pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Reachability and Invariance Computation of Stochastic Systems using Linear Programming
Authors: Niklas Schmid, John Lygeros
Date: 2022-11-14 00:00:00

We consider the safety evaluation of discrete time, stochastic systems over a finite horizon. Therefore, we discuss and link probabilistic invariance with reachability as well as reach-avoid problems. We show how to efficiently compute these quantities using dynamic and linear programming.
ADRC in Output and Error Form: Connection, Equivalence, Performance
Authors: Rafal Madonski, Gernot Herbst, Momir Stankovic
Date: 2022-11-14 00:00:00
Tasks: Form

In this work, we investigate two specific linear ADRC structures, namely output- and error-based. The former is considered a 'standard' version of ADRC, a title obtained primarily thanks to its simplicity and effectiveness, which have spurred its adoption across multiple industries. The latter is found to be especially appealing to practitioners as its feedback error-driven structure bares similarities to conventional control solutions, like PI and PID. In this paper, we describe newly found connections between the two considered ADRC structures, which allowed us to formally establish conditions for their equivalence. Furthermore, the conducted comprehensive performance comparison between output- and error-based ADRCs has facilitated the identification of specific modules within them, which can now be conveniently used as building blocks, thus aiding the control designers in customizing ADRC-based solutions and making them most suitable for their applications.
Low-Profile Dual Band-pass Frequency Selective Surface with independent bands of operation
Authors: Komlan Payne
Date: 2022-11-14 00:00:00

A low-profile, modified Complementary Frequency Selective Surface (CFSS) with dual band-pass characteristic is presented. This technique adds independent control of the operation bands, which was a limitation from previous FSSs design based on CFSS concept. The FSS structure utilizes resonant elements and interaction between the layers to deliverer the first operating bandpass response at a low frequency. It is demonstrated that the first order response can achieved miniaturized periodic dimensions of about $\lambda_l/15$ and ultra-thin overall thickness compressed down to $\lambda_l/200$, where $\lambda_l$ is the free space wavelength at the lowest band of operation. An equivalent circuit model (ECM) is presented to explain the working principles of the proposed FSS. This investigation is further supported by parametric studies based on full wave simulation showing how this simple approach allows decoupling of the two bands of operation. A prototype, simultaneously operating at S and C bands is fabricated and tested in a free space environment for validation purpose.
Privacy and Security in Network Controlled Systems via Dynamic Masking
Authors: Mohamed Abdalmoaty, Sribalaji C. Anand, André M. H. Teixeira
Date: 2022-11-14 00:00:00

In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system dynamics (privacy) using system identification techniques, and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. However, using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates of the adversary: thus providing privacy of the system parameters, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture.
Tuning and Implementation Variants of Discrete-Time ADRC
Authors: Gernot Herbst, Rafal Madonski
Date: 2022-11-14 00:00:00

Practical implementations of active disturbance rejection control (ADRC) will almost always take place in discretized form. Since applications may have quite different needs regarding their discrete-time controllers, this article summarizes and extends the available set of ADRC implementations to provide a suitable variant for as many as possible use cases. In doing so, the gap between quasi-continuous and discrete-time controller tuning is being closed for applications with low sampling frequencies. The main contribution of this article is the derivation of three different discrete-time implementations of error-based ADRC. It is shown that these are almost one-to-one counterparts of existing output-based implementations, to the point where transfer functions and coefficients can be reused in unaltered form. In this way, error-based implementations become firmly rooted in the established landscape of discrete-time ADRC. Furthermore, it becomes possible to equip error-based variants with windup protection abilities known from output-based ADRC.
Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control
Authors: Sophie Hall, Lukas Ortmann, Miguel Picallo, Florian Dörfler
Date: 2022-11-14 00:00:00
Tasks: Model Predictive Control

Medical drug infusion problems pose a combination of challenges such as nonlinearities from physiological models, model uncertainty due to inter- and intra-patient variability, as well as strict safety specifications. With these challenges in mind, we propose a novel real-time Nonlinear Model Predictive Control (NMPC) scheme based on projected gradient descent iterations. At each iteration, a small number of steps along the gradient of the NMPC cost is taken, generating a suboptimal input which asymptotically converges to the optimal input. We retrieve classical Lyapunov stability guarantees by performing a sufficient number of gradient iterations until fulfilling a stopping criteria. Such a real-time control approach allows for higher sampling rates and faster feedback from the system which is advantageous for the class of highly variable and uncertain drug infusion problems. To demonstrate the controller's potential, we apply it to hypnosis control in anesthesia of two interacting drugs. The controller successfully regulates hypnosis even under disturbances and uncertainty and fulfils benchmark performance criteria.
Data-Driven Inverse of Linear Systems and Application to Disturbance Observers
Authors: Yongsoon Eun, Jaeho Lee, Hyungbo Shim
Date: 2022-11-14 00:00:00

This work develops a data-based construction of inverse dynamics for LTI systems. Specifically, the problem addressed here is to find an input sequence from the corresponding output sequence based on pre-collected input and output data. The problem can be considered as a reverse of the recent use of behavioral approach, in which the output sequence is obtained for a given input sequence by solving an equation formed by pre-collected data. The condition under which the problem gives a solution is investigated and turns out to be L-delay invertibility of the plant and a certain degree of persistent excitation of the data input. The result is applied to form a data-driven disturbance observer. The plant dynamics augmented by the data-driven disturbance observer exhibits disturbance rejection without the model knowledge of the plant.
Tracking control on homogeneous spaces: the Equivariant Regulator (EqR)
Authors: Matthew Hampsey, Pieter van Goor, Robert Mahony
Date: 2022-11-13 00:00:00

Accurate tracking of planned trajectories in the presence of perturbations is an important problem in control and robotics. Symmetry is a fundamental mathematical feature of many dynamical systems and exploiting this property offers the potential of improved tracking performance. In this paper, we investigate the tracking problem for systems on homogeneous spaces, manifolds which admit symmetries with transitive group actions. We show that there is natural manner to lift any desired trajectory of such a system to a lifted trajectory on the symmetry group. This construction allows us to define a global tracking error and apply LQR design to obtain an approximately optimal control in a single coordinate chart. The resulting control is then applied to the original plant and shown to yield excellent tracking performance. We term the resulting design methodology the Equivariant Regulator (EqR). We provide an example system posed on a homogeneous space, derive the trajectory linearisation in error coordinates and demonstrate the effectiveness of EqR compared to standard approaches in simulation.
A note on power allocation for optimal capacity
Authors: Shravan Mohan
Date: 2022-11-13 00:00:00

The problems of determining the optimal power allocation, within maximum power bounds, to (i) maximize the minimum Shannon capacity, and (ii) minimize the weighted latency are considered. In the first case, the global optima can be achieved in polynomial time by solving a sequence of linear programs (LP). In the second case, the original non-convex problem is replaced by a convex surrogate (a geometric program), using a functional approximation. Since the approximation error is relatively low, the optima of the surrogate is close to the global optimal point of the original problem. In either cases, there is no assumption on the SINR range. The use of LPs and geometric programming make the proposed algorithms numerically efficient. Computations are provided for corroboration.
Control-orientation, conservation of mass and model-based control of compressible fluid networks
Authors: Sven Brüggemann, Robert H. Moroto, Robert R. Bitmead
Date: 2022-11-13 00:00:00

We study a gas network flow regulation control problem showing the closed-loop consequences of using interconnected component models, which have been designed to preserve a variant of mass flow conservation without the inclusion of algebraic constraints into the dynamics. These are candidate \textit{control-oriented} models because they are linear state-space systems. This leads to a study of mass conservation in flow models and the inheritance of conservation at the network level when present at each component. Conservation is expressed as a transfer function property at DC. This property then is shown to imply the existence of integrators and other DC structure of the network model, which has important consequences for the subsequent control design. An example based on an industrial system is used to explore the facility of moving from modeling to automated interconnection or components to model reduction to digital controller design and performance evaluation. Throughout, the focus is on the teasing out of control orientation in modeling. The example shows a strong connection between the modeling and the controller design.
A Compendium of Control-Oriented Models of Gas Processing Equipment Components
Authors: Sven Brüggemann, Robert H. Moroto, Robert R. Bitmead
Date: 2022-11-13 00:00:00
Tasks: State Space Models

This work provides linear control-oriented state space models of gas flow through standard equipment, such as valves, compressors, manifolds and non-trivial pipe geometries. After presenting the catalog of components, we show how to interconnect them: we briefly recall the matrix methodology derived elsewhere, accompanied by a Matlab example for a gas loop; secondly for the same example we provide an alternative approach using Matlab's connect function. All the aforementioned models are suited to model-based MIMO control design tools as they are in state-space form and they can be physically parametrized. Conservation of mass is the source of the algebraic constraints in and is a property subsumed in these new models. This is characterized by model transfer function properties at frequency zero. Each of the component models is shown to posses this property.
Quadrature Control-Bounded ADCs
Authors: Hampus Malmberg, Fredrik Feyling, Jose M de la Rosa
Date: 2022-11-12 00:00:00

In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated by considering band-pass analog-to-digital conversion. We show how a low-pass control-bounded analog-to-digital converter can be translated into a band-pass version where the guaranteed stability, converter bandwidth, and signal-to-noise ratio are preserved while the center frequency for conversion can be positioned freely. The proposed converter is validated with behavioral simulations for a variety of filter orders, notch-filter frequencies, and oversampling ratios. Finally, robustness against component variations is demonstrated by Monte Carlo simulations.
Calibrating Control-Bounded ADCs
Authors: Hampus Malmberg, Till Mettler, Thomas Burger, Fredrik Feyling, Hans-Andrea Loeliger
Date: 2022-11-12 00:00:00

The paper considers the calibration of control-bounded analog-to-digital converters. It is demonstrated that variations of the analog frontend can be addressed by calibrating the digital estimation filter. In simulations (both behavioral and transistor level) of a leapfrog analog frontend, the proposed calibration method restores essentially the nominal performance. Moreover, with digital-filter calibration in mind, the paper reformulates the design problem of control-bounded converters and thereby clarifies the role of sampling, desired filter shape, and nominal conversion error.
Economic Potential for Hybrid Electric Vehicles in Urban Signal-free Intersections with Decentralized MPC
Authors: Kai Tang, Weijie Wang, Xiao Pan, Boli Chen, Simos A. Evangelou
Date: 2022-11-12 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper provides a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control, which is able to ensure a rapid search and unique solution in real time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results show that the traveling cost of sHEVs approaches and even under some circumstances reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.
Stackelberg Meta-Learning Based Control for Guided Cooperative LQG Systems
Authors: Yuhan Zhao, Quanyan Zhu
Date: 2022-11-11 00:00:00
Tasks: Meta-Learning

Guided cooperation allows intelligent agents with heterogeneous capabilities to work together by following a leader-follower type of interaction. However, the associated control problem becomes challenging when the leader agent does not have complete information about follower agents. There is a need for learning and adaptation of cooperation plans. To this end, we develop a meta-learning-based Stackelberg game-theoretic framework to address the challenges in the guided cooperative control for linear systems. We first formulate the guided cooperation between agents as a dynamic Stackelberg game and use the feedback Stackelberg equilibrium as the agent-wise cooperation strategy. We further leverage meta-learning to address the incomplete information of follower agents, where the leader agent learns a meta-response model from a prescribed set of followers offline and adapts to a new coming cooperation task with a small amount of learning data. We use a case study in robot teaming to corroborate the effectiveness of our framework. Comparison with other learning approaches also shows that our learned cooperation strategy provides better transferability for different cooperation tasks.
Toward Models of Impact and Recovery of the US Western Grid from Earthquake Events
Authors: Riley Weinmann, Eduardo Cotilla-Sanchez, Ted K. A. Brekken
Date: 2022-11-11 00:00:00

A Cascadia Subduction Zone (CSZ) earthquake will cause widespread damage to numerous lifelines and infrastructure along the northern US west coast. The goal of the presented research is to provide a bottom up estimate of the impact on and subsequent recovery of a Cascadia Subduction Zone earthquake on the US western grid to supplement and enhance the expert opinion estimates provided to date. The scope is limited to consideration of shaking damage to utility substation equipment components of a power system model. The analysis utilizes probabilistic models of damage and recovery for substation power system assets, along with graph techniques for modeling connectivity, and Monte Carlo quasi steady state power flow solutions. The results show that a conservative estimate of the initial damage and loss of load is approximately 4,000 MW, with a recovery estimate of 230 days.
Grid-forming control of three-phase and single-phase converters across unbalanced transmission and distribution systems
Authors: Shahin S. Nudehi, Dominic Groß
Date: 2022-11-11 00:00:00

In this work, we investigate grid-forming control for power systems containing three-phase and single-phase converters connected to unbalanced distribution and transmission networks, investigate self-balancing between single-phase converters, and propose a novel balancing feedback for grid-forming control that explicitly allows to trade-off unbalances in voltage and power. We develop a quasi-steady-state power network model that allows to analyze the interactions between three-phase and single-phase power converters across transmission, distribution, and standard transformer interconnections. We first investigate conditions under which this general network admits a well-posed kron-reduced quasi-steady-state network model. Our main contribution leverages this reduced-order model to develop analytical conditions for stability of the overall network with grid-forming three-phase and single-phase converters connected through standard transformer interconnections. Specifically, we provide conditions on the network topology under which (i) single-phase converters autonomously self-synchronize to a phase-balanced operating point and (ii) single-phase converters phase-balance through synchronization with three-phase converters. Moreover, we establish that the conditions can be relaxed if a phase-balancing feedback control is used. Finally, case studies combining detailed models of transmission systems (i.e., IEEE 9-bus) and distribution systems (i.e., IEEE 13-bus) are used to illustrate the results for (i) a power system containing a mix of transmission and distribution connected converters and, (ii) a power system solely using distribution-connected converters at the grid edge.
Structural aging of human neurons is the opposite of the changes in schizophrenia
Authors: Ryuta Mizutani, Rino Saiga, Yoshiro Yamamoto, Masayuki Uesugi, Akihisa Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Vincent De Andrade, Francesco De Carlo, Susumu Takekoshi, Chie Inomoto, Naoya Nakamura, Youta Torii, Itaru Kushima, Shuji Iritani, Norio Ozaki, Kenichi Oshima, Masanari Itokawa, Makoto Arai
Date: 2022-11-13 00:00:00
Tasks: Hallucination

Human mentality develops with age and is altered in psychiatric disorders, though their underlying mechanism is unknown. In this study, we analyzed nanometer-scale three-dimensional structures of brain tissues of the anterior cingulate cortex from eight schizophrenia and eight control cases. The distribution profiles of neurite curvature of the control cases showed a trend depending on their age, resulting in an age-correlated decrease in the standard deviation of neurite curvature (Pearson's r = -0.80, p = 0.018). In contrast to the control cases, the schizophrenia cases deviate upward from this correlation, exhibiting a 60% higher neurite curvature compared with the controls (p = 7.8 x 10^(-4)). The neurite curvature also showed a correlation with a hallucination score (Pearson's r = 0.80, p = 1.8 x 10^(-4)), indicating that neurite structure is relevant to brain function. We suggest that neurite curvature plays a pivotal role in brain aging and can be used as a hallmark to exploit a novel treatment of schizophrenia. This nano-CT paper is the result of our decade-long analysis and is unprecedented in terms of number of cases.
Clustering of countries based on the associated social contact patterns in epidemiological modelling
Authors: Evans Kiptoo Korir, Zsolt Vizi
Date: 2022-11-08 00:00:00
Tasks: Clustering

Mathematical models have been used to understand the spread patterns of infectious diseases such as Coronavirus Disease 2019 (COVID-19). The transmission component of the models can be modelled in an age-dependent manner via introducing contact matrix for the population, which describes the contact rates between the age groups. Since social contact patterns vary from country to country, we can compare and group the countries using the corresponding contact matrices. In this paper, we present a framework for clustering countries based on their contact matrices with respect to an underlying epidemic model. Since the pipeline is generic and modular, we demonstrate its application in a COVID-19 model from R\"ost et. al. which gives a hint about which countries can be compared in a pandemic situation, when only non-pharmaceutical interventions are available.
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Authors: Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen, Tianlong Chen, Xiao Huang, Zhangyang Wang, Xia Hu
Date: 2022-11-09 00:00:00

Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages. Despite lots of efforts developed in computer vision domain, one has not fully explored QNNs for the real-world graph property classification and evaluated them in the quantum device. To bridge the gap, we propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections and relieve the error rate of quantum gates, and use skip connection to augment the quantum outputs with original node features to improve robustness. The experimental results show that our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets. The comprehensive evaluations in both simulator and real quantum machines demonstrate the applicability of QuanGCN to the future graph analysis problem.
Type I Tobit Bayesian Additive Regression Trees for Censored Outcome Regression
Authors: Eoghan O'Neill
Date: 2022-11-14 00:00:00
Tasks: regression, Vocal Bursts Type Prediction

Censoring occurs when an outcome is unobserved beyond some threshold value. Methods that do not account for censoring produce biased predictions of the unobserved outcome. This paper introduces Type I Tobit Bayesian Additive Regression Tree (TOBART-1) models for censored outcomes. Simulation results and real data applications demonstrate that TOBART-1 produces accurate predictions of censored outcomes. TOBART-1 provides posterior intervals for the conditional expectation and other quantities of interest. The error term distribution can have a large impact on the expectation of the censored outcome. Therefore the error is flexibly modeled as a Dirichlet process mixture of normal distributions.