pwc-archive/papers-with-abstracts huggingface dataset:
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies
Authors: Gael Poux-Medard, Sergio Cobo-Lopez, Jordi Duch, Roger Guimera, Marta Sales-Pardo
Date: 2021-03-09 00:00:00
Tasks: Decision Making, Model Selection

Many studies have shown that there are regularities in the way human beings make decisions. However, our ability to obtain models that capture such regularities and can accurately predict unobserved decisions is still limited. We tackle this problem in the context of individuals who are given information relative to the evolution of market prices and asked to guess the direction of the market. We use a networks inference approach with stochastic block models (SBM) to find the model and network representation that is most predictive of unobserved decisions. Our results suggest that users mostly use recent information (about the market and about their previous decisions) to guess. Furthermore, the analysis of SBM groups reveals a set of strategies used by players to process information and make decisions that is analogous to behaviors observed in other contexts. Our study provides and example on how to quantitatively explore human behavior strategies by representing decisions as networks and using rigorous inference and model-selection approaches.
Optimal Transmission Topology for Facilitating the Growth of Renewable Power Generation
Authors: Emily Little, Sandrine Bortolotti, Jean-Yves Bourmaud, Efthymios Karangelos, Yannick Perez
Date: 2021-03-29 00:00:00

Transmission topology control is a tool used by system operators in the role of a control action taken into account as a preventive or corrective action relative to a specific outage or set of outages. However, their inclusion in most electricity market frameworks is limited. With the increasing penetration of intermittent energy sources, optimal topology can be used as a lever of flexibility to decrease the total system cost. This paper demonstrates the evolution of optimal topology control on systems with increasing quantities of intermittent renewable energy along two axes. First, the effects of the increased variable sources on the variations of optimal topology are explored. Second, we elaborate on the growing advantages of exploiting transmission level grid flexibility in terms of total system cost. Case studies are performed on a modified RTS-96 network.
On data-driven stabilization of systems with quadratic nonlinearities
Authors: Alessandro Luppi, Claudio De Persis, Pietro Tesi
Date: 2021-03-29 00:00:00

In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length experiment. Necessary and sufficient conditions are derived to guarantee that the system is absolutely stabilizable and a controller is designed. Results derived under some relaxed prior information about the system and strengthened data assumptions are also discussed. Numerical examples illustrate the method with different levels of prior information.
Stability analysis of time-delay systems in the parametric space
Authors: Vukan Turkulov, Milan R. Rapaic, Rachid Malti
Date: 2021-03-29 00:00:00

This paper presents a novel method for stability analysis of a wide class of linear, time-delay systems (TDS), including retarded non-neutral ones, as well as those incorporating incommensurate and distributed delays. The proposed method is based on frequency domain analysis and the application of Rouche's theorem. Given a parametrized TDS, and some parametric point for which the number of unstable poles is known, the proposed method is capable of identifying the maximum surrounding region in the parametric space for which the number of unstable poles remains invariant. First, a procedure for investigating stability along a line is developed. Then, the results are extended by the application of Holder's inequality to investigating stability within a region. Contrary to existing approaches, the proposed method is uniformly applicable to parameters of different types (delays, distributed delay limits, time constants, etc.). Efficacy of the proposed method is demonstrated using illustrative examples.
Higher Order Convergent Control Barrier Functions for Leader-Follower Multi-Agent Systems under STL Tasks
Authors: Maryam Sharifi, Dimos V. Dimarogonas
Date: 2021-03-29 00:00:00

This paper presents control strategies based on time-varying convergent higher order control barrier functions for a class of leader-follower multi-agent systems under signal temporal logic (STL) tasks. Each agent is assigned a local STL task which may be dependent on the behavior of agents involved in other tasks. The leader has knowledge on the associated tasks and controls the performance of the subgroup involved agents. Robust solutions for the task satisfaction, based on the leader's accessibility to the follower agents' states are suggested. Our approach finds solutions to guarantee the satisfaction of STL tasks independent of the agents' initial conditions.
Fixed-Time Convergent Control Barrier Functions for Coupled Multi-Agent Systems Under STL Tasks
Authors: Maryam Sharifi, Dimos V. Dimarogonas
Date: 2021-03-01 00:00:00

This paper presents a control strategy based on a new notion of time-varying fixed-time convergent control barrier functions (TFCBFs) for a class of coupled multi-agent systems under signal temporal logic (STL) tasks. In this framework, each agent is assigned a local STL task regradless of the tasks of other agents. Each task may be dependent on the behavior of other agents which may cause conflicts on the satisfaction of all tasks. Our approach finds a robust solution to guarantee the fixed-time satisfaction of STL tasks in a least violating way and independent of the agents' initial condition in the presence of undesired violation effects of the neighbor agents. Particularly, the robust performance of the task satisfactions can be adjusted in a user-specified way.
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization
Authors: Alisa Rupenyan, Mohammad Khosravi, John Lygeros
Date: 2021-03-29 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the increase of productivity in positioning systems. The high-level controller pre-optimizes the input to a low-level cascade controller, using a contouring predictive control approach. This control structure requires tuning of multiple parameters. We propose a sample-efficient tuning algorithm, where the performance metrics associated with the full geometry traversal are modelled as Gaussian processes and used to form the global cost and the constraints in a constrained Bayesian optimization algorithm. This approach enables the trade-off between fast traversal, high tracking accuracy, and suppression of vibrations in the system. The performance improvement is evaluated numerically when tuning different combinations of parameters. We demonstrate that tuning the parameters of the MPC contour-controller achieves the best performance in terms of time, tracking accuracy, and minimization of the vibrations in the system.
A Distributed Scheme for Stability Assessment in Large-Scale Structure-Preserving Models via Singular Perturbation
Authors: Amin Gholami, Xu Andy Sun
Date: 2021-03-29 00:00:00

Assessing small-signal stability of power systems composed of thousands of interacting generators is a computationally challenging task. To reduce the computational burden, this paper introduces a novel condition to assess and certify small-signal stability. Using this certificate, we can see the impact of network topology and system parameters (generators' damping and inertia) on the eigenvalues of the system. The proposed certificate is derived from rigorous analysis of the classical structure-preserving swing equation model and has a physically insightful interpretation related to the generators' parameters and reactive power. To develop the certificate, we use singular perturbation techniques, and in the process, we establish the relationship between the structure-preserving model and its singular perturbation counterpart. As the proposed method is fully distributed and uses only local measurements, its computational cost does not increase with the size of the system. The effectiveness of the scheme is numerically illustrated on the WSCC system.
Stability of Multi-Microgrids: New Certificates, Distributed Control, and Braess's Paradox
Authors: Amin Gholami, Xu Andy Sun
Date: 2021-03-29 00:00:00

This paper investigates the theory of resilience and stability in multi-microgrid networks. We derive new sufficient conditions to guarantee small-signal stability of multi-microgrids in both lossless and lossy networks. The new stability certificate for lossy networks only requires local information, thus leads to a fully distributed control scheme. Moreover, we study the impact of network topology, interface parameters (virtual inertia and damping), and local measurements (voltage magnitude and reactive power) on the stability of the system. The proposed stability certificate suggests the existence of Braess's Paradox in the stability of multi-microgrids, i.e. adding more connections between microgrids could worsen the multi-microgrid system stability as a whole. We also extend the presented analysis to structure-preserving network models, and provide a stability certificate as a function of original network parameters, instead of the Kron reduced network parameters. We provide a detailed numerical study of the proposed certificate, the distributed control scheme, and a coordinated control approach with line switching. The simulation shows the effectiveness of the proposed stability conditions and control schemes in a four-microgrid network, IEEE 33-bus system, and several large-scale synthetic grids.
On Anderson acceleration for partially observable Markov decision processes
Authors: Melike Ermis, MinGyu Park, Insoon Yang
Date: 2021-03-29 00:00:00

This paper proposes an accelerated method for approximately solving partially observable Markov decision process (POMDP) problems offline. Our method carefully combines two existing tools: Anderson acceleration (AA) and the fast informed bound (FIB) method. Adopting AA, our method rapidly solves an approximate Bellman equation with an efficient combination of previous solution estimates. Furthermore, the use of FIB alleviates the scalability issue inherent in POMDPs. We show the convergence of the overall algorithm to the suboptimal solution obtained by FIB. We further consider a simulation-based method and prove that the approximation error is bounded explicitly. The performance of our algorithm is evaluated on several benchmark problems. The results of our experiments demonstrate that the proposed algorithm converges significantly faster without degrading the quality of the solution compared to its standard counterpart.
Anomaly Detection Under Multiplicative Noise Model Uncertainty
Authors: Venkatraman Renganathan, Benjamin J. Gravell, Justin Ruths, Tyler H. Summers
Date: 2021-03-28 00:00:00
Tasks: Anomaly Detection, model

State estimators are crucial components of anomaly detectors that are used to monitor cyber-physical systems. Many frequently-used state estimators are susceptible to model risk as they rely critically on the availability of an accurate state-space model. Modeling errors make it more difficult to distinguish whether deviations from expected behavior are due to anomalies or simply a lack of knowledge about the system dynamics. In this research, we account for model uncertainty through a multiplicative noise framework. Specifically, we propose to use the multiplicative noise LQG based compensator in this setting to hedge against the model uncertainty risk. The size of the residual from the estimator can then be compared against a threshold to detect anomalies. Finally, the proposed detector is validated using numerical simulations. Extension of state-of-the-art anomaly detection in cyber-physical systems to handle model uncertainty represents the main novel contribution of the present work.
A Bulk-Controlled Low-Voltage CMOS Quadrature Oscillator
Authors: R. Picos, M. A. Calafat, K. Suenaga, S. Bota, M. Roca, E. Isern, E. García-Moreno
Date: 2021-03-28 00:00:00

In this paper, an schema for controlling the oscillation frequency of a quadrature oscillator is proposed. The method involves controlling the threshold voltage of the PMOS transistors in the inverter through control of the bulk bias voltage. Results obtained using HSPICE simulation are presented in a technology of 0.35{\mu}m, and experimental results using discrete elements (HEF4007) are also shown. Both sets of experiments show the effectiveness of the technique.
Hidden Markov Model Based Approach for Diagnosing Cause of Alarm Signals
Authors: Joshiba Ariamuthu Venkidasalapathy, Costas Kravaris
Date: 2021-03-28 00:00:00

When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub-sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.
Compressibility of Network Opinion and Spread States in the Laplacian-Eigenvector Basis
Authors: Sandip Roy, Mengran Xue
Date: 2021-03-28 00:00:00

Opinion-evolution and spread processes on networks (e.g., infectious disease spread, opinion formation in social networks) are not only high dimensional but also volatile and multiscale in nature. In this study, we explore whether snapshot data from these processes can admit terse representations. Specifically, using three case studies, we explore whether the data are compressible in the Laplacian-eigenvector basis, in the sense that each snapshot can be approximated well using a (possibly different) small set of basis vectors. The first case study is concerned with a linear consensus model that is subject to a stochastic input at an unknown location; both empirical and formal analyses are used to characterize compressibility. Second, compressibility of state snapshots for a stochastic voter model is assessed via an empirical study. Finally, compressibility is studied for state-level daily COVID-19 positivity-rate data. The three case studies indicate that state snapshots from opinion-evolution and spread processes allow terse representations, which nevertheless capture their rich propagative dynamics.
Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge
Authors: Aayushya Agarwal, Amritanshu Pandey, Larry Pileggi
Date: 2021-03-17 00:00:00
Tasks: valid

Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow. Current commercial tools, however, prioritize speed of convergence over accuracy by relying on initial conditions that are taken from the steady state solution of similar network configurations that are not guaranteed to lie within a convex region of a valid solution. In this paper we introduce a globally convergent algorithm to facilitate fast and accurate AC steady state simulation and optimization based on prior knowledge from similar networks. The approach uses a homotopy method that gradually and efficiently translates a previously known network configuration to the current network configuration. The proposed formulation is highly scalable, and its efficacy is demonstrated for resiliency study and optimization of large networks up to 70k buses.
Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach
Authors: Tianyi Chen, Meng Wang, Siyuan Gong, Yang Zhou, Bin Ran
Date: 2021-03-28 00:00:00

In this study, we propose a rotation-based connected automated vehicle (CAV) distributed cooperative control strategy for an on-ramp merging scenario. By assuming the mainline and ramp line are straight, we firstly design a virtual rotation approach that transfers the merging problem to a virtual car following (CF) problem to reduce the complexity and dimension of the cooperative CAVs merging control. Based on this concept, a multiple-predecessor virtual CF model and a unidirectional multi-leader communication topology are developed to determine the longitudinal behavior of each CAV. Specifically, we exploit a distributed feedback and feedforward longitudinal controller in preparation for actively generating gaps for merging CAVs, reducing the voids caused by merging, and ensuring safety and traffic efficiency during the process. To ensure the disturbance attenuation property of this system, practical string stability is mathematically proved for the virtual CF controllers to prohibit the traffic oscillation amplification through the traffic stream. Moreover, as a provision for extending the virtual CF application scenarios of any curvy ramp geometry, we utilize a curvilinear coordinate to model the two-dimensional merging control, and further design a local lateral controller based on an extended linear-quadratic regulator to regulate the position deviation and angular deviation of the lane centerlines. For the purpose of systematically evaluating the control performance of the proposed methods, numerical simulation experiments are conducted. As the results indicate, the proposed controllers can actively reduce the void and meanwhile guarantee the damping of traffic oscillations in the merging control area.
Definition and Analytical Expression on State Observe Ability for Linear Discrete-time Systems with the Bounded Noise Energy
Authors: Mingwang Zhao
Date: 2021-03-28 00:00:00

In this article, the definition on the observe ability and its relation to the signal detecting performance are studied systematically for the linear discrete-time(LDT) systems. Firstly, to define and analyze the observe ability for the practical systems with the measured noise, six kinds of bounded noise models are classified. For the noise energy bounded case, the observability ellipsoid and the image observability ellipsoid are defined by the state observed error and then a novel concept on the LDT systems, called as the observe ability, is proposed. Based on that, some theorems and properties about the observe ability and the signal detecting performances are given and proven, and then the reason that to maximize the observe ability is to optimize the signal detecting performances is established. Secondly, a dual relation between the observability ellipsoid and the controllability ellipsoid, which volumes and radii are respectively with some inverse relations, is stated and proven. Accordingly, the analytical computing equations for the volume of the two observability ellipsoids are got and some analytical shape factors of these ellipsoids are deconstructed. Based on these effective compting for the volumes, radii, and shape factors, analyzing and optimizing for the observe ability can be carried out. Thirdly, to compare rationally the state observe ability between the different systems or different system parameters, the normalization of the output variables, the state variables, and the system models are discussed. Finally, some numerical experiments and their results show the effectiveness of the computing and comparing methods for the observe ability.
Control Ability with Time Attributy for Linear Continous-time Systems
Authors: Mingwang Zhao
Date: 2021-03-28 00:00:00
Tasks: Attribute

In this paper, the control ability with time attributy for the linear continuous-time (LCT) systems are defined and analyzed by the volume computing for the controllability region. Firstly, a relation theorem about the open-loop control ability, the control strategy space (\textit{i.e.}, the solution space of the input variable for control problems), and the some closed-loop performance for the LCT systems is purposed and proven. This theorem shows us the necessity to optimize the control ability for the practical engineering problems. Secondly, recurssive volume-computing algorithms with the low computing complexities for the finite-time controllability region are discussed. Finally, two analytical volume computations of the infinite-time controllability region for the systems with $n$ different and repeated real eigenvalues are deduced, and then by deconstructing the volume computing equations, 3 classes of the shape factors are constructed. These analytical volume and shape factors can describe accurately the size and shape of the controllability region. Because the time-attribute control ability for LCT systems is directly related to the controllability region with the unit input variables, based on these analytical expressions on the volume and shape factors, the time-attribute control ability can be computed and optimized conveniently.
On the equivalence of contraction and Koopman approaches for nonlinear stability and control
Authors: Bowen Yi, Ian R. Manchester
Date: 2021-03-28 00:00:00

In this paper we prove new connections between two frameworks for analysis and control of nonlinear systems: the Koopman operator framework and contraction analysis. Each method, in different ways, provides exact and global analyses of nonlinear systems by way of linear systems theory. The main results of this paper show equivalence between contraction and Koopman approaches for a wide class of stability analysis and control design problems. In particular: stability or stablizability in the Koopman framework implies the existence of a contraction metric (resp. control contraction metric) for the nonlinear system. Further in certain cases the converse holds: contraction implies the existence of a set of observables with which stability can be verified via the Koopman framework. We provide results for the cases of autonomous and time-varying systems, as well as orbital stability of limit cycles. Furthermore, the converse claims are based on a novel relation between the Koopman method and construction of a Kazantzis-Kravaris-Luenberger observer. We also provide a byproduct of the main results, that is, a new method to learn contraction metrics from trajectory data via linear system identification.