pwc-archive/papers-with-abstracts huggingface dataset:
Reconstructing the Dynamic Directivity of Unconstrained Speech
Authors: Camille Noufi, Dejan Markovic, Peter Dodds
Date: 2022-09-09 00:00:00

This article presents a method for estimating and reconstructing the spatial energy distribution pattern of natural speech, which is crucial for achieving realistic vocal presence in virtual communication settings. The method comprises two stages. First, recordings of speech captured by a real, static microphone array are used to create an egocentric virtual array that tracks the movement of the speaker over time. This virtual array is used to measure and encode the high-resolution directivity pattern of the speech signal as it evolves dynamically with natural speech and movement. In the second stage, the encoded directivity representation is utilized to train a machine learning model that can estimate the full, dynamic directivity pattern given a limited set of speech signals, such as those recorded using the microphones on a head-mounted display. Our results show that neural networks can accurately estimate the full directivity pattern of natural, unconstrained speech from limited information. The proposed method for estimating and reconstructing the spatial energy distribution pattern of natural speech, along with the evaluation of various machine learning models and training paradigms, provides an important contribution to the development of realistic vocal presence in virtual communication settings.
Modeling of Political Systems using Wasserstein Gradient Flows
Authors: Nicolas Lanzetti, Joudi Hajar, Florian Dörfler
Date: 2022-09-12 00:00:00
Tasks: Time Series, Time Series Analysis

The study of complex political phenomena such as parties' polarization calls for mathematical models of political systems. In this paper, we aim at modeling the time evolution of a political system whereby various parties selfishly interact to maximize their political success (e.g., number of votes). More specifically, we identify the ideology of a party as a probability distribution over a one-dimensional real-valued ideology space, and we formulate a gradient flow in the probability space (also called a Wasserstein gradient flow) to study its temporal evolution. We characterize the equilibria of the arising dynamic system, and establish local convergence under mild assumptions. We calibrate and validate our model with real-world time-series data of the time evolution of the ideologies of the Republican and Democratic parties in the US Congress. Our framework allows to rigorously reason about various political effects such as parties' polarization and homogeneity. Among others, our mechanistic model can explain why political parties become more polarized and less inclusive with time (their distributions get "tighter"), until all candidates in a party converge asymptotically to the same ideological position.
Leveraging Conflicting Constraints in Solving Vehicle Routing Problems
Authors: Sabino Francesco Roselli, Remco Vader, Martin Fabian, Knut Akesson
Date: 2021-03-15 00:00:00
Tasks: Combinatorial Optimization

The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is a combinatorial optimization problem of designing routes for vehicles to visit customers such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The CF-EVRP involves constraints such as time windows on the delivery to the customers, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can simultaneously accommodate. In previous work, the compositional algorithm ComSat was introduced and that solves the CF-EVRP by breaking it down into sub-problems and iteratively solve them to build an overall solution. Though ComSat showed good performance in general, some problems took significant time to solve due to the high number of iterations required to find solutions that satisfy the road segments' capacity constraints. The bottleneck is the Path Changing Problem, i.e., the sub-problem of finding a new set of shortest paths to connect a subset of the customers, disregarding previously found shortest paths. This paper presents an improved version of the PathsChanger function to solve the Path Changing Problem that exploits the unsatisfiable core, i.e., information on which constraints conflict, to guide the search for feasible solutions. Experiments show faster convergence to feasible solutions compared to the previous version of PathsChanger.
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control
Authors: Jan-Willem van Wingerden, Sebastiaan Mulders, Rogier Dinkla, Tom Oomen, Michel Verhaegen
Date: 2022-09-12 00:00:00

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable~(IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.
Multiple Control Barrier Functions: An Application to Reactive Obstacle Avoidance for a Multi-steering Tractor-trailer System
Authors: Mohammad Aali, Jun Liu
Date: 2022-09-12 00:00:00
Tasks: Model Predictive Control

Control barrier functions (CBFs) recently introduced a systematic way to guarantee the system's safety through set invariance. Together with a nominal control method, it establishes a safety-critical control mechanism. The resulting safety constraints can be enforced as hard constraints in quadratic programming (QP) optimization, which rectifies the nominal control law based on the set of safe inputs. In this work, we introduce a multiple CBFs scheme which enforces several safety constraints with high relative degrees. This control structure is essential in many challenging robotic applications that need to meet several safety criteria simultaneously. In order to illustrate the capabilities of the proposed method, we have addressed the problem of reactive obstacle avoidance for a class of tractor-trailer systems. Safety is one of the fundamental issues in autonomous tractor-trailer systems design. The lack of fast response due to poor maneuverability makes reactive obstacle avoidance difficult for these systems. We develop a control structure based on a multiple CBFs scheme for a multi-steering tractor-trailer system to ensure a collision-free maneuver for both the tractor and trailer in the presence of several obstacles. Model predictive control is selected as the nominal tracking controller, and the proposed control strategy is tested in several challenging scenarios.
Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach
Authors: Leontine Aarnoudse, Johan Kon, Koen Classens, Max van Meer, Maurice Poot, Paul Tacx, Nard Strijbosch, Tom Oomen
Date: 2022-09-12 00:00:00

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.
Secure state estimation over Markov wireless communication channels (extended version)
Authors: Anastasia Impicciatore, Anastasios Tsiamis, Yuriy Zacchia Lun, Alessandro D'Innocenzo, George J. Pappas
Date: 2022-09-12 00:00:00
Tasks: State Estimation

This note studies state estimation in wireless networked control systems with secrecy against eavesdropping. Specifically, a sensor transmits a system state information to the estimator over a legitimate user link, and an eavesdropper overhears these data over its link independent of the user link. Each connection may be affected by packet losses and is modeled by a finite-state Markov channel (FSMC), an abstraction widely used to design wireless communication systems. This paper presents a novel concept of optimal mean square expected secrecy over FSMCs and delineates the design of a secrecy parameter requiring the user mean square estimation error (MSE) to be bounded and eavesdropper MSE unbounded. We illustrate the developed results on an example of an inverted pendulum on a cart whose parameters are estimated remotely over a wireless link exposed to an eavesdropper.
Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation
Authors: Leontine Aarnoudse, Tom Oomen
Date: 2022-09-12 00:00:00

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both the search direction and step size are determined through system experiments. In particular, the search direction is chosen as an unbiased estimate of the gradient which is obtained from a single experiment, regardless of the size of the MIMO system. The approach is illustrated using a simulation example, in which it is shown to be superior to a deterministic method in terms of convergence speed and thus experimental cost.
Recursive Prediction Error Gradient-Based Algorithms and Framework to Identify PMSM Parameters Online
Authors: Aravinda Perera, Roy Nilsen
Date: 2022-09-12 00:00:00
Tasks: Scheduling

Real-time acquisition of accurate machine parameters is of significance to achieving high performance in electric drives, particularly targeted for mission-critical applications. Unlike the saturation effects, the temperature variations are difficult to predict, thus it is essential to track temperature-dependent parameters online. In this paper, a unified framework is developed for online parameter identification of rotating electric machines, premised on the Recursive Prediction Error Method (RPEM). Secondly, the prediction gradient ($\mathbf{\Psi}^T$)-based RPEM is adopted for identification of the temperature-sensitive parameters, i.e., the permanent magnet flux linkage ($\Psi_m$) and stator-winding resistance ($R_s$) of the Interior Permanent Magnet Synchronous Machine (IPMSM). Three algorithms, namely, Stochastic Gradient (SGA), Gauss-Newton (GNA), and physically interpretative method (PhyInt) are investigated for the estimation gains computation. A speed-dependent gain-scheduling scheme is used to decouple the inter-dependency of $\Psi_m$ and $R_s$. With the aid of offline simulation methods, the main elements of RPEM such as $\mathbf{\Psi}^T$ are analyzed. The concept validation and the choice of the optimal algorithm is made with the use of System-on-Chip (SoC) based Embedded Real-Time Simulator (ERTS). Subsequently, the selected algorithms are validated with the aid of a 3-kW, IPMSM drive where the control and estimation routines are implemented in the SoC-based industrial embedded control system. The experimental results reveal that $\mathbf{\Psi}^T$-based RPEM, in general, can be a versatile technique in temperature-sensitive parameter adaptation both online and offline.
Deployment Optimization of Dual-functional UAVs for Integrated Localization and Communication
Authors: Zheyuan Yang, Suzhi Bi, Ying-Jun Angela Zhang
Date: 2022-09-12 00:00:00

In emergency scenarios, unmanned aerial vehicles (UAVs) can be deployed to assist localization and communication services for ground terminals. In this paper, we propose a new integrated air-ground networking paradigm that uses dual-functional UAVs to assist the ground networks for improving both communication and localization performance. We investigate the optimization problem of deploying the minimal number of UAVs to satisfy the communication and localization requirements of ground users. The problem has several technical difficulties including the cardinality minimization, the non-convexity of localization performance metric regarding UAV location, and the association between user and communication terminal. To tackle the difficulties, we adopt D-optimality as the localization performance metric, and derive the geometric characteristics of the feasible UAV hovering regions in 2D and 3D based on accurate approximation values. We solve the simplified 2D projection deployment problem by transforming the problem into a minimum hitting set problem, and propose a low-complexity algorithm to solve it. Through numerical simulations, we compare our proposed algorithm with benchmark methods. The number of UAVs required by the proposed algorithm is close to the optimal solution, while other benchmark methods require much more UAVs to accomplish the same task.
A New Fuzzy $H_{\infty}$ Filter Design for Nonlinear Time-Delay Systems with Mismatched Premise Membership Functions
Authors: Qianqian Ma, Hongwei Xia, Li Li, Guangcheng Ma
Date: 2022-09-12 00:00:00

This paper is concerned with the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay. To overcome the shortcomings of the conventional methods with matched preconditions, the fuzzy $H_{\infty}$ filter to be designed and the T-S fuzzy model are assumed to have different premise membership functions and number of rules, thus, greater design flexibility and robustness to uncertainty can be achieved. However, such design will also make the derived results conservative, to relax the result, a novel integral inequality which is tighter than the traditional inequalities derived from the Leibniz-Newton formula is applied, besides, a fuzzy Lypunov function and the information of the membership functions are also introduced. All the design methods are presented in LMI-based conditions. Finally, two numerical examples are given to prove the effectiveness and superiority of the proposed approach.
Equivariant Filter Design for Discrete-time systems
Authors: Yixiao Ge, Pieter van Goor, Robert Mahony
Date: 2022-09-12 00:00:00

The kinematics of many nonlinear control systems, especially in the robotics field, admit a transitive Lie-group symmetry, which is useful in high performance observer design. The recently proposed equivariant filter (EqF) exploits equivariance to generate high performance filters for a wide range of real-world systems. However, existing work on the equivariant filter, and equivariance of control systems in general, is based on a continuous-time formulation. In this paper, we first present the equivariant structure of a discrete-time system. We then use this to propose a discrete-time version of the equivariant filter. A novelty of the approach is that the geometry of the symmetry group naturally appears as parallel transport in the reset step of the filter. Preliminary results for linear second order kinematics with separate bearing and range measurements indicate that the discrete EqF significantly outperforms both a discretized version of the continuous EqF and a classical discrete EKF.
Operational and Economy-Wide Impacts of Compound Cyberattacks and Extreme Weather Events on Electric Power Networks
Authors: Charalampos Avraam, Luis Ceferino, Yury Dvorkin
Date: 2022-09-11 00:00:00
Tasks: Bilevel Optimization

The growing frequencies of extreme weather events and cyberattacks give rise to a novel threat where a malicious cyber actor aims to disrupt stressed components of critical infrastructure systems immediately before, during, or shortly after an extreme weather event. In this paper, we initiate the study of Compound Cyber-Physical Threats and develop a two-stage framework for the analysis of operational disruptions in electric power networks and economy-wide impacts under three scenarios: a Heatwave, a Cyberattack, and a Compound scenario when the Cyberattack is timed with the Heatwave. In the first stage, we use a bilevel optimization problem to represent the adversarial rationale of a cyberattacker in the upper level. In the lower level, we model disruptions in the electric power network using an optimal power flow model. In the second stage, we couple the disruption of electricity supply with a Computable General Equilibrium model to elucidate the impacts on all economic sectors. For the New York Independent System Operator, we find that a 9% demand increase in a Heatwave may not lead to unserved load. The Cyberattack can lead to 4% of unserved electric load in Long Island, while the Compound scenario can increase unserved electric load in Long Island to 13% and affect almost 600,000 customers. Our results show that the activity of state and local government enterprises can decrease by 30% in the Compound scenario. We conclude that the vulnerability of federal, state, and local government enterprises to electricity disruptions can affect a broad range of populations.
Two-Stage Boost Inverter for Wave Energy Conversion
Authors: Souvik Datta, Sriramalakshmi P
Date: 2022-09-11 00:00:00
Tasks: Vocal Bursts Valence Prediction

Mitigation of fossil fuel dependency is becoming a major concern in most of the countries around the globe. In that context, this research article presents the single phase two stage boost inverter system for wave energy conversion. The proposed wave energy based power conditioning system includes wave energy based functional block, permanent magnet synchronous generator, zeta converter, single phase H-bridge inverter and a resistive load. The boost inverter is used to boost up the voltage availed from the permanent magnet generator powered by a wave energy converter system using zeta converter and then invert it using an H-bridge inverter. Hence variable magnitude AC voltage is converted to constant magnitude AC voltage at constant frequency. The blocks of each component is designed to deliver the power of 1kW at the load terminals and simulation study is carried out in the MATLAB / Simulink platform for further analysis. The simulation results are presented and discussed to show how wave powered boost inverter can efficiently convert the wave energy to electrical energy and condition the electrical power before feeding it to any AC load or AC grid. The performance analysis of wave energy conversion system based on zeta converter is included at the end of the article
Social Shaping of Dynamic Multi-Agent Systems over a Finite Horizon
Authors: Zeinab Salehi, Yijun Chen, Ian R. Petersen, Elizabeth L. Ratnam, Guodong Shi
Date: 2022-09-10 00:00:00

This paper studies self-sustained dynamic multiagent systems (MAS) for decentralized resource allocation operating at a competitive equilibrium over a finite horizon. The utility of resource consumption, along with the income from resource exchange, forms each agent's payoff which is aimed to be maximized. Each utility function is parameterized by individual preferences which can be designed by agents independently. By shaping these preferences and proposing a set of utility functions, we can guarantee that the optimal resource price at the competitive equilibrium always remains socially acceptable, i.e., it never violates a given threshold that indicates affordability. First, we show this problem is solvable at the conceptual level under some convexity assumptions. Then, as a benchmark case, we consider quadratic MAS and formulate the associated social shaping problem as a multi-agent LQR problem which enables us to propose explicit utility sets using quadratic programming and dynamic programming. Finally, a numerical algorithm is presented for calculating the range of the preference function parameters which guarantee a socially accepted price. Some illustrative examples are given to examine the effectiveness of the proposed methods.
Barrier Pairs for Safety Control of Uncertain Output Feedback Systems
Authors: Binghan He, Takashi Tanaka
Date: 2022-09-10 00:00:00

The barrier function method for safety control typically assumes the availability of full state information. Unfortunately, in many scenarios involving uncertain dynamical systems, full state information is often unavailable. In this paper, we aim to solve the safety control problem for an uncertain single-input single-output system with partial state information. First, we develop a synthesis method that simultaneously creates a barrier function and a dynamic output feedback safety controller. This safety controller guarantees that the unit sub-level set of the barrier function is an invariant set under the uncertain dynamics and disturbances of the system. Then, we build an identifier-based estimator that provides a state estimate affine to the uncertain model parameters of the system. To detect the potential risks of the system, a fault detector uses the state estimate to find an upper bound for the barrier function. The fault detector triggers the safety controller when the system's original action leads to a potential safety issue and resumes the original action when the potential safety issue is resolved by the safety controller.
Data-Driven Affinely Adjustable Robust Volt/VAr Control
Authors: Naihao Shi, Rui Cheng, LiMing Liu, Zhaoyu Wang, Qianzhi Zhang
Date: 2022-09-10 00:00:00

This paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.