pwc-archive/papers-with-abstracts huggingface dataset:
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Authors: Jerome Sieber, Andrea Zanelli, Samir Bennani, Melanie N. Zeilinger
Date: 2021-11-05 00:00:00
Tasks: Model Predictive Control

Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. This paper presents a novel tube-based MPC formulation based on system level disturbance reachable sets (SL-DRS), which leverage the affine system level parameterization (SLP). We show that imposing a finite impulse response (FIR) constraint on the affine SLP guarantees containment of all future deviations in a finite sequence of SL-DRS. This allows us to formulate a system level tube-MPC (SLTMPC) method using the SL-DRS as tubes, which enables concurrent optimization of the nominal trajectory and the tubes, while using a positively invariant terminal set. Finally, we show that the SL-DRS tubes can also be computed offline.
Multivariable Super-Twisting Algorithm for Systems with Uncertain Input Matrix and Perturbations
Authors: Jaime A. Moreno, Hector Riosand Luis Ovalle, Leonid Fridman
Date: 2021-11-05 00:00:00

This paper proposes a Lyapunov approach to the design of a multivariable generalized Super-Twisting algorithm (MGSTA), which is able to control a system with perturbations and uncertain control matrix, both depending on time and the system states. The presented procedure shows that, under reasonable assumptions for the uncertainties, it is always possible to find a set of constant gains for the MGSTA in order to ensure global and robust finite-time stability of the system's outputs. Simulation results on an omnidirectional mobile robot illustrate the performance of the MGSTA.
On the confidentiality of controller states under sensor attacks
Authors: David Umsonst, Henrik Sandberg
Date: 2020-01-08 00:00:00

With the emergence of cyber-attacks on control systems it has become clear that improving the security of control systems is an important task in today's society. We investigate how an attacker that has access to the measurements transmitted from the plant to the controller can perfectly estimate the internal state of the controller. This attack on sensitive information of the control loop is, on the one hand, a violation of the privacy, and, on the other hand, a violation of the security of the closed-loop system if the obtained estimate is used in a larger attack scheme. Current literature on sensor attacks often assumes that the attacker has already access to the controller's state. However, this is not always possible. We derive conditions for when the attacker is able to perfectly estimate the controller's state. These conditions show that if the controller has unstable poles a perfect estimate of the controller state is not possible. Moreover, we propose a defence mechanism to render the attack infeasible. This defence is based on adding uncertainty to the controller dynamics. We also discuss why an unstable controller is only a good defence for certain plants. Finally, simulations with a three-tank system verify our results.
Formation Path Following Control of Underactuated AUVs -- With Proofs
Authors: Josef Matouš, Kristin Y. Pettersen, Claudio Paliotta
Date: 2021-11-05 00:00:00

This paper proposes a novel method for formation path following of multiple underactuated autonomous underwater vehicles. The method combines line-of-sight guidance with null-space-based behavioral control, allowing the vehicles to follow curved paths while maintaining the desired formation. We investigate the dynamics of the path-following error using cascaded systems theory, and show that the closed-loop system is uniformly semi-globally exponentially stable. We validate the theoretical results through numerical simulations.
Harmonic Modeling, Data Generation, and Analysis of Power Electronics-Interfaced Residential Loads
Authors: Ankit Singhal, Dexin Wang, Andrew P. Reiman, YuAn Liu, Donald J. Hammerstrom, Soumya Kundu
Date: 2021-11-05 00:00:00

Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization. These devices may introduce significant harmonics into power networks that need to be closely studied in order to accurately model and forecast load. However, it can be difficult to obtain harmonic-rich voltage and current data -- necessary for identifying accurate load models -- for residential electrical loads. Recognizing this need, first a set of electronics-based end-use loads is identified and modeled in an electromagnetic transients program tool for a residence. Second, an impedance-varying method is proposed to generate harmonic data that captures harmonic propagation to the supply voltage and harmonic interactions among end-use loads connected to the same supply voltage. Third, a harmonic-rich dataset produced via the proposed methodology is demonstrated to successfully identify frequency coupling matrix-based harmonic load models using the least-squares method. Numerical results demonstrate the accuracy of the model. The impact of limited data availability on model identification is also explored.
Experimental evaluation of sensor attacks and defense mechanisms in feedback systems
Authors: David Umsonst, Henrik Sandberg
Date: 2021-11-05 00:00:00

In this work, we evaluate theoretical results on the feasibility of, the worst-case impact of, and defense mechanisms against a stealthy sensor attack in an experimental setup. We demonstrate that for a controller with stable dynamics the stealthy sensor attack is possible to conduct and the theoretical worst-case impact is close to the achieved practical one. However, although the attack should theoretically be possible when the controller has integral action, we show that the integral action slows the attacker down and the attacker is not able to remain stealthy if it has not perfect knowledge of the controller state. In addition to that, we investigate the effect of different anomaly detectors on the attack impact and conclude that the impact under detectors with internal dynamics is smaller. Finally, we use noise injection into the controller dynamics to unveil the otherwise stealthy attacks.
Stress-informed Control of Medium- and High-head Hydropower Plants to Reduce Penstock Fatigue
Authors: Stefano Cassano, Fabrizio Sossan
Date: 2021-11-05 00:00:00
Tasks: Model Predictive Control

The displacement of conventional generation in favor of stochastic renewable requires increasing regulation duties from the remaining dispatchable resources. In high- and medium-head hydropower plants (HPPs), providing regulation services to the grid and frequently changing the plant's set-point causes water hammer, which engenders pressure and stress transients within the pressurized conduits, especially the penstock, damaging it in the long run. This paper proposes a model predictive control (MPC) that explicitly models the hydraulic transients within the penstock. It achieves to reduce the mechanical loads on the penstock wall, and, consequently, fatigue effectively. Thanks to a suitable linearization of the plant model, the optimization problem underlying the MPC scheme is convex and can be solved with off-the-shelf optimization libraries. The performance of the proposed controller is tested with numerical simulations on a 230MW medium-head HPP with Francis turbine providing primary frequency control. Simulation results show substantially reduced penstock fatigue, existing approaches outperformed, and problem resolution times compatible with real-time control requirements.
Integral state-feedback control of linear time-varying systems: A performance preserving approach
Authors: Richard Seeber, Markus Tranninger
Date: 2021-11-05 00:00:00

An integral extension of state-feedback controllers for linear time-varying plants is proposed, which preserves performance of the nominal controller in the unperturbed case. Similar to time-invariant state feedback with integral action, the controller achieves complete rejection of disturbances whose effective action on the plant is constant with respect to the control input. Moreover, bounded-input bounded-state stability with respect to arbitrary disturbances is shown. A modification for preventing controller windup as well as tuning guidelines are discussed. The efficacy of the proposed technique is demonstrated in simulation for a two-tank system that is linearized along a time-varying reference trajectory.
Stochastic Operation of Energy Constrained Microgrids Considering Battery Degradation
Authors: Per Aaslid, Magnus Korpås, Michael M Belsnes, Olav B Fosso
Date: 2021-11-05 00:00:00
Tasks: energy management, Management

Power systems with high penetration of variable renewable generation are vulnerable to periods with low generation. An alternative to retain high dispatchable generation capacity is electric energy storage that enables utilization of surplus power, where the electric energy storage contributes to the security of supply. Such systems can be considered as energy-constrained, and the operation of the electric energy storage must balance operating cost minimization and the risk of scarcity. In this matter, operation dependent storage characteristics such as energy storage degradation are a complicating factor. This paper proposes a linear approximation of battery state-of-charge degradation and implements it in a stochastic dual dynamic programming based energy-management model in combination with cycling degradation. The implications of degradation modelling are studied for a small Norwegian microgrid with variable renewable power generation and limited dispatchable generation capacity as well as battery and hydrogen storage to balance supply and demand. Our results show that the proposed strategy can prolong the expected battery lifetime by more than four years compared to the naive stochastic strategy but may cause increased degradation for other system resources. It is also evident that a stochastic strategy is crucial to retain low risk of scarcity in energy-constrained systems.
Threat Scenarios and Monitoring Requirements for Cyber-Physical Systems of Flexibility Markets
Authors: Nils Müller, Zeeshan Afzal, Per Eliasson, Mathias Ekstedt, Kai Heussen
Date: 2021-11-05 00:00:00
Tasks: Management

The ongoing integration of renewable generation and distributed energy resources introduces new challenges to distribution network operation. Due to the increasing volatility and uncertainty, distribution system operators (DSOs) are seeking concepts to enable more active management and control. Flexibility markets (FMs) offer a platform for economically efficient trading of electricity flexibility between DSOs and other participants. The integration of cyber, physical and market domains of multiple participants makes FMs a system of cyber-physical systems (CPSs). While cross-domain integration sets the foundation for efficient deployment of flexibility, it introduces new physical and cyber vulnerabilities to participants. This work systematically formulates threat scenarios for the CPSs of FMs, revealing several remaining security challenges across all domains. Based on the threat scenarios, unresolved monitoring requirements for secure participation of DSOs in FMs are identified, providing the basis for future works that address these gaps with new technical concepts.
A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control
Authors: Yuxuan Gu, Jianxiao Wang, Yuanbo Chen, Zhongwei Deng, Hongye Guo, Kedi Zheng, Qixin Chen
Date: 2021-11-05 00:00:00

The penetrations of lithium-ion batteries in transport, energy and communication systems are increasing rapidly. A meticulous model applicable for precise in-situ monitoring and convenient online controlling is in sought to bridge the gap between research and applications. This paper proposes a simplified electro-chemical model and its discrete-time state-space realization derived from the pseudo-two-dimensional model. The solution-phase migration and solid-phase diffusion dynamics with varying parameters are captured and rigorous mathematical expressions of reaction rate distribution and terminal voltage are derived. A simulation framework including initializing, stabilizing and closed-loop correcting schemes with low computation cost are designed. Numeric experiments on different types of batteries in various operating scenarios are conducted for validation.
Dual-Update Data-Driven Control of Deformable Mirrors Using Walsh Basis Functions
Authors: Aleksandar Haber, Thomas Bifano
Date: 2021-11-05 00:00:00

In this paper, we develop a novel data-driven method for Deformable Mirror (DM) control. The developed method updates both the DM model and DM control actions that produce desired mirror surface shapes. The novel method explicitly takes into account actuator constraints and couples a feedback control algorithm with an algorithm for recursive estimation of DM influence function models. In addition to this, we explore the possibility of using Walsh basis functions for DM control. By expressing the desired and observed mirror surface shapes as sums of Walsh pattern matrices, we formulate the control problem in the 2D Walsh basis domain. We thoroughly experimentally verify the developed approach on a 140-actuator MEMS DM, developed by Boston Micromachines. Our results show that the novel method produces the root-mean-square surface error in the $14-40$ nanometer range. These results can additionally be improved by tuning the control and estimation parameters. The developed approach is also applicable to other DM types, such as for example, segmented DMs.
Controller Reduction for Nonlinear Systems by Generalized Differential Balancing
Authors: Yu Kawano
Date: 2021-11-05 00:00:00

In this paper, we aim at developing computationally tractable methods for nonlinear model/controller reduction. Recently, model reduction by generalized differential (GD) balancing has been proposed for nonlinear systems with constant input-vector fields and linear output functions. First, we study incremental properties in the GD balancing framework. Next, based on these analyses, we provide GD LQG balancing and GD $H_\infty$-balancing as controller reduction methods for nonlinear systems by focusing on linear feedback and observer gains. Especially for GD $H_\infty$-balancing, we clarify when the closed-loop system consisting of the full-order system and a reduced-order controller is exponentially stable. All provided methods for controller reduction can be relaxed to linear matrix inequalities.
Stochastic Logarithmic Lipschitz Constants: A Tool to Analyze Contractivity of Stochastic Differential Equations
Authors: Zahra Aminzare
Date: 2021-11-04 00:00:00

We introduce the notion of stochastic logarithmic Lipschitz constants and use these constants to characterize stochastic contractivity of It\^o stochastic differential equations (SDEs) with multiplicative noise. We find an upper bound for stochastic logarithmic Lipschitz constants based on known logarithmic norms (matrix measures) of the Jacobian of the drift and diffusion terms of the SDEs. We discuss noise-induced contractivity in SDEs and common noise-induced synchronization in network of SDEs and illustrate the theoretical results on a noisy Van der Pol oscillator. We show that a deterministic Van der Pol oscillator is not contractive. But, adding a multiplicative noise makes the underlying SDE stochastically contractive.
A `Rosetta stone' for the population dynamics of spiking neuron networks
Authors: Gianni V. Vinci, Maurizio Mattia
Date: 2021-11-05 00:00:00

Populations of spiking neuron models have densities of their microscopic variables (e.g., single-cell membrane potentials) whose evolution fully capture the collective dynamics of biological networks, even outside equilibrium. Despite its general applicability, the Fokker-Planck equation governing such evolution is mainly studied within the borders of the linear response theory, although alternative spectral expansion approaches offer some advantages in the study of the out-of-equilibrium dynamics. This is mainly due to the difficulty in computing the state-dependent coefficients of the expanded system of differential equations. Here, we address this issue by deriving analytic expressions for such coefficients by pairing perturbative solutions of the Fokker-Planck approach with their counterparts from the spectral expansion. A tight relationship emerges between several of these coefficients and the Laplace transform of the inter-spike interval density (i.e., the distribution of first-passage times). `Coefficients' like the current-to-rate gain function, the eigenvalues of the Fokker-Planck operator and its eigenfunctions at the boundaries are derived without resorting to integral expressions. For the leaky integrate-and-fire neurons, the coupling terms between stationary and nonstationary modes are also worked out paving the way to accurately characterize the critical points and the relaxation timescales in networks of interacting populations.
Snapshot Ptychography on Array cameras
Authors: Chengyu Wang, Minghao Hu, Yuzuru Takashima, Timothy J. Schulz, David J. Brady
Date: 2021-11-05 00:00:00

We use convolutional neural networks to recover images optically down-sampled by $6.7\times$ using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, here we apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve SNR. In place training on experimental measurements eliminates the need to directly calibrate the measurement system. We also present simulations of diverse array camera sampling strategies to explore how snapshot compressive systems might be optimized.
Bootstrap inference for panel data quantile regression
Authors: Antonio F. Galvao, Thomas Parker, Zhijie Xiao
Date: 2021-11-05 00:00:00
Tasks: quantile regression, regression

This paper develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.
Social Cost of Carbon: What Do the Numbers Really Mean?
Authors: Nikolay Khabarov, Alexey Smirnov, Michael Obersteiner
Date: 2020-01-24 00:00:00
Tasks: SMAC, SMAC+

Social cost of carbon (SCC) is estimated by integrated assessment models (IAM) and is widely used by government agencies to value climate policy impacts. While there is an ongoing debate about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method itself. This work attempts to fill the gap by providing theoretical background and economic interpretation of the SCC calculation approach implemented in the open-source IAM DICE (Dynamic Integrated model of Climate and the Economy). Our analysis indicates that the present calculation method provides an approximation that might work pretty well in some cases, while in the other cases the estimated value substantially (by the factor of four) deviates from the "true" value. This deviation stems from the inability of the present calculation method to catch the linkages between two key IAM's components -- complex interconnected systems -- climate and economy, both influenced by emission abatement policies. Within the modeling framework of DICE, the presently estimated SCC valuates policy-uncontrolled emissions against economically unjustified consumption, which makes it irrelevant for application in climate-economic policies and, therefore, calls for a replacement by a more appropriate indicator. An apparent SCC alternative, which can be employed for policy formulation is the direct output of the DICE model -- the socially optimal marginal abatement cost (SMAC), which corresponds to technological possibilities at optimal level of carbon emissions abatement. In policy making, because of the previously employed implicit approximation, great attention needs to be paid to the use of SCC estimates obtained earlier.
Real-Time Detection of Drowsiness Among Vehicle Drivers: A Machine Learning Algorithm for Embedded Systems
Authors: Ashwin Pillay, Aditya Kale, Raj Anchan, Aniket Bhadricha, Sangeetha Prasanna Ram
Date: 2021-11-04 00:00:00

Numerous studies have established the necessity for developing safety equipment to detect drowsiness among vehicle drivers. However, for reliable implementations, such systems must employ dependable sources of stimuli; through Electrooculography (EOG), the tendencies of drowsiness can be directly sensed by measuring blinks of prolonged durations. While conventional machine learning (ML) algorithms can be utilized for the detection and classification of these prolonged blinks (PB), executing them on microcontroller units (MCU) may prove to be a laborious task. Hence, by keeping resource constraints and practicality in mind, an ML algorithm is proposed in this study to identify PBs executed by an individual with desirable accuracy and precision while being efficient enough to be deployed on portable wearables using economic MCUs. Furthermore, the suggested algorithm is subjected to multiple rounds of testing in this study thereby, establishing its possibility as a feasible drowsiness detection measure for wearable systems.
A Validated Method for Predicting Small Molecule Ionization Sites using Gibb's Free Energies
Authors: Jessica L. Bade, Sean M. Colby, Ryan S. Renslow, Thomas O. Metz
Date: 2021-11-04 00:00:00

Accurate molecular identification of metabolites can unlock new areas of the molecular universe and allow greater insight into complex biological and environmental systems than currently possible. Analytical approaches for measuring the metabolome, such as NMR spectroscopy, and separation techniques coupled with mass spectrometry, such as LC-IMS-MS, have risen to this challenge by yielding rich experimental data that can be queried by cross-reference with similar information for known standards in reference libraries. Confident identification of molecules in metabolomics studies, though, is often limited by the diversity of available data across chemical space, the unavailability of authentic reference standards, and the corresponding lack of comprehensiveness of standard reference libraries. The In Silico Chemical Library Engine (ISiCLE) addresses theses hindrances by providing a first-principles, cheminformatics pipeline that yields collisional cross section (CCS) values for any given molecule and without the need for training data. In this program, chemical identifiers undergo MD simulations, quantum chemical transformations, and ion mobility calculations for the generation of predicted CCS values. Here, we present a new module for ISiCLE that addresses the sensitivity of CCS predictions to ionization site location. An update to adduct creation methods is proposed concerning a transition from pKa and pKb led predictions to a Gibb's free energy (GFE) based determinacy of true ionization site location. A validation set of experimentally confirmed molecular protonation sites was assembled from literature and cross-referenced with the respective pKb predicted locations and GFE values for all potential ionization site placements. Upon evaluation of the two methods, the lowest GFE value was found to predict the true ionization site location with 100% accuracy while pKb had less accuracy.