control of networked and quantized control systems. Next, sufficient conditions are derived and decentralized event-triggered mechanisms are designed to ensure the tracking performance. Finally, a numerical example is given to illustrate the obtained results.
User Based Design and Evaluation Pipeline for Indoor Airships
Authors: Zhaoliang Zheng, Jiahao Li, Parth Agrawal, Zhao Lei, Aaron John-Sabu, Ankur Mehta
Date: 2021-10-19 00:00:00

Designing a controllable airship for non-expert users or preemptively evaluating the performance of desired airships has always been a very challenging problem. This paper explores the blimp design parameter space from the aspect of the user by considering various distributions of thrust, combinations of propulsive mechanisms, and balloon shapes. We provide open-source modular hardware and reconfigurable software design tools that allow inexperienced users to design a custom airship in a short time. Based on these design parameters, this paper develops a more engineering-focused evaluation system that can characterize the performance of different indoor blimps. An analytical comparison and some case studies that consider various points in the design parameter space have been conducted to prove the feasibility and validity of our design and evaluation system.
Geometry-Based Output Robust Tracking Control of a Quadrotor
Authors: O. I. Borisov, M. A. Kakanov, A. Iu. Zhivitckii, A. A. Pyrkin
Date: 2021-10-18 00:00:00

The paper solves the problem of tracking control of a quadrotor with unmeasurable pitch and roll angles based on the geometric approach with the use of the enhanced extended observer and the internal model. The proposed approach makes it possible to ensure the movement of a quadrotor in a horizontal plane along a trajectory given in the form of a sinusoidal or second-order polynomial function with semiglobal asymptotic convergence of the tracking errors to zero.
Robust Control of a Surface Vessel with Adaptive Rejection of Disturbances with Unknown Parameters
Authors: O. I. Borisov, F. B. Gromova, A. Iu. Zhivitckii, A. A. Pyrkin
Date: 2021-10-18 00:00:00

This paper solves the problem of station-keeping control of a surface vessel under conditions of sinusoidal disturbances with unknown parameters. The proposed control algorithm is based on the geometric approach with the use of the adaptive internal model and the extended observer. The paper analytically proves the boundedness of the trajectories of the system and their semiglobal convergence to an arbitrarily small set. The performance of the algorithm is illustrated by computer simulation.
Dynamics and control of clustered tensegrity systems
Authors: Shuo Ma, Muhao Chen, Robert E. Skelton
Date: 2021-10-17 00:00:00

This paper presents the formulations of nonlinear and linearized statics, dynamics, and control for any clustered tensegrity system (CTS). Based on the Lagrangian method and FEM assumptions, the nonlinear clustered tensegrity dynamics with and without constraints are first derived. It is shown that the traditional tensegrity system (TTS), whose node to node strings are individual ones, yield to be a special case of the CTS. Then, equilibrium equations of the CTS in three standard forms (in terms of nodal coordinate, force density, and force vector) and the compatibility equation are given. Moreover, the linearized dynamics and modal analysis of the CTS with and without constraints are also derived. We also present a nonlinear shape control law for the control of any CTS. The control turns out to be a linear algebra problem in terms of the control variable, which is the force densities in the strings. The statics, dynamics, and control examples are carefully selected to demonstrate the developed principles. The presented approaches can boost the comprehensive studies of the statics, dynamics, and control for any CTS, as well as promoting the integration of structure and control design.
Effects of measures on phase transitions in two cooperative susceptible-infectious-recovered dynamics
Authors: Adib Khazaee, Fakhteh Ghanbarnejad
Date: 2021-10-18 00:00:00

In recent studies, it has been shown that a cooperative interaction in a co-infection spread can lead to a discontinuous transition at a decreased threshold. Here, we investigate effects of immunization with a rate proportional to the extent of the infection on phase transitions of a cooperative co-infection. We use the mean-field approximation to illustrate how measures that remove a portion of the susceptible compartment, like vaccination, with high enough rates can change discontinuous transitions in two coupled susceptible-infectious-recovered dynamics into continuous ones while increasing the threshold of transitions. First, we introduce vaccination with a fixed rate into a symmetric spread of two diseases and investigate the numerical results. Second, we set the rate of measures proportional to the size of the infectious compartment and scrutinize the dynamics. We solve the equations numerically and analytically and probe the transitions for a wide range of parameters. We also determine transition points from the analytical solutions. Third, we adopt a heterogeneous mean-field approach to include heterogeneity and asymmetry in the dynamics and see if the results corresponding to homogeneous symmetric case stand.