pwc-archive/papers-with-abstracts huggingface dataset:
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
Authors: Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski, Ruggero Carli, Diego Romeres
Date: 2021-04-26 00:00:00

In this paper, we consider the use of black-box Gaussian process (GP) models for trajectory tracking control based on feedback linearization, in the context of mechanical systems. We considered two strategies. The first computes the control input directly by using the GP model, whereas the second computes the input after estimating the individual components of the dynamics. We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice. Results show that the second implementation is more robust w.r.t. the kernel choice and model inaccuracies. Moreover, as regards the choice of kernel, the obtained performance shows that the use of a structured kernel, such as a polynomial kernel, is advantageous, because of its effectiveness with both strategies.
CPS Engineering: Gap Analysis and Perspectives
Authors: Emmanuel Ledinot
Date: 2021-04-26 00:00:00

Virtualization of computing and networking, IT-OT convergence, cybersecurity and AI-based enhancement of autonomy are significantly increasing the complexity of CPS and CPSoS. New challenges have emerged to demonstrate that these systems are safe and secure. We emphasize the role of control and emerging fields therein, like symbolic control or set-based fault-tolerant and decentralized control, to address safety. We have chosen three open verification problems we deem central in cost-effective development and certification of safety critical CPSoS. We review some promising threads of research that could lead in the long term to a scalable and powerful verification strategy. Its main components are set-based and invariant-based design, contracts, adversarial testing, algorithmic geometry of dynamics, and probabilistic estimation derived from compositional massive testing. To explore these orientations in collaborative projects, and to promote them in certification arenas, we propose to continue and upgrade an open innovation drone-based use case that originated from a collaborative research project in aeronautic certification reformation
BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data
Authors: Hadrien Pujol, Éric Bavu, Alexandre Garcia
Date: 2021-04-27 00:00:00
Tasks: Audio Signal Processing, BIG-bench Machine Learning, Computational Efficiency, GPU

Sound sources localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has allowed to drastically improve performances for machine hearing. This has motivated the scientific community to also develop machine learning strategies for source localization applications. In this paper, we present BeamLearning, a multi-resolution deep learning approach that allows to encode relevant information contained in unprocessed time domain acoustic signals captured by microphone arrays. The use of raw data aims at avoiding simplifying hypothesis that most traditional model-based localization methods rely on. Benefits of its use are shown for realtime sound source 2D-localization tasks in reverberating and noisy environments. Since supervised machine learning approaches require large-sized, physically realistic, precisely labelled datasets, we also developed a fast GPU-based computation of room impulse responses using fractional delays for image source models. A thorough analysis of the network representation and extensive performance tests are carried out using the BeamLearning network with synthetic and experimental datasets. Obtained results demonstrate that the BeamLearning approach significantly outperforms the wideband MUSIC and SRP-PHAT methods in terms of localization accuracy and computational efficiency in presence of heavy measurement noise and reverberation.
IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples
Authors: D. Trejo Pizzo, S. Esteban
Date: 2021-04-27 00:00:00
Tasks: Chatbot, Specificity

OBJECTIVE: Our objective is to evaluate the possibility of using cough audio recordings (spontaneous or simulated) to detect sound patterns in people who are diagnosed with COVID-19. The research question that led our work was: what is the sensitivity and specificity of a machine learning based COVID-19 cough classifier, using RT-PCR tests as gold standard? SETTING: The audio samples that were collected for this study belong to individuals who were swabbed in the City of Buenos Aires in 20 public and 1 private facilities where RT-PCR studies were carried out on patients suspected of COVID, and 14 out-of-hospital isolation units for patients with confirmed COVID mild cases. The audios were collected through the Buenos Aires city government WhatsApp chatbot that was specifically designed to address citizen inquiries related to the coronavirus pandemic (COVID-19). PARTICIPANTS: The data collected corresponds to 2821 individuals who were swabbed in the City of Buenos Aires, between August 11 and December 2, 2020. Individuals were divided into 1409 that tested positive for COVID-19 and 1412 that tested negative. From this sample group, 52.6% of the individuals were female and 47.4% were male. 2.5% were between the age of 0 and 20 , 61.1% between the age of 21 and 40 , 30.3% between the age of 41 and 60 and 6.1% were over 61 years of age. RESULTS: Using the dataset of 2821 individuals our results showed that the neural network classifier was able to discriminate between the COVID-19 positive and the healthy coughs with an accuracy of 86%. This accuracy obtained during the training process was later tested and confirmed with a second dataset corresponding to 492 individuals.
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
Authors: Diego Di Carlo, Pinchas Tandeitnik, Cédric Foy, Antoine Deleforge, Nancy Bertin, Sharon Gannot
Date: 2021-04-27 00:00:00
Tasks: Benchmarking, Retrieval, Room Impulse Response (RIR), Speech Enhancement

This paper presents dEchorate: a new database of measured multichannel Room Impulse Responses (RIRs) including annotations of early echo timings and 3D positions of microphones, real sources and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling and reflectors estimation. The database is accompanied with software utilities to easily access, manipulate and visualize the data as well as baseline methods for echo-related tasks.
Learning Markov models of fading channels in wireless control networks: a regression trees based approach
Authors: Luis Felipe Florenzan Reyes, Francesco Smarra, Yuriy Zacchia Lun, Alessandro D'Innocenzo
Date: 2021-04-27 00:00:00
Tasks: regression

Finite-state Markov models are widely used for modeling wireless channels affected by a variety of non-idealities, ranging from shadowing to interference. In an industrial environment, the derivation of a Markov model based on the wireless communication physics can be prohibitive as it requires a complete knowledge of both the communication dynamics parameters and of the disturbances/interferers. In this work, a novel methodology is proposed to learn a Markov model of a fading channel via historical data of the signal-to-interference-plus-noise-ratio (SINR). Such methodology can be used to derive a Markov jump model of a wireless control network, and thus to design a stochastic optimal controller that takes into account the interdependence between the plant and the wireless channel dynamics. The proposed method is validated by comparing its prediction accuracy and control performance with those of a stationary finite-state Markov chain derived assuming perfect knowledge of the physical channel model and parameters of a WirelessHART point-to-point communication based on the IEEE-802.15.4 standard.
Symplectic discrete-time energy-based control for nonlinear mechanical systems
Authors: Paul Kotyczka, Tobias Thoma
Date: 2021-04-29 00:00:00

In this article we present a novel discrete-time design approach which reduces the deteriorating effects of sampling on stability and performance in digitally controlled nonlinear mechanical systems. The method is motivated by recent results for linear systems, where feedback imposes closed-loop behavior that exactly represents the symplectic discretization of a desired target system. In the nonlinear case, both the second order accurate representation of the sampling process and the definition of the target dynamics stem from the application of the implicit midpoint rule. The implicit nature of the resulting state feedback requires the numerical solution of an in general nonlinear system of algebraic equations in every sampling interval. For an implementation with pure position feedback, the velocities/momenta have to be approximated in the sampling instants, which gives a clear interpretation of our approach in terms of the St\"ormer-Verlet integration scheme on a staggered grid. We present discrete-time versions of impedance or energy shaping plus damping injection control as well as computed torque tracking control. Both the Hamiltonian and the Lagrangian perspective are adopted. Besides a linear example to introduce the concept, the simulations with a planar two link robot model illustrate the performance and stability gain compared to the discrete implementations of continuous-time control laws. A short analysis of computation times shows the real-time capability of our method.
On Identification of Boolean Control Networks
Authors: Biao Wang, Jun-e Feng, Daizhan Cheng
Date: 2021-04-29 00:00:00

A new analytical framework consisting of two phenomena: single sample and multiple samples, is proposed to deal with the identification problem of Boolean control networks (BCNs) systematically and comprehensively. Under this framework, the existing works on identification can be categorized as special cases of these two phenomena. Several effective criteria for determining the identifiability and the corresponding identification algorithms are proposed. Three important results are derived: (1) If a BN is observable, it is uniquely identifiable; (2) If a BCN is O1-observable, it is uniquely identifiable, where O1-observability is the most general form of the existing observability terms; (3) A BN or BCN may be identifiable, but not observable. In addition, remarks present some challenging future research and contain a preliminary attempt about how to identify unobservable systems.
Combined Robust and Stochastic Model Predictive Control for Models of Different Granularity
Authors: Tim Brüdigam, Johannes Teutsch, Dirk Wollherr, Marion Leibold
Date: 2020-03-14 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

Long prediction horizons in Model Predictive Control (MPC) often prove to be efficient, however, this comes with increased computational cost. Recently, a Robust Model Predictive Control (RMPC) method has been proposed which exploits models of different granularity. The prediction over the control horizon is split into short-term predictions with a detailed model using MPC and long-term predictions with a coarse model using RMPC. In many applications robustness is required for the short-term future, but in the long-term future, subject to major uncertainty and potential modeling difficulties, robust planning can lead to highly conservative solutions. We therefore propose combining RMPC on a detailed model for short-term predictions and Stochastic MPC (SMPC), with chance constraints, on a simplified model for long-term predictions. This yields decreased computational effort due to a simple model for long-term predictions, and less conservative solutions, as robustness is only required for short-term predictions. The effectiveness of the method is shown in a mobile robot collision avoidance simulation.
Invariance and Contraction in Geometrically Periodic Systems with Differential Inclusions
Authors: Chen Qian, Yongchun Fang
Date: 2021-04-29 00:00:00

The objective of this paper is to derive the essential invariance and contraction properties for the geometric periodic systems, which can be formulated as a category of differential inclusions, and primarily rendered in the phase coordinate, or the cycle coordinate. First, we introduce the geometric averaging method for this category of systems, and also analyze the accuracy of its averaging approximation. Specifically, we delve into the details of the geometrically periodic system through the tunnel of considering the convergence between the system and its geometrically averaging approximation. Under different corresponding conditions, the approximation on infinite time intervals can achieve certain accuracies, such that one can use the stability result of either the original system or the averaging system to deduce the stability of the other. After that, we employ the graphical stability to investigate the "pattern stability" with respect to the phase-based system. Finally, by virtue of the contraction analysis on the Finsler manifold, the idea of accentuating the periodic pattern incremental stability and convergence is nurtured in the phase-based differential inclusion system, and comes to its preliminary fruition in application to biomimetic mechanic robot control problem.
Practical Output Consensus of Nonlinear Heterogeneous Multi-Agent Systems with Limited Data Rate
Authors: Maopeng Ran, Lihua Xie
Date: 2021-04-27 00:00:00

This paper investigates the consensus problem for nonlinear heterogeneous multi-agent systems with limited communication data rate. Each agent is modeled by a higher-order strict-feedback continuous-time system with unknown nonlinearities andexternal disturbance, and only the first state variable being measurable. Extended state observers (ESOs) are used to estimate the unmeasurable agent states and the unknown nonlinear dynamics. An ESO-based distributed output feedback protocol with dynamic encoding and decoding is then presented. It is shown that, for a connected undirected network, the proposed protocol guarantees practical output consensus, in which the steady-state consensus error can be made arbitrarily small. The ESO-based protocol also shapes the transient consensus performance, as it is capable of recovering the consensus performance of a linear counterpart with fully measurable states. Furthermore, we prove that for higher-order uncertain nonlinear multi-agent systems, consensus can be achieved with merely one bit information exchange between each pair of adjacent agents at each time step. Finally, simulations on third-order pendulum systems are given, which verify the theoretical results.
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
Authors: Ryan K. Cosner, Andrew W. Singletary, Andrew J. Taylor, Tamas G. Molnar, Katherine L. Bouman, Aaron D. Ames
Date: 2021-04-28 00:00:00
Tasks: State Estimation

The increasing complexity of modern robotic systems and the environments they operate in necessitates the formal consideration of safety in the presence of imperfect measurements. In this paper we propose a rigorous framework for safety-critical control of systems with erroneous state estimates. We develop this framework by leveraging Control Barrier Functions (CBFs) and unifying the method of Backup Sets for synthesizing control invariant sets with robustness requirements -- the end result is the synthesis of Measurement-Robust Control Barrier Functions (MR-CBFs). This provides theoretical guarantees on safe behavior in the presence of imperfect measurements and improved robustness over standard CBF approaches. We demonstrate the efficacy of this framework both in simulation and experimentally on a Segway platform using an onboard stereo-vision camera for state estimation.
Channel Models and Coding Solutions for 1S1R Crossbar Resistive Memory with High Line Resistance
Authors: Zehui Chen, Lara Dolecek
Date: 2021-04-28 00:00:00

Crossbar resistive memory with the 1 Selector 1 Resistor (1S1R) structure is attractive for nonvolatile, high-density, and low-latency storage-class memory applications. As technology scales down to the single-nm regime, the increasing resistivity of wordline/bitline becomes a limiting factor to device reliability. This paper presents write/read communication channels while considering the line resistance and device variabilities by statistically relating the degraded write/read margins and the channel parameters. Binary asymmetric channel (BAC) models are proposed for the write/read operations. Simulations based on these models suggest that the bit-error rate of devices are highly non-uniform across the memory array. These models provide quantitative tools for evaluating the trade-offs between memory reliability and design parameters, such as array size, technology nodes, and aspect ratio, and also for designing coding-theoretic solutions that would be most effective for crossbar memory. Method for optimizing the read threshold is proposed to reduce the raw bit-error rate (RBER). We propose two schemes for efficient channel coding based on Bose-Chaudhuri-Hocquenghem (BCH) codes. An interleaved coding scheme is proposed to mitigate the non-uniformity of reliability and a location dependent coding framework is proposed to leverage this non-uniformity. Both of our proposed coding schemes effectively reduce the undetected bit-error rate (UBER).
Verification and Synthesis of Control Barrier Functions
Authors: Andrew Clark
Date: 2021-04-28 00:00:00

Control systems often must satisfy strict safety requirements over an extended operating lifetime. Control Barrier Functions (CBFs) are a promising recent approach to constructing simple and safe control policies. This paper proposes a framework for verifying that a CBF guarantees safety for all time and synthesizing CBFs with verifiable safety in polynomial control systems. Our approach is to show that safety of CBFs is equivalent to the non-existence of solutions to a family of polynomial equations, and then prove that this nonexistence is equivalent to a pair of sum-of-squares constraints via the Positivstellensatz of algebraic geometry. We develop this Positivstellensatz to verify CBFs, as well as generalization to high-degree systems and multiple CBF constraints. We then propose a set of heuristics for CBF synthesis, including a general alternating-descent heuristic, a specialized approach for compact safe regions, and an approach for convex unsafe regions. Our approach is illustrated on two numerical examples.
A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid
Authors: Zhigang Chu, Andrea Pinceti, Ramin Kaviani, Roozbeh Khodadadeh, Xingpeng Li, Jiazi Zhang, Karthik Saikumar, Mostafa Sahraei-Ardakani, Christopher Mosier, Robin Podmore, Kory Hedman, Oliver Kosut, Lalitha Sankar
Date: 2021-04-28 00:00:00
Tasks: energy management, Management, State Estimation

In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within this framework, we have designed a complete simulation platform to emulate realistic EMS operations: it includes state estimation (SE), real-time contingency analysis (RTCA), and security constrained economic dispatch (SCED). This software platform allowed us to achieve two main objectives: 1) to study the cyber vulnerabilities of an EMS and understand their consequences on the system, and 2) to formulate and implement countermeasures against cyber-attacks exploiting these vulnerabilities. Our results show that the false data injection attacks against state estimation described in the literature do not easily cause base-case overflows because of the conservatism introduced by RTCA. For a successful attack, a more sophisticated model that includes all of the EMS blocks is needed; even in this scenario, only post-contingency violations can be achieved. Nonetheless, we propose several countermeasures that can detect changes due to cyber-attacks and limit their impact on the system.
Data-Driven Reachability Analysis with Christoffel Functions
Authors: Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
Date: 2021-04-28 00:00:00

We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions. The level sets of Christoffel functions are known empirically to provide good approximations to the support of probability distributions: the algorithm uses this property for reachability analysis by solving a probabilistic relaxation of the reachable set computation problem. We also provide a guarantee that the output of the algorithm is an accurate reachable set approximation in a probabilistic sense, provided that a certain sample size is attained. We also investigate three numerical examples to demonstrate the algorithm's capabilities, such as providing non-convex reachable set approximations and detecting holes in the reachable set.
Closed-loop Control Design and Motor Allocation for a Lower-limb Cable-driven Exoskeleton: A Switched Systems Approach
Authors: Chen-Hao Chang, Jonathan Casas, Victor H. Duenas
Date: 2021-04-28 00:00:00

Powered lower-limb exoskeletons provide assistive torques to coordinate limb motion during walking in individuals with movement disorders. Advances in sensing and actuation have improved the wearability and portability of state-of-the-art exoskeletons for walking. Cable-driven exoskeletons offload the actuators away from the user, thus rendering light-weight devices to facilitate locomotion training. However, cable-driven mechanisms experience a slacking behavior if tension is not accurately controlled. Moreover, counteracting forces can arise between the agonist and antagonist motors yielding undesired joint motion. In this paper, the strategy is to develop two control layers to improve the performance of a cable-driven exoskeleton. First, a joint tracking controller is designed using a high-gain robust approach to track desired knee and hip trajectories. Second, a motor synchronization objective is developed to mitigate the effects of cable slacking for a pair of electric motors that actuate each joint. A sliding-mode robust controller is designed for the motor synchronization objective. A Lyapunov-based stability analysis is developed to guarantee a uniformly ultimately bounded result for joint tracking and exponential tracking for the motor synchronization objective. Moreover, an average dwell time analysis provides a bound on the number of motor switches when allocating the control between motors that actuate each joint. An experimental result with an able-bodied individual illustrates the feasibility of the developed control methods.