pwc-archive/papers-with-abstracts huggingface dataset:
Magnitude-Corrected and Time-Aligned Interpolation of Head-Related Transfer Functions
Authors: Johannes M. Arend, Christoph Pörschmann, Stefan Weinzierl, Fabian Brinkmann
Date: 2023-03-17 00:00:00

Head-related transfer functions (HRTFs) are essential for virtual acoustic realities, as they contain all cues for localizing sound sources in three-dimensional space. Acoustic measurements are one way to obtain high-quality HRTFs. To reduce measurement time, cost, and complexity of measurement systems, a promising approach is to capture only a few HRTFs on a sparse sampling grid and then upsample them to a dense HRTF set by interpolation. However, HRTF interpolation is challenging because small changes in source position can result in significant changes in the HRTF phase and magnitude response. Previous studies greatly improved the interpolation by time-aligning the HRTFs in preprocessing, but magnitude interpolation errors, especially in contralateral regions, remain a problem. Building upon the time-alignment approaches, we propose an additional post-interpolation magnitude correction derived from a frequency-smoothed HRTF representation. Employing all 96 individual simulated HRTF sets of the HUTUBS database, we show that the magnitude correction significantly reduces interpolation errors compared to state-of-the-art interpolation methods applying only time alignment. Our analysis shows that when upsampling very sparse HRTF sets, the subject-averaged magnitude error in the critical higher frequency range is up to 1.5 dB lower when averaged over all directions and even up to 4 dB lower in the contralateral region. As a result, the interaural level differences in the upsampled HRTFs are considerably improved. The proposed algorithm thus has the potential to further reduce the minimum number of HRTFs required for perceptually transparent interpolation.
Reproduction number of SARS-CoV-2 Omicron variants, China, December 2022-January 2023
Authors: Yuan Bai, Zengyang Shao, Xiao Zhang, Ruohan Chen, Lin Wang, Sheikh Taslim Ali, Tianmu Chen, Eric H. Y. Lau, Dong-Yan Jin, Zhanwei Du
Date: 2023-03-19 00:00:00

China adjusted the zero-COVID strategy in late 2022, triggering an unprecedented Omicron wave. We estimated the time-varying reproduction numbers of 32 provincial-level administrative divisions from December 2022 to January 2023. We found that the pooled estimate of initial reproduction numbers is 4.74 (95% CI: 4.41, 5.07).
Fitting Low-rank Models on Egocentrically Sampled Partial Networks
Authors: Angus Chan, Tianxi Li
Date: 2023-03-09 00:00:00
Tasks: Link Prediction

The statistical modeling of random networks has been widely used to uncover interaction mechanisms in complex systems and to predict unobserved links in real-world networks. In many applications, network connections are collected via egocentric sampling: a subset of nodes is sampled first, after which all links involving this subset are recorded; all other information is missing. Compared with the assumption of ``uniformly missing at random", egocentrically sampled partial networks require specially designed modeling strategies. Current statistical methods are either computationally infeasible or based on intuitive designs without theoretical justification. Here, we propose an approach to fit general low-rank models for egocentrically sampled networks, which include several popular network models. This method is based on graph spectral properties and is computationally efficient for large-scale networks. It results in consistent recovery of missing subnetworks due to egocentric sampling for sparse networks. To our knowledge, this method offers the first theoretical guarantee for egocentric partial network estimation in the scope of low-rank models. We evaluate the technique on several synthetic and real-world networks and show that it delivers competitive performance in link prediction tasks.
Global Performance Guarantees for Localized Model Predictive Control
Authors: Jing Shuang Li, Carmen Amo Alonso
Date: 2023-03-20 00:00:00
Tasks: model, Model Predictive Control

Recent advances in model predictive control (MPC) leverage local communication constraints to produce localized MPC algorithms whose complexities scale independently of total network size. However, no characterization is available regarding global performance, i.e. whether localized MPC (with communication constraints) performs just as well as global MPC (no communication constraints). In this paper, we provide analysis and guarantees on global performance of localized MPC -- in particular, we derive sufficient conditions for optimal global performance in the presence of local communication constraints. We also present an algorithm to determine the communication structure for a given system that will preserve performance while minimizing computational complexity. The effectiveness of the algorithm is verified in simulations, and additional relationships between network properties and performance-preserving communication constraints are characterized. A striking finding is that in a network of 121 coupled pendula, each node only needs to communicate with its immediate neighbors to preserve optimal global performance. Overall, this work offers theoretical understanding on the effect of local communication on global performance, and provides practitioners with the tools necessary to deploy localized model predictive control by establishing a rigorous method of selecting local communication constraints. This work also demonstrates -- surprisingly -- that the inclusion of severe communication constraints need not compromise global performance.
Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach
Authors: Max Bolderman, Mircea Lazar, Hans Butler
Date: 2023-03-20 00:00:00

Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (inverse) model identification error on the resulting tracking error is only analyzed a posteriori in experiments. Therefore, in this work, we develop an approach to feedforward control design that aims at minimizing the tracking error a priori. To achieve this, we present a model of the system in a lifted space of trajectories, based on which we derive an upperbound on the reference tracking performance. Minimization of this bound yields a feedforward control-oriented system identification cost function, and a finite-horizon optimization to compute the feedforward control signal. The nonlinear feedforward control design method is validated using physics-guided neural networks on a nonlinear, nonminimum phase mechatronic example, where it outperforms linear ILC.
Time- versus event-triggered consensus of a single-integrator multi-agent system
Authors: David Meister, Frank Aurzada, Mikhail A. Lifshits, Frank Allgöwer
Date: 2023-03-20 00:00:00

Event-triggered control has shown the potential for providing improved control performance at the same average sampling rate when compared to time-triggered control. While this observation motivates numerous event-triggered control schemes, proving it from a theoretical perspective has only been achieved for a limited number of settings. Inspired by existing performance analyses for the single-loop case, we provide a first fundamental performance comparison of time- and event-triggered control in a multi-agent consensus setting. For this purpose, we consider undirected connected network topologies without communication delays, a level-triggering rule for event-triggered control, and the long-term average of the quadratic deviation from consensus as a performance measure. The main finding of our analysis is that time-triggered control provably outperforms event-triggered control beyond a certain number of agents in our particular setting. We thereby provide an illustrative distributed problem setup in which event-triggered control results in a performance disadvantage when compared to time-triggered control in the case of large networks. Moreover, we derive the asymptotic order of the performance measure under both triggering schemes which gives more insights into the cost relationship for large numbers of agents. Thus, by presenting an analysis for a particular setup, this work points out that transferring an event-triggering scheme from the single-loop to the multi-agent setting can lead to a loss of the often presumed superiority of event-triggered control over time-triggered control. In particular, the design of performant decentralized event-triggering schemes can therefore pose additional challenges when compared to the analogue single-loop case.
Once upon a time step: A closed-loop approach to robust MPC design
Authors: Anilkumar Parsi, Marcell Bartos, Amber Srivastava, Sebastien Gros, Roy S. Smith
Date: 2023-03-20 00:00:00
Tasks: LEMMA, Model Predictive Control

A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization. Necessary and sufficient conditions are derived for recursive feasibility, based on the effects of model perturbations and disturbances occurring at one time step. Using these conditions and Farkas' lemma, sufficient conditions suitable for design are formulated. The proposed method is called a closed-loop design, as only the existence of feasible inputs at the next time step is enforced by design. This is in contrast to most existing formulations, which compute control policies that are feasible under the worst-case realizations of all model perturbations and exogenous disturbances in the MPC prediction horizon. The proposed method has an online computational complexity similar to nominal MPC methods while preserving guarantees of constraint satisfaction, recursive feasibility and stability. Numerical simulations demonstrate the efficacy of our proposed approach.
An analysis of $\mathbb{P}$-invariance and dynamical compensation properties from a control perspective
Authors: Akram Ashyani, Yu-Heng Wu, Huan-Wei Hsu, Torbjörn E. M. Nordling
Date: 2023-03-20 00:00:00

Dynamical compensation (DC) provides robustness to parameter fluctuations. As an example, DC enable control of the functional mass of endocrine or neuronal tissue essential for controlling blood glucose by insulin through a nonlinear feedback loop. Researchers have shown that DC is related to structural unidentifiability and $\mathbb{P}$-invariance property, and $\mathbb{P}$-invariance property is a sufficient and necessary condition for the DC property. In this article, we discuss DC and $\mathbb{P}$-invariancy from an adaptive control perspective. An adaptive controller is a self-tuning controller used to compensate for changes in a dynamical system. To design an adaptive controller with the DC property, it is easier to start with a two-dimensional dynamical model. We introduce a simplified system of ordinary differential equations (ODEs) with the DC property and extend it to a general form. The value of the ideal adaptive control lies in developing methods to synthesize DC to variations in multiple parameters. Then we investigate the stability of the system with time-varying input and disturbance signals, with a focus on the system's $\mathbb{P}$-invariance properties. This study provides phase portraits and step-like response graphs to visualize the system's behavior and stability properties.
Quantifying the Value of Preview Information for Safety Control
Authors: Zexiang Liu, Necmiye Ozay
Date: 2023-03-19 00:00:00
Tasks: Autonomous Vehicles

Safety-critical systems, such as autonomous vehicles, often incorporate perception modules that can anticipate upcoming disturbances to system dynamics, expecting that such preview information can improve the performance and safety of the system in complex and uncertain environments. However, there is a lack of formal analysis of the impact of preview information on safety. In this work, we introduce a notion of safety regret, a properly defined difference between the maximal invariant set of a system with finite preview and that of a system with infinite preview, and show that this quantity corresponding to finite-step preview decays exponentially with preview horizon. Furthermore, algorithms are developed to numerically evaluate the safety regret of the system for different preview horizons. Finally, we demonstrate the established theory and algorithms via multiple examples from different application domains.
Topology, Analysis, and Modulation Strategy of a Fully Controlled Modular Battery Pack with Interconnected Output Ports
Authors: Nima Tashakor, Jan Kacetl, Davood Keshavarzi, Stefan Goetz
Date: 2023-03-19 00:00:00

Modular battery integrated converters or so called dynamically reconfigurable battery packs are expanding into emerging applications, including electromobility and grid storage. Although they offer many degrees of freedom, the state of the art focuses on single output systems and mostly neglects potential of such systems in generating multiple controlled outputs. This paper investigates the use of the extra degrees of freedom that a reconfigurable battery offers to generate multiple outputs for the adjustable supply of various independent loads. The technology offers the potential for functional integration, exploiting already existing transistors, and for a reduction of separate dedicated power conversion stages. Interleaved output ports, where some modules are shared among different outputs, can further reduce the necessary power electronics and cost. However, using conventional modulation techniques in particular phase-shifted carrier modulation can be challenging with shared modules among multiple ports, and different control objectives can adversely impact the overall performance. Therefore, this paper proposes a strategy to decouple the control of multiple ports to enable further simplification of required power electronics. The proposed system does not require any additional active switches for the isolated port and can operate with a wide range of output voltages. Simulation and experiments verify the developed analysis.
Direct data-driven state-feedback control of general nonlinear systems
Authors: Chris Verhoek, Patrick J. W. Koelewijn, Sofie Haesaert, Roland Tóth
Date: 2023-03-19 00:00:00
Tasks: LEMMA, Scheduling

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc.
Tracking performance of PID for nonlinear stochastic systems
Authors: Cheng Zhao, Shuo Yuan
Date: 2023-03-19 00:00:00

In this paper, we will consider a class of continuous-time stochastic control systems with both unknown nonlinear structure and unknown disturbances, and investigate the capability of the classical proportional-integral-derivative(PID) controller in tracking time-varying reference signals. First, under some suitable conditions on system nonlinear functions, reference signals, and unknown disturbances, we will show that PID controllers can be designed to globally stabilize such systems and ensure the boundedness of the tracking error. Analytic design formulae for PID gain matrices are also provided, which only involve some prior knowledge of the partial derivatives of system structural nonlinear functions. Besides, it will be shown that the steady-state tracking error hinges on three critical factors: i) the change rate of reference signals and external disturbances; ii) the intensity of random noises; iii) the selection of PID gains, and can be made arbitrarily small by choosing PID gains suitably large. Finally, by introducing a desired transient process which is shaped from the reference signal, we will present a new PID tuning rule, which can guarantee both nice steady-state and superior transient control performances.
Robust two-degrees-of-freedom control of hydraulic drive with remote wireless operation
Authors: Riccardo Checchin, Michael Ruderman, Roberto Oboe
Date: 2023-03-18 00:00:00
Tasks: Position

In this paper, a controller design targeting the remotely operated hydraulic drive system is presented. A two-degrees-of-freedom PID position controller is used, which is designed so that to maximize the integral action under robust constraint. A linearized model of the system plant, affected by the parameters uncertainties such as variable communication time-delay and overall system gain, is formulated and serves for the control design and analysis. The performed control synthesis and evaluation are targeting the remote operation where the wireless communication channel cannot secure a deterministic real-time of the control loop. The provided analysis of uncertainties makes it possible to ensure system stability under proper conditions. The theoretically expected results are confirmed through laboratory experiments on the standard industrial hydraulic components.
Ensemble Nonlinear Model Predictive Control for Residential Solar-Battery Energy Management
Authors: Yang Li, D. Mahinda Vilathgamuwa, Daniel E. Quevedo, Chih Feng Lee, Changfu Zou
Date: 2023-03-18 00:00:00
Tasks: energy management, Management, Model Predictive Control

In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange. Providing a schedule of this bidirectional power dispatch can facilitate the operational planning for the grid operator and bring additional benefits to the prosumers with some economic incentives. However, the major obstacle to achieving this win-win situation is the difficulty in 1) predicting the nonlinear behaviors of battery degradation under unknown operating conditions and 2) addressing the highly uncertain generation/load patterns, in a computationally viable way. This paper thus establishes a robust short-term dispatch framework for residential prosumers equipped with rooftop solar photovoltaic panels and household batteries. The objective is to achieve the minimum-cost operation under the dynamic distribution energy market environment with stipulated dispatch rules. A general nonlinear optimization problem is formulated, taking into consideration the operating costs due to electricity trading, battery degradation, and various operating constraints. The optimization problem is solved in real-time using a proposed ensemble nonlinear model predictive control-based economic dispatch strategy, where the uncertainty in the forecast has been addressed adequately albeit with limited local data. The effectiveness of the proposed algorithm has been validated using real-world prosumer datasets.
Covariance Steering for Systems Subject to Unknown Parameters
Authors: Jacob Knaup, Panagiotis Tsiotras
Date: 2023-03-18 00:00:00

This work considers the optimal covariance steering problem for systems subject to both additive noise and uncertain parameters which may enter multiplicatively with the state and the control. The unknown parameters are modeled as a constant random variable sampled from a distribution with known moments. The optimal covariance steering problem is formulated using a moment-based representation of the system dynamics, which includes dependence between the unknown parameters and future states, and is solved using sequential convex programming. The proposed approach is demonstrated numerically using a holonomic spacecraft system and an autonomous vehicle control application.
Adaptive Modified RISE-based Quadrotor Trajectory Tracking with Actuator Uncertainty Compensation
Authors: Krishna Bhavithavya Kidambi, Madhur Tiwari, Emmanuel Ogbanje Ijoga, William MacKunis
Date: 2023-03-17 00:00:00

This paper presents an adaptive robust nonlinear control method, which achieves reliable trajectory tracking control for a quadrotor unmanned aerial vehicle in the presence of gyroscopic effects, rotor dynamics, and external disturbances. Through novel mathematical manipulation in the error system development, the quadrotor dynamics are expressed in a control-oriented form, which explicitly incorporates the uncertainty in the gyroscopic term and control actuation term. An adaptive robust nonlinear control law is then designed to stabilize both the position and attitude loops of the quadrotor system. A rigorous Lyapunov-based analysis is utilized to prove asymptotic trajectory tracking, where the region of convergence can be made arbitrarily large through judicious control gain selection. Moreover, the stability analysis formally addresses gyroscopic effects and actuator uncertainty. To illustrate the performance of the control law, comparative numerical simulation results are provided, which demonstrate the improved closed-loop performance achieved under varying levels of parametric uncertainty and disturbance magnitudes.
Online Linear Quadratic Tracking with Regret Guarantees
Authors: Aren Karapetyan, Diego Bolliger, Anastasios Tsiamis, Efe C. Balta, John Lygeros
Date: 2023-03-17 00:00:00

Online learning algorithms for dynamical systems provide finite time guarantees for control in the presence of sequentially revealed cost functions. We pose the classical linear quadratic tracking problem in the framework of online optimization where the time-varying reference state is unknown a priori and is revealed after the applied control input. We show the equivalence of this problem to the control of linear systems subject to adversarial disturbances and propose a novel online gradient descent based algorithm to achieve efficient tracking in finite time. We provide a dynamic regret upper bound scaling linearly with the path length of the reference trajectory and a numerical example to corroborate the theoretical guarantees.
Online Control Synthesis for Uncertain Systems under Signal Temporal Logic Specifications
Authors: Pian Yu, Yulong Gao, Frank J. Jiang, Karl H. Johansson, Dimos V. Dimarogonas
Date: 2021-03-16 00:00:00

This paper studies the online control synthesis problem for uncertain discrete-time systems subject to signal temporal logic (STL) specifications. Different from existing techniques, this work proposes an approach based on STL, reachability analysis, and temporal logic trees. Firstly, a real-time version of STL semantics and a tube-based temporal logic tree (tTLT) are proposed. We show that the tTLT is an underapproximation for the STL formula, in the sense that a trajectory satisfying an tTLT also satisfies the corresponding STL formula. Secondly, an online control synthesis algorithm is designed. It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tTLT, it is guaranteed that the trajectory generated by the control synthesis algorithm satisfies the STL formula. The effectiveness of the proposed approach is verified by a simulation example and a practical experiment.