pwc-archive/papers-with-abstracts huggingface dataset:
An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data
Authors: Hyeonjin Kim, Kai Ye, Han Pyo Lee, Rongxing Hu, Ning Lu, Di wu, PJ Rehm
Date: 2022-09-19 00:00:00

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods.Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.
Online Poisoning Attacks Against Data-Driven Predictive Control
Authors: Yue Yu, Ruihan Zhao, Sandeep Chinchali, Ufuk Topcu
Date: 2022-09-19 00:00:00

Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics. It repeatedly optimizes a system's future trajectories based on past input-output data. We develop a numerical method that computes poisoning attacks that inject additive perturbations to the online output data to change the trajectories optimized by DPC. This method is based on implicitly differentiating the solution map of the trajectory optimization in DPC. We demonstrate that the resulting attacks can cause an output tracking error one order of magnitude higher than random perturbations in numerical experiments.
Coexistence of UAVs and Terrestrial Users in Millimeter-Wave Urban Networks
Authors: Seongjoon Kang, Marco Mezzavilla, Angel Lozano, Giovanni Geraci, Sundeep Rangan, Vasilii Semkin, William Xia, Giuseppe Loianno
Date: 2022-09-19 00:00:00

5G millimeter-wave (mmWave) cellular networks are in the early phase of commercial deployments and present a unique opportunity for robust, high-data-rate communication to unmanned aerial vehicles (UAVs). A fundamental question is whether and how mmWave networks designed for terrestrial users should be modified to serve UAVs. The paper invokes realistic cell layouts, antenna patterns, and channel models trained from extensive ray tracing data to assess the performance of various network alternatives. Importantly, the study considers the addition of dedicated uptilted rooftop-mounted cells for aerial coverage, as well as novel spectrum sharing modes between terrestrial and aerial network operators. The effect of power control and of multiuser multiple-input multiple-output are also studied.
Adaptive Output Feedback Model Predictive Control
Authors: Anchita Dey, Abhishek Dhar, Shubhendu Bhasin
Date: 2022-09-19 00:00:00
Tasks: model, Model Predictive Control, State Estimation

Model predictive control (MPC) for uncertain systems in the presence of hard constraints on state and input is a non-trivial problem, and the challenge is increased manyfold in the absence of state measurements. In this paper, we propose an adaptive output feedback MPC technique, based on a novel combination of an adaptive observer and robust MPC, for single-input single-output discrete-time linear time-invariant systems. At each time instant, the adaptive observer provides estimates of the states and the system parameters that are then leveraged in the MPC optimization routine while robustly accounting for the estimation errors. The solution to the optimization problem results in a homothetic tube where the state estimate trajectory lies. The true state evolves inside a larger outer tube obtained by augmenting a set, invariant to the state estimation error, around the homothetic tube sections. The proof for recursive feasibility for the proposed `homothetic and invariant' two-tube approach is provided, along with simulation results on an academic system.
A Simplified Model for the Battery Ageing Potential Under Highly Rippled Load for Battery Management and Active Degradation Control
Authors: Tomas Kacetl, Jan Kacetl, Nima Tashakor, Stefan Goetz
Date: 2022-09-19 00:00:00
Tasks: Management

Whereas in typical standardized tests batteries are almost exclusively loaded with constant current or relatively slowly changing cycles, real applications involve rapid load ripple, which do not contribute to the net energy. The trend to reduced filter capacitors and even dynamically reconfigurable batteries further increases the ripple. The influence of rippled load on lithium batteries is therefore receiving increased attention. According to recent studies, accelerated ageing strongly depends on the frequency of the ripple. We use electrochemical models to derive a highly simplified regression model that catches the asymptotic behavior and allows parameter identification and calibration to specific cells. The model allows quantitative monitoring of the additional ageing due to ripple current in battery management systems. Furthermore it enables active control of the ageing potential by influencing the frequency content in modern battery systems, such as reconfigurable batteries.
Automated data-driven creation of the Digital Twin of a brownfield plant
Authors: Dominik Braun, Wolfgang Schloegl, Michael Weyrich
Date: 2022-09-19 00:00:00
Tasks: Position

The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned, supported and simplified with the Digital Twin of the system providing this knowledge. However, digital models as the basis of a Digital Twin are usually missing for these plants. This article presents a data-driven approach to gain knowledge about a brownfield system to create the digital models of a Digital Twin and their relations. Finally, a proof of concept shows that process data and position data as data sources deliver the relations between the models of the Digital Twin.
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
Authors: Henglai Wei, Changxin Liu, Yang Shi
Date: 2022-09-19 00:00:00
Tasks: Model Predictive Control

This paper studies the consensus problem of general linear discrete-time multi-agent systems (MAS) with input constraints and bounded time-varying communication delays. We propose a robust distributed model predictive control (DMPC) consensus protocol that integrates the offline consensus design with online DMPC optimization to exploit their respective advantages. More precisely, each agent is equipped with an offline consensus protocol, which is a priori designed, depending on its immediate neighbors' estimated states. Further, the estimation errors propagated over time due to inexact neighboring information are proved bounded under mild technical assumptions, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying input constraints. Moreover, it is shown that, with the suitably designed cost function and constraints, the feasibility of the associated optimization problem can be recursively ensured. We further provide the consensus convergence result of the constrained MAS in the presence of bounded varying delays. Finally, two numerical examples are given to verify the effectiveness of the proposed distributed consensus algorithm.
Online Pricing Incentive to Sample Fresh Information
Authors: Hongbo Li, Lingjie Duan
Date: 2022-09-19 00:00:00

Today mobile users such as drivers are invited by content providers (e.g., Tripadvisor) to sample fresh information of diverse paths to control the age of information (AoI). However, selfish drivers prefer to travel through the shortest path instead of the others with extra costs in time and gas. To motivate drivers to route and sample diverse paths, this paper is the first to propose online pricing for a provider to economically reward drivers for diverse routing and control the actual AoI dynamics over time and spatial path domains. This online pricing optimization problem should be solved without knowing drivers' costs and even arrivals, and is intractable due to the curse of dimensionality in both time and space. If there is only one non-shortest path, we leverage the Markov decision process (MDP) techniques to analyze the problem. Accordingly, we design a linear-time algorithm for returning optimal online pricing, where a higher pricing reward is needed for a larger AoI. If there are a number of non-shortest paths, we prove that pricing one path at a time is optimal, yet it is not optimal to choose the path with the largest current AoI. Then we propose a new backward-clustered computation method and develop an approximation algorithm to alternate different paths to price over time. Perhaps surprisingly, our analysis of approximation ratio suggests that our algorithm's performance approaches closer to optimum given more paths.
A THz Video SAR Imaging Algorithm Based on Chirp Scaling
Authors: Jiawei Jiang, Yinwei Li, Qibin Zheng
Date: 2022-09-19 00:00:00

In video synthetic aperture radar (SAR) imaging mode, the polar format algorithm (PFA) is more computational effective than the backprojection algorithm (BPA). However, the two-dimensional (2-D) interpolation in PFA greatly affects its computational speed, which is detrimental to the real-time imaging of video SAR. In this paper, a terahertz (THz) video SAR imaging algorithm based on chirp scaling is proposed, which utilizes the small synthetic angular feature of THz SAR and the inherent property of linear frequency modulation. Then, two-step chirp scaling is used to replace the 2-D interpolation in the PFA to obtain a similar focusing effect, but with a faster operation. Point target simulation is used to verify the effectiveness of the proposed method.
Bumpless Topology Transition
Authors: Tong Han, Yue Song, David J. Hill
Date: 2022-09-18 00:00:00

The topology transition problem of transmission networks is becoming increasingly crucial with topological flexibility more widely leveraged to promote high renewable penetration. This paper proposes a novel methodology to address this problem. Aiming at achieving a bumpless topology transition regarding both static and dynamic performance, this methodology utilizes various eligible control resources in transmission networks to cooperate with the optimization of line-switching sequence. Mathematically, a composite formulation is developed to efficiently yield bumpless transition schemes with AC feasibility and stability both ensured. With linearization of all non-convexities involved and tractable bumpiness metrics, a convex mixed-integer program firstly optimizes the line-switching sequence and partial control resources. Then, two nonlinear programs recover AC feasibility, and optimize the remaining control resources by minimizing the $\mathcal{H}_2$-norm of associated linearized systems, respectively. The final transition scheme is selected by accurate evaluation including stability verification using time-domain simulations. Finally, numerical studies demonstrate the effectiveness and superiority of the proposed methodology to achieve bumpless topology transition.
Design and Development of a Lorentz Force-Based MRI-Driven Neuroendoscope
Authors: Martin Francis Phelan III, Nihal Olcay Dogan, Jelena Lazovic, Metin Sitti
Date: 2022-09-18 00:00:00

The introduction of neuroendoscopy, microneurosurgery, neuronavigation, and intraoperative imaging for surgical operations has made significant improvements over other traditionally invasive surgical techniques. The integration of magnetic resonance imaging (MRI)-driven surgical devices with intraoperative imaging and endoscopy can enable further advancements in surgical treatments and outcomes. This work proposes the design and development of an MRI-driven endoscope leveraging the high (3-7 T), external magnetic field of an MR scanner for heat-mitigated steering within the ventricular system of the brain. It also demonstrates the effectiveness of a Lorentz force-based grasper for diseased tissue manipulation and ablation. Feasibility studies show the neuroendoscope can be steered precisely within the lateral ventricle to locate a tumor using both MRI and endoscopic guidance. Results also indicate grasping forces as high as 31 mN are possible and power inputs as low as 0.69 mW can cause cancerous tissue ablation. These findings enable further developments of steerable devices using MR imaging integrated with endoscopic guidance for improved outcomes.
An Edge Architecture Oriented Model Predictive Control Scheme for an Autonomous UAV Mission
Authors: Achilleas Santi Seisa, Sumeet Gajanan Satpute, Björn Lindqvist, George Nikolakopoulos
Date: 2022-09-17 00:00:00
Tasks: Cloud Computing, Edge-computing, Future prediction, Model Predictive Control

In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more computation power in comparison to other controllers, such as PID or LQR, since it use cost functions, optimization methods and iteratively predicts the output of the system and the control commands for some determined steps in the future (prediction horizon). Thus, the computation power required depends on the prediction horizon, the complexity of the cost functions and the optimization. The more steps determined for the horizon the more efficient the controller can be, but also more computation power is required. Since sometimes robots are not capable of managing all the computing process locally, it is important to offload some of the computing process from the robot to the cloud. But then some disadvantages may occur, such as latency and safety issues. Cloud computing may offer "infinity" computation power but the whole system suffers in latency. A solution to this is the use of Edge Computing, which will reduce time delays since the Edge device is much closer to the source of data. Moreover, by using the Edge we can offload the demanding controller from the UAV and set a longer prediction horizon and try to get a more efficient controller.
Feedback Stability Analysis via Dissipativity with Dynamic Supply Rates
Authors: Sei Zhen Khong, Chao Chen, Alexander Lanzon
Date: 2022-09-17 00:00:00
Tasks: Miscellaneous

We propose a general notion of dissipativity with dynamic supply rates for nonlinear systems. This extends classical dissipativity with static supply rates and dynamic supply rates of miscellaneous quadratic forms. The main results of this paper concern Lyapunov and asymptotic stability analysis for nonlinear feedback dissipative systems that are characterised by dissipation inequalities with respect to compatible dynamic supply rates but involving possibly different and independent auxiliary systems. Importantly, dissipativity conditions guaranteeing stability of the state of the feedback systems, without concerns on the stability of the state of the auxiliary systems, are provided. The key results also specialise to a simple coupling test for the interconnection of two nonlinear systems described by dynamic (Psi, Pi, Upsilon, Omega)-dissipativity, and are shown to recover several existing results in the literature, including small-gain, passivity indices, static (Q, S, R)-dissipativity, dissipativity with terminal costs, etc. Comparison with the input-output approach to feedback stability analysis based on integral quadratic constraints is also made.
On Power Control of Grid-Forming Converters: Modeling, Controllability, and Full-State Feedback Design
Authors: Meng Chen, Dao Zhou, Ali Tayyebi, Eduardo Prieto-Araujo, Florian Dörfler, Frede Blaabjerg
Date: 2022-09-17 00:00:00

The popular single-input single-output control structures and classic design methods (e.g., root locus analysis) for the power control of grid-forming converters have limitations in applying to different line characteristics and providing favorable performance. This paper studies the grid-forming converter power loops from the perspective of multi-input multi-output systems. First, the error dynamics associated with power control loops (error-based state-space model) are derived while taking into account the natural dynamical coupling terms of the power converter models. Thereafter, the controllability Gramian of the grid-forming converter power loops is studied. Last, a full-state feedback control design using only the local measurements is applied. By this way, the eigenvalues of the system can be arbitrarily placed in the timescale of power loops based on predefined time-domain specifications. A step-by-step construction and design procedure of the power control of grid-forming converters is also given. The analysis and proposed method are verified by experimental results.
RISE-Based Adaptive Control with Mass-Inertia Parameter Estimation for Aerial Transportation of Multi-Rotor UAVs
Authors: Shuyang Shi, Yuzhu Li, Wei Dong
Date: 2022-09-17 00:00:00
Tasks: parameter estimation

This paper proposes an adaptive tracking strategy with mass-inertia estimation for aerial transportation problems of multi-rotor UAVs. The dynamic model of multi-rotor UAVs with disturbances is firstly developed with a linearly parameterized form. Subsequently, a cascade controller with the robust integral of the sign of the error (RISE) terms is applied to smooth the control inputs and address bounded disturbances. Then, adaptive estimation laws for mass-inertia parameters are designed based on a filter operation. Such operation is introduced to extract estimation errors exploited to theoretically guarantee the finite-time (FT) convergence of estimation errors. Finally, simulations are conducted to verify the effectiveness of the designed controller. The results show that the proposed method provides better tracking and estimation performance than traditional adaptive controllers based on sliding mode control algorithms and gradient-based estimation strategies.
Valley-Spin Hall Effect-based Nonvolatile Memory with Exchange-Coupling-Enabled Electrical Isolation of Read and Write Paths
Authors: Karam Cho, Sumeet Kumar Gupta
Date: 2022-09-16 00:00:00

Valley-spin hall (VSH) effect in monolayer WSe2 has been shown to exhibit highly beneficial features for nonvolatile memory (NVM) design. Key advantages of VSH-based magnetic random-access memory (VSH-MRAM) over spin orbit torque (SOT)-MRAM include access transistor-less compact bit-cell and low power switching of perpendicular magnetic anisotropy (PMA) magnets. Nevertheless, large device resistance in the read path (RS) due to low mobility of WSe2 and Schottky contacts deteriorates sense margin, offsetting the benefits of VSH-MRAM. To address this limitation, we propose another flavor of VSH-based MRAM that (while inheriting most of the benefits of VSH-MRAM) achieves lower RS in the read path by electrically isolating the read and write terminals. This is enabled by coupling VSH with electrically-isolated but magnetically-coupled PMA magnets via interlayer exchange-coupling. Designing the proposed devices using object oriented micro magnetic framework (OOMMF) simulation, we ensure the robustness of the exchange-coupled PMA system under process variations. To maintain a compact memory footprint, we share the read access transistor across multiple bit-cells. Compared to the existing VSH-MRAMs, our design achieves 39%-42% and 36%-46% reduction in read time and energy, respectively, along with 1.1X-1.3X larger sense margin at a comparable area. This comes at the cost of 1.7X and 2.0X increase in write time and energy, respectively. Thus, the proposed design is suitable for applications in which reads are more dominant than writes.
Disturbance Observers for Robust Safety-critical Control with Control Barrier Functions
Authors: Anil Alan, Tamas G. Molnar, Ersin Das, Aaron D. Ames, Gabor Orosz
Date: 2022-09-16 00:00:00

This work provides formal safety guarantees for control systems with disturbance. A disturbance observer-based robust safety-critical controller is proposed, that estimates the effect of the disturbance on safety and utilizes this estimate with control barrier functions to attain provably safe dynamic behavior. The observer error bound - which consists of transient and steady-state parts - is quantified, and the system is endowed with robustness against this error via the proposed controller. An adaptive cruise control problem is used as illustrative example through simulations including real disturbance data.
Cascading Failures in Power Grids
Authors: Rounak Meyur
Date: 2022-09-16 00:00:00

This paper studies the consequences of a human-initiated targeted attack on the national electric power system. We consider two kinds of attacks: ($i$) an attack by an adversary that uses a tactical weapon and destroys a large part of the grid, by physically targeting a large geographic region; ($ii$) a targeted attack by an adversary that takes out a small number of critical components in the network simultaneously. Our analysis uses ($i$) a realistic representation of the underlying power grid, including the topology, the control and protection components, ($ii$) a realistic representation of the targeted attack scenario, and ($iii$) a dynamic stability analysis, that goes beyond traditional work comprising structural and linear flow analysis. Such realistic analysis is expensive, but critical since it can capture cascading failures that result from transient instabilities introduced due to the attack. Our model acknowledges the presence of hidden failures in the protection systems resulting in relay misoperations. We analyze the extent of cascading outages for different levels of hidden failures. Our results show that: ($i$) the power grid is vulnerable to both these attacks, ($ii$) the tactical attack has significant social, economic and health damage but need not result in a regional cascade; on the contrary the targeted attack can cause significant cascade and lead to power outage over a large region. Our work shows the necessity to harden the power grid not just to cyber-attacks but also to physical attacks. Furthermore, we show that realistic representations and analysis can lead to fundamentally new insights that simplified models are unlikely to capture. Finally, the methods and results help us identify critical elements in the grid; the system can then be hardened in a more precise manner to reduce the vulnerabilities.