pwc-archive/papers-with-abstracts huggingface dataset:
Optimal decentralized wavelength control in light sources for lithography
Authors: Mruganka Kashyap
Date: 2024-09-07 00:00:00

Pulsed light sources are a critical component of modern lithography, with fine light beam wavelength control paramount for wafer etching accuracy. We study optimal wavelength control by casting it as a decentralized linear quadratic Gaussian (LQG) problem in presence of time-delays. In particular, we consider the multi-optics module (optics and actuators) used for generating the requisite wavelength in light sources as cooperatively interacting systems defined over a directed acyclic graph (DAG). We show that any measurement and other continuous time-delays can be exactly compensated, and the resulting optimal controller implementation at the individual optics-level outperforms any existing wavelength control techniques.
Stability of the Theta Method for Systems with Multiple Time-Delayed Variables
Authors: Andreas Bouterakos, Georgios Tzounas
Date: 2024-09-06 00:00:00

The paper focuses on the numerical stability and accuracy of implicit time-domain integration (TDI) methods when applied for the solution of a power system model impacted by time delays. Such a model is generally formulated as a set of delay differential algebraic equations (DDAEs) in non index-1 Hessenberg form. In particular, the paper shows that numerically stable ordinary differential equation (ODE) methods, such as the trapezoidal and the Theta method, can become unstable when applied to a power system that includes a significant number of delayed variables. Numerical stability is discussed through a scalar test delay differential equation, as well as through a matrix pencil approach that accounts for the DDAEs of any given dynamic power system model. Simulation results are presented in a case study based on the IEEE 39-bus system.
Towards a Socially Acceptable Competitive Equilibrium in Energy Markets
Authors: Koorosh Shomalzadeh, Nima Monshizadeh
Date: 2024-09-06 00:00:00
Tasks: Fairness

This paper addresses the problem of energy sharing between a population of price-taking agents who adopt decentralized primal-dual gradient dynamics to find the Competitive Equilibrium (CE). Although the CE is efficient, it does not ensure fairness and can potentially lead to high prices. As the agents and market operator share a social responsibility to keep the price below a certain socially acceptable threshold, we propose an approach where the agents modify their utility functions in a decentralized way. We introduce a dynamic feedback controller for the primal-dual dynamics to steer the agents to a Socially acceptable Competitive Equilibrium (SCE). We demonstrate our theoretical findings in a case study.
Capturing Opportunity Costs of Batteries with a Staircase Supply-Demand Function
Authors: Ye Guo, Chenge Gao, Cong Chen
Date: 2024-09-06 00:00:00

In the global pursuit of carbon neutrality, the role of batteries is indispensable. They provide pivotal flexibilities to counter uncertainties from renewables, preferably by participating in electricity markets. Unlike thermal generators, however, the dominant type of cost for batteries is opportunity cost, which is more vague and challenging to represent through bids in stipulated formats. This article shows the opposite yet surprising results: The demand-supply function of an ideal battery, considering its opportunity cost, is a staircase function with no more than five segments, which is a perfect match with existing rules in many real electricity markets. The demand-supply function shifts horizontally with price forecasts and vertically with the initial SOC. These results can be generalized to imperfect batteries and numerous battery-like resources, including battery clusters, air-conditioners, and electric vehicle charging stations, although the number of segments may vary. These results pave the way for batteries to participate in electricity markets.
An updated look on the convergence and consistency of data-driven dynamical models
Authors: Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Date: 2024-09-06 00:00:00

Deep sequence models are receiving significant interest in current machine learning research. By representing probability distributions that are fit to data using maximum likelihood estimation, such models can model data on general observation spaces (both continuous and discrete-valued). Furthermore, they can be applied to a wide range of modelling problems, including modelling of dynamical systems which are subject to control. The problem of learning data-driven models of systems subject to control is well studied in the field of system identification. In particular, there exist theoretical convergence and consistency results which can be used to analyze model behaviour and guide model development. However, these results typically concern models which provide point predictions of continuous-valued variables. Motivated by this, we derive convergence and consistency results for a class of nonlinear probabilistic models defined on a general observation space. The results rely on stability and regularity assumptions, and can be used to derive consistency conditions and bias expressions for nonlinear probabilistic models of systems under control. We illustrate the results on examples from linear system identification and Markov chains on finite state spaces.
Jam-absorption driving with data assimilation
Authors: Siyu Li, Ryosuke Nishi, Daichi Yanagisawa, Katsuhiro Nishinari
Date: 2024-09-06 00:00:00

This paper introduces a data assimilation (DA) framework based on the extended Kalman filter-cell transmission model, designed to assist jam-absorption driving (JAD) operation to alleviate sag traffic congestion. To ascertain and demonstrate the effectiveness of the DA framework for JAD operation, in this paper, we initially investigated its impact on the motion and control performance of a single absorbing vehicle. Numerical results show that the DA framework effectively mitigated underestimated or overestimated control failures of JAD caused by misestimation of key parameters (e.g., free flow speed and critical density) of the traffic flow fundamental diagram. The findings suggest that the proposed DA framework can reduce control failures and prevent significant declines and deteriorations in JAD performance caused by changes in traffic characteristics, e.g., weather conditions or traffic composition.
Vehicular Resilient Control Strategy for a Platoon of Self-Driving Vehicles under DoS Attack
Authors: Hassan Mokari, Yufei Tang
Date: 2024-09-05 00:00:00
Tasks: Autonomous Vehicles, Navigate

In a platoon, multiple autonomous vehicles engage in data exchange to navigate toward their intended destination. Within this network, a designated leader shares its status information with followers based on a predefined communication graph. However, these vehicles are susceptible to disturbances, leading to deviations from their intended routes. Denial-of-service (DoS) attacks, a significant type of cyber threat, can impact the motion of the leader. This paper addresses the destabilizing effects of DoS attacks on platoons and introduces a novel vehicular resilient control strategy to restore stability. Upon detecting and measuring a DoS attack, modeled with a time-varying delay, the proposed method initiates a process to retrieve the attacked leader. Through a newly designed switching system, the attacked leader transitions to a follower role, and a new leader is identified within a restructured platoon configuration, enabling the platoon to maintain consensus. Specifically, in the event of losing the original leader due to a DoS attack, the remaining vehicles do experience destabilization. They adapt their motions as a cohesive network through a distributed resilient controller. The effectiveness of the proposed approach is validated through an illustrative case study, showing its applicability in real-world scenarios.
A Nonlinear Controller for Parallel DC-DC Converters with ZIP Load and Constrained Output Voltage
Authors: Somayyeh Bahrami
Date: 2024-09-05 00:00:00

In this paper, an adaptive nonlinear controller is designed for a parallel DC-DC converter system that feeds an unknown ZIP load, characterized by constant impedance (Z), constant current (I), and constant power (P), at the DC bus. The proposed controller ensures simultaneous voltage adjustment and power sharing in the large signal sense despite uncertainties in ZIP loads, DC input voltages, and other electrical parameters. To keep the output voltage within a desired range, we utilize a barrier function that is invertible, smoothly continuous, and strictly increasing. Its limits at infinity represent the upper and lower bounds for the output voltage. We apply the invertible transformation of the barrier function to the output voltage and then design the controller using the adaptive backstepping method. Using this barrier-function-based adaptive backstepping controller, uncertain parameters are identified on-line, and the voltage adjustment and power sharing objectives are established. Moreover, voltage constraint is not violated event in the presence of sudden and unknown large variations of load. The efficiency of the proposed nonlinear controller is evaluated through simulations of a parallel DC-DC converter system using the MATLAB/Simscape Electrical environment.
Data-informativity conditions for structured linear systems with implications for dynamic networks
Authors: Paul M. J. Van den Hof, Shengling Shi, Stefanie J. M. Fonken, Karthik R. Ramaswamy, Håkan Hjalmarsson, Arne G. Dankers
Date: 2024-09-05 00:00:00

When estimating models of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. In this paper it is investigated how such a condition can be relaxed by exploiting prior structural information on the multivariable system, such as structural zero elements in the transfer matrix or entries that are a priori known and therefore not parametrized. It is shown that in particular situations the data-informativity condition can be decomposed into different MISO (multiple input single output) situations, leading to relaxed conditions for the MIMO (multiple input multiple output) model. When estimating a single module in a linear dynamic network, the data-informativity conditions can generically be formulated as path-based conditions on the graph of the network. The new relaxed conditions for data-informativity will then also lead to relaxed path-based conditions on the network graph. Additionally the new expressions are shown to be closely related to earlier derived conditions for (generic) single module identifiability.
Distributionally Robust Control for Chance-Constrained Signal Temporal Logic Specifications
Authors: Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas, Sadegh Soudjani
Date: 2024-09-05 00:00:00

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method.
An Effective Current Limiting Strategy to Enhance Transient Stability of Virtual Synchronous Generator
Authors: Yifan Zhao, Zhiqian Zhang, Ziyang Xu, Zhenbin Zhang, Jose Rodriguez
Date: 2024-09-05 00:00:00

VSG control has emerged as a crucial technology for integrating renewable energy sources. However, renewable energy have limited tolerance to overcurrent, necessitating the implementation of current limiting (CL)strategies to mitigate the overcurrent. The introduction of different CL strategies can have varying impacts on the system. While previous studies have discussed the effects of different CL strategies on the system, but they lack intuitive and explicit explanations. Meanwhile, previous CL strategy have failed to effectively ensure the stability of the system. In this paper, the Equal Proportional Area Criterion (EPAC) method is employed to intuitively explain how different CL strategies affect transient stability. Based on this, an effective current limiting strategy is proposed. Simulations are conducted in MATLAB/Simulink to validate the proposed strategy. The simulation results demonstrate that, the proposed effective CL strategy exhibits superior stability.
An innovation-based cycle-slip, multipath estimation, detection and mitigation method for tightly coupled GNSS/INS/Vision navigation in urban areas
Authors: Bo Xu, Shoujian Zhang, Jingrong Wang, Jiancheng Li
Date: 2024-09-05 00:00:00
Tasks: Blocking, Sensor Fusion

Precise, consistent, and reliable positioning is crucial for a multitude of uses. In order to achieve high precision global positioning services, multi-sensor fusion techniques, such as the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Vision integration system, combine the strengths of various sensors. This technique is essential for localization in complex environments and has been widely used in the mass market. However, frequent signal deterioration and blocking in urban environments exacerbates the degradation of GNSS positioning and negatively impacts the performance of the multi-sensor integration system. For GNSS pseudorange and carrier phase observation data in the urban environment, we offer an innovation-based cycle slip/multipath estimation, detection, and mitigation (I-EDM) method to reduce the influence of multipath effects and cycle slips on location induced by obstruction in urban settings. The method obtains the innovations of GNSS observations with the cluster analysis method. Then the innovations are used to detect the cycle slips and multipath. Compared with the residual-based method, the innovation-based method avoids the residual overfitting caused by the least square method, resulting in better detection of outliers within the GNSS observations. The vehicle tests carried out in urban settings verify the proposed approach. Experimental results indicate that the accuracy of 0.23m, 0.11m, and 0.31m in the east, north and up components can be achieved by the GNSS/INS/Vision tightly coupled system with the I-EDM method, which has a maximum of 21.6% improvement when compared with the residual-based EDM (R-EDM) method.
Recursive Quantization for $\mathcal{L}_2$ Stabilization of a Finite Capacity Stochastic Control Loop with Intermittent State Observations
Authors: Shrija Karmakar, Ritwik Kumar Layek
Date: 2024-09-05 00:00:00
Tasks: Quantization

The problem of $\mathcal{L}_2$ stabilization of a state feedback stochastic control loop is investigated under different constraints. The discrete time linear time invariant (LTI) open loop plant is chosen to be unstable. The additive white Gaussian noise is assumed to be stationary. The link between the plant and the controller is assumed to be a finite capacity stationary channel, which puts a constraint on the bit rate of the transmission. Moreover, the state of the plant is observed only intermittently keeping the loop open some of the time. In this manuscript both scalar and vector plants under Bernoulli and Markov intermittence models are investigated. Novel bounds on intermittence parameters are obtained to ensure $\mathcal{L}_2$ stability. Moreover, novel recursive quantization algorithms are developed to implement the stabilization scheme under all the constraints. Suitable illustrative examples are provided to elucidate the main results.
On Using Curved Mirrors to Decrease Shadowing in VLC
Authors: Borja Genoves Guzman, Ana Garcia Armada, Maïté Brandt-Pearce
Date: 2024-09-05 00:00:00

Visible light communication (VLC) complements radio frequency in indoor environments with large wireless data traffic. However, VLC is hindered by dramatic path losses when an opaque object is interposed between the transmitter and the receiver. Prior works propose the use of plane mirrors as optical reconfigurable intelligent surfaces (ORISs) to enhance communications through non-line-of-sight links. Plane mirrors rely on their orientation to forward the light to the target user location, which is challenging to implement in practice. This paper studies the potential of curved mirrors as static reflective surfaces to provide a broadening specular reflection that increases the signal coverage in mirror-assisted VLC scenarios. We study the behavior of paraboloid and semi-spherical mirrors and derive the irradiance equations. We provide extensive numerical and analytical results and show that curved mirrors, when developed with proper dimensions, may reduce the shadowing probability to zero, while static plane mirrors of the same size have shadowing probabilities larger than 65%. Furthermore, the signal-to-noise ratio offered by curved mirrors may suffice to provide connectivity to users deployed in the room even when a line-of-sight link blockage occurs.
Grid-Forming Storage Networks: Analytical Characterization of Damping and Design Insights
Authors: Kaustav Chatterjee, Ramij Raja Hossain, Sai Pushpak Nandanoori, Soumya Kundu, Subhrajit Sinha, Diane Baldwin, Ronald Melton
Date: 2024-09-05 00:00:00
Tasks: Sensitivity

The paper presents a theoretical study on small-signal stability and damping in bulk power systems with multiple grid-forming inverter-based storage resources. A detailed analysis is presented, characterizing the impacts of inverter droop gains and storage size on the slower eigenvalues, particularly those concerning inter-area oscillation modes. From these parametric sensitivity studies, a set of necessary conditions are derived that the design of droop gain must satisfy to enhance damping performance. The analytical findings are structured into propositions highlighting potential design considerations for improving system stability. The findings are illustrated via numerical studies on an IEEE 68-bus grid-forming storage network.
Model Predictive Online Trajectory Planning for Adaptive Battery Discharging in Fuel Cell Vehicle
Authors: Katsuya Shigematsu, Hikaru Hoshino, Eiko Furutani
Date: 2024-09-05 00:00:00
Tasks: energy management, Management, Trajectory Planning

This paper presents an online trajectory planning approach for optimal coordination of Fuel Cell (FC) and battery in plug-in Hybrid Electric Vehicle (HEV). One of the main challenges in energy management of plug-in HEV is generating State-of-Charge (SOC) reference curves by optimally depleting battery under high uncertainties in driving scenarios. Recent studies have begun to explore the potential of utilizing partial trip information for optimal SOC trajectory planning, but dynamic responses of the FC system are not taken into account. On the other hand, research focusing on dynamic operation of FC systems often focuses on air flow management, and battery has been treated only partially. Our aim is to fill this gap by designing an online trajectory planner for dynamic coordination of FC and battery systems that works with a high-level SOC planner in a hierarchical manner. We propose an iterative LQR based online trajectory planning method where the amount of electricity dischargeable at each driving segment can be explicitly and adaptively specified by the high-level planner. Numerical results are provided as a proof of concept example to show the effectiveness of the proposed approach.
CyberDep: Towards the Analysis of Cyber-Physical Power System Interdependencies Using Bayesian Networks and Temporal Data
Authors: Leen Al Homoud, Katherine Davis, Shamina Hossain-McKenzie, Nicholas Jacobs
Date: 2024-09-05 00:00:00

Modern-day power systems have become increasingly cyber-physical due to the ongoing developments to the grid that include the rise of distributed energy generation and the increase of the deployment of many cyber devices for monitoring and control, such as the Supervisory Control and Data Acquisition (SCADA) system. Such capabilities have made the power system more vulnerable to cyber-attacks that can harm the physical components of the system. As such, it is of utmost importance to study both the physical and cyber components together, focusing on characterizing and quantifying the interdependency between these components. This paper focuses on developing an algorithm, named CyberDep, for Bayesian network generation through conditional probability calculations of cyber traffic flows between system nodes. Additionally, CyberDep is implemented on the temporal data of the cyber-physical emulation of the WSCC 9-bus power system. The results of this work provide a visual representation of the probabilistic relationships within the cyber and physical components of the system, aiding in cyber-physical interdependency quantification.
Combined Plant and Control Co-design via Solutions of Hamilton-Jacobi-Bellman Equation Based on Physics-informed Learning
Authors: Kenjiro Nishimura, Hikaru Hoshino, Eiko Furutani
Date: 2024-09-04 00:00:00

This paper addresses integrated design of engineering systems, where physical structure of the plant and controller design are optimized simultaneously. To cope with uncertainties due to noises acting on the dynamics and modeling errors, an Uncertain Control Co-design (UCCD) problem formulation is proposed. Existing UCCD methods usually rely on uncertainty propagation analyses using Monte Calro methods for open-loop solutions of optimal control, which suffer from stringent trade-offs among accuracy, time horizon, and computational time. The proposed method utilizes closed-loop solutions characterized by the Hamilton-Jacobi-Bellman equation, a Partial Differential Equation (PDE) defined on the state space. A solution algorithm for the proposed UCCD formulation is developed based on PDE solutions of Physics-informed Neural Networks (PINNs). Numerical examples of regulator design problems are provided, and it is shown that simultaneous update of PINN weights and the design parameters effectively works for solving UCCD problems.
Rigid-Body Attitude Control on $\mathsf{SO(3)}$ using Nonlinear Dynamic Inversion
Authors: Hafiz Zeeshan Iqbal Khan, Farooq Aslam, Muhammad Farooq Haydar, Jamshed Riaz
Date: 2024-09-04 00:00:00

This paper presents a cascaded control architecture, based on nonlinear dynamic inversion (NDI), for rigid body attitude control. The proposed controller works directly with the rotation matrix parameterization, that is, with elements of the Special Orthogonal Group $\mathsf{SO(3)}$, and avoids problems related to singularities and non-uniqueness which affect other commonly used attitude representations such as Euler angles, unit quaternions, modified Rodrigues parameters, etc. The proposed NDI-based controller is capable of imposing desired linear dynamics of any order for the outer attitude loop and the inner rate loop, and gives control designers the flexibility to choose higher-order dynamic compensators in both loops. In addition, sufficient conditions are presented in the form of linear matrix inequalities (LMIs) which ensure that the outer loop controller renders the attitude loop almost globally asymptotically stable (AGAS) and the rate loop globally asymptotically stable (GAS). Furthermore, the overall cascaded control architecture is shown to be AGAS in the case of attitude error regulation. Lastly, the proposed scheme is compared with an Euler angles-based NDI scheme from literature for a tracking problem involving agile maneuvering of a multicopter in a high-fidelity nonlinear simulation.