pwc-archive/papers-with-abstracts huggingface dataset:
Equilibrium Formulation of a 3 DOF Compliant Mechanism using Sylvester Dialytic Method of Elimination
Authors: Mustafa M. Mustafa, Carl D. Crane, Ibrahim Hamarash
Date: 2024-08-05 00:00:00

This paper studies the equilibrium formulation of a three degree of freedom planar compliant platform mechanism, which is in contact with a solid body in its environment. The mechanism includes two platforms, which are connected in parallel by three linear springs. The capability of deformation by manipulating both platforms exceptionally complicates the problem. The analysis aims to determine all equilibrium configurations for two different cases: FIRST CASE all three springs have zero free lengths and SECOND CASE only two of the springs have zero free lengths. The proposed procedure calculates the pose of the top platform when it is not in contact with the surface, and then detects if the top platform is in contact to determine the equilibrium configurations. To solve the geometric equations of the mechanism, we use Sylvester method of elimination. The approach obtains 4th and 48th degree polynomial equations for the first and second cases, respectively. Numerical examples have been applied to verify the process of analysis. The results, which are numerically calculated by software Maple, prove the validity of the analysis.
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows
Authors: Zihao Ren, Lei Wang, Deming Yuan, Hongye Su, Guodong Shi
Date: 2024-08-05 00:00:00
Tasks: Distributed Optimization

In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a system-level optimization problem with local objective functions and node-to-node communication by distributed algorithms. The scalability of such algorithms crucially depends on the complexity of the communication messages, and a number of communication compressors for distributed optimization have recently been proposed in the literature. First of all, we introduce a general spatio-temporal compressor characterized by the stability of the resulting dynamical system along the vector field of the compressor. We show that several important distributed optimization compressors such as the greedy sparsifier, the uniform quantizer, and the scalarizer all fall into the category of this spatio-temporal compressor. Next, we propose two distributed prime-dual flows with the spatio-temporal compressors being applied to local node states and local error states, respectively, and prove (exponential) convergence of the node trajectories to the global optimizer for (strongly) convex cost functions. Finally, a few numerical examples are present to illustrate our theoretical results.
Real-time Trajectory Optimization of Impaired Aircraft based on Steady State Manoeuvres
Authors: Ramin Norouzi, Amirreza Kosari, Mark H. Lowenberg
Date: 2024-08-05 00:00:00

Aircraft failures alter dynamics, diminishing manoeuvrability. Such manoeuvring flight envelope variations, governed by the aircraft's complex nonlinear dynamics, are unpredictable by pilots and existing flight management systems. To prevent in-flight Loss of Control, post-failure trajectories must be optimal, planned in real-time, avoid terrain, and adhere to the impaired aircraft's reduced manoeuvrability and dynamic constraints. This paper presents a novel real-time trajectory optimization method for impaired aircraft based on a combination of differential flatness theory, the pseudospectral method, nonlinear programming, and inverse dynamics. In the proposed method, which utilizes a high-fidelity nonlinear six degree-of-freedom model, to conform to aircraft's altered dynamics a sequence of trim points is selected from the impaired aircraft's manoeuvring flight envelope based on the chosen optimization criteria, ensuring that the resulting three-dimensional trajectory observes terrain avoidance. Then, the required control inputs are obtained for each manoeuvre in less than a second. The method is applied to the NASA Generic Transport Model with rudder failure near a complex mountainous terrain. Both an optimal one-piece trajectory and a near-optimal piecewise path consisting of several optimal trajectories, are generated in non-real-time and real-time, respectively, and compared. Results show that the near-optimal real-time trajectory retains up to 80% of the optimality.
Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes
Authors: Zhaoyang Li, Minghao Han, Dat-Nguyen Vo, Xunyuan Yin
Date: 2024-08-05 00:00:00
Tasks: Chemical Process, Model Predictive Control

Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mapping from the original state-space to a lifted state space. In this work, we propose an input-augmented Koopman modeling and model predictive control approach. Both the states and the known inputs are lifted using two deep neural networks (DNNs), and a Koopman model with nonlinearity in inputs is trained within the higher-dimensional state-space. A Koopman-based model predictive control problem is formulated. To bypass non-convex optimization induced by the nonlinearity in the Koopman model, we further present an iterative implementation algorithm, which approximates the optimal control input via solving a convex optimization problem iteratively. The proposed method is applied to a chemical process and a biological water treatment process via simulations. The efficacy and advantages of the proposed modeling and control approach are demonstrated.
Extra Throughput versus Days Lost in load-shifting V2G services: Influence of dominant degradation mechanism
Authors: Hamidreza Movahedi, Sravan Pannala, Jason Siegel, Stephen J. Harris, David Howey, Anna Stefanopoulou
Date: 2024-08-04 00:00:00

Electric vehicle (EV) batteries are often underutilized. Vehicle-to-grid (V2G) services can tap into this unused potential, but increased battery usage may lead to more degradation and shorter battery life. This paper substantiates the advantages of providing load-shifting V2G services when the battery is aging, primarily due to calendar aging mechanisms (active degradation mechanisms while the battery is not used). After parameterizing a physics-based digital-twin for three different dominant degradation patterns within the same chemistry (NMC), we introduce a novel metric for evaluating the benefit and associated harm of V2G services: \textit{throughput gained versus days lost (TvD)} and show its strong relationship to the ratio of loss of lithium inventory (LLI) due to calendar aging to the total LLI ($\text{LLI}_\text{Cal}/\text{LLI}$). Our results that focus systematically on degradation mechanisms via lifetime simulation of digital-twins significantly expand prior work that was primarily concentrating on quantifying and reducing the degradation of specific cells by probing their usage and charging patterns. Examining various cell chemistries and conditions enables us to take a broader view and determine whether a particular battery pack is appropriate for load-shifting (V2G) services. Our research demonstrates that the decision "to V2G or not to V2G" can be made by merely estimating the portion of capacity deterioration caused by calendar aging. Specifically, TvD is primarily influenced by the chemistry of cells and the environmental temperature where the car is parked, while the usage intensity and charging patterns of EVs play a lesser role.
Isolating Signatures of Cyberattacks under Stressed Grid Conditions
Authors: Sanchita Ghosh, Syed Ahsan Raza Naqvi, Sai Pushpak Nandanoori, Soumya Kundu
Date: 2024-08-04 00:00:00
Tasks: Diagnostic

In a controlled cyber-physical network, such as a power grid, any malicious data injection in the sensor measurements can lead to widespread impact due to the actions of the closed-loop controllers. While fast identification of the attack signatures is imperative for reliable operations, it is challenging to do so in a large dynamical network with tightly coupled nodes. A particularly challenging scenario arises when the cyberattacks are strategically launched during a grid stress condition, caused by non-malicious physical disturbances. In this work, we propose an algorithmic framework -- based on Koopman mode (KM) decomposition -- for online identification and visualization of the cyberattack signatures in streaming time-series measurements from a power network. The KMs are capable of capturing the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements and thus revealing the specific influences of cyberattacks, even under existing non-malicious grid stress events. Most importantly, it enables us to quantitatively compare the outcomes of different potential cyberattacks injected by an attacker. The performance of the proposed algorithmic framework is illustrated on the IEEE 68-bus test system using synthetic attack scenarios. Such knowledge regarding the detection of various cyberattacks will enable us to devise appropriate diagnostic scheme while considering varied constraints arising from different attacks.
Convergence Analysis of Weighted-Median Opinion Dynamics with Prejudice
Authors: Ruichang Zhang, Zhixin Liu, Ge Chen, Wenjun Mei
Date: 2024-08-04 00:00:00

The Friedkin-Johnsen (FJ) model introduces prejudice into the opinion evolution and has been successfully validated in many practical scenarios; however, due to its weighted average mechanism, only one prejudiced agent can always guide all unprejudiced agents synchronizing to its prejudice under the connected influence network, which may not be in line with some social realities. To fundamentally address the limitation of the weighted average mechanism, a weighted-median opinion dynamics has been recently proposed; however, its theoretical analysis is challenging due to its nonlinear nature. This paper studies the weighted-median opinion dynamics with prejudice, and obtains the convergence and convergence rate when all agents have prejudice, and a necessary and sufficient condition for asymptotic consensus when a portion of agents have prejudice. These results are the first time to analyze the discrete-time and synchronous opinion dynamics with the weighted median mechanism, and address the phenomenon of the FJ model that connectivity leads to consensus when a few agents with the same prejudice join in an unprejudiced group.
Adaptive Safety with Control Barrier Functions and Triggered Batch Least-Squares Identifier
Authors: Jiajun Shen, Wei Wang, Jing Zhou, Jinhu Lü
Date: 2024-08-03 00:00:00

In this paper, a triggered Batch Least-Squares Identifier (BaLSI) based adaptive safety control scheme is proposed for uncertain systems with potentially conflicting control objectives and safety constraints. A relaxation term is added to the Quadratic Programs (QP) combining the transformed Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs), to mediate the potential conflict. The existing Lyapunov-based adaptive schemes designed to guarantee specific properties of the Lyapunov functions, may grow unboundedly under the effects of the relaxation term. The adaptive law is designed by processing system inputs and outputs, to avoid unbounded estimates and overparameterization problems in the existing results. A safetytriggered condition is presented, based on which the forward invariant property of the safe set is shown and Zeno behavior can be excluded. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive control scheme.
Composite Learning Adaptive Control without Excitation Condition
Authors: Jiajun Shen, Wei Wang, Changyun Wen, Jinhu Lu
Date: 2024-08-03 00:00:00
Tasks: parameter estimation, regression

This paper focuses on excitation collection and composite learning adaptive control design for uncertain nonlinear systems. By adopting the spectral decomposition technique, a linear regression equation is constructed to collect previously appeared excitation information, establishing a relationship between unknown parameters and the system's historical data. A composite learning term, developed using the linear regression equation, is incorporating into the Lyapunov-based parameter update law. In comparison to the existing results, all spectrums of previously appeared excitation information are collected, with the matrices in linear regression equation guaranteed to be bounded. This paper introduces concepts of excited and unexcited subspaces for analyzing the parameter estimation errors, and a novel Lyapunov function is developed for stability analysis. It is demonstrated that, without imposing any excitation condition, the state and excited parameter estimation error component converge to zero, while the unexcited component remains unchanged. Simulation results are provided to validate the theoretical findings.
Distributed Adaptive Time-Varying Optimization with Global Asymptotic Convergence
Authors: Liangze Jiang, Zheng-Guang Wu, Lei Wang
Date: 2024-07-30 00:00:00

In this note, we study distributed time-varying optimization for a multi-agent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the introduction of the estimator relaxes the requirement for the Hessians of cost functions, and the integrated design eliminates the waiting time required in the relevant literature for estimating global parameter during algorithm implementation. We then extend this result to a more general class of time-varying cost functions. Two examples are used to verify the proposed designs.
On Game Based Distributed Decision Approach for Multi-agent Optimal Coverage Problem with Application to Constellations Reconfiguration
Authors: Zixin Feng, Wenchao Xue, Yifen Mu, Ming Wei, Bin Meng, Wei Cui
Date: 2024-08-02 00:00:00

This paper focuses on the optimal coverage problem (OCP) for multi-agent systems with decentralized optimization. A game based distributed decision approach for the the multi-agent OCP is proposed. The equivalence between the equilibrium of the game and the extreme value of the global performance objective is strictly proved. Then, a distributed algorithm only using local information to obtain the global near-optimal coverage is developed, and its convergence is proved. Finally, the proposed method is applied to maximize the covering time of a satellite constellation for a target. The simulation results under different scenarios show our method costs much less computation time under some level index than traditional centralized optimization.
Relax, Estimate, and Track: a Simple Battery State-of-charge and State-of-health Estimation Method
Authors: Shida Jiang, Junzhe Shi, Scott Moura
Date: 2024-08-02 00:00:00
Tasks: Management

Battery management is a critical component of ubiquitous battery-powered energy systems, in which battery state-of-charge (SOC) and state-of-health (SOH) estimations are of crucial importance. Conventional SOC and SOH estimation methods, especially model-based methods, often lack accurate modeling of the open circuit voltage (OCV), have relatively high computational complexity, and lack theoretical analysis. This study introduces a simple SOC and SOH estimation method that overcomes all these weaknesses. The key idea of the proposed method is to momentarily set the cell's current to zero for a few minutes during the charging, perform SOC and SOH estimation based on the measured data, and continue tracking the cell's SOC afterward. The method is based on rigorous theoretical analysis, requires no hyperparameter fine-tuning, and is hundreds of times faster than conventional model-based methods. The method is validated on six batteries charged at different C rates and temperatures, realizing fast and accurate estimations under various conditions, with a SOH root mean square error (RMSE) of around 3% and a SOC RMSE of around 1.5%. The data and codes are available at https://berkeley.box.com/s/jz1w6po2iqzzfy7irxd9ok47ku3tr86j.
Practical Guidelines for Data-driven Identification of Lifted Linear Predictors for Control
Authors: Loi Do, Adam Uchytil, Zdeněk Hurák
Date: 2024-08-02 00:00:00

Lifted linear predictor (LLP) is an artificial linear dynamical system designed to predict trajectories of a generally nonlinear dynamical system based on the current state (or measurements) and the input. The main benefit of the LLP is its potential ability to capture the nonlinear system's dynamics with precision superior to other linearization techniques, such as local linearization about the operation point. The idea of lifting is supported by the theory of Koopman Operators. For LLP identification, we focus on the data-driven method based on the extended dynamic mode decomposition (EDMD) algorithm. However, while the EDMD algorithm presents an extremely simple and efficient way to obtain the LLP, it can also yield poor results. In this paper, we present some less intuitive practical guidelines for data-driven identification of the LLPs, aiming at improving usability of LLPs for designing control. We support the guidelines with two motivating examples. The implementation of the examples are shared on a public repository.
Multibeam Hybrid Transmitarray Based on Polarization Rotating Metasurface With Reconfigurable Bidirectional Radiation
Authors: Fan Qin, Yifei Liu, Chao Gu, Linfeng Zeng, Wenchi Cheng, Hailin Zhang, Steven Gao
Date: 2024-08-02 00:00:00

This paper proposes a bidirectional multibeam hybrid transmitarray (HTA) employing a transmission polarization-rotating metasurface (TPRM). A novel configuration is introduced to facilitate bidirectional beam scanning by combining the transmitarray (TA) and folded-transmitarray (FTA). To accomplish the reconfiguration of both unidirectional and bidirectional radiation states in the +z, -z, and +/-z directions, a polarization switchable multi-feed array (MFA) is placed at the focal plane between the TA and FTA, radiating x-polarization, y-polarization, and 45-degree oblique polarization waves, respectively. Meanwhile, the proposed antenna can achieve multibeam radiation in the three aforementioned states by switching the polarization of the MFA. To demonstrate the operating principle, a prototype has been designed, simulated, and fabricated. The measured results agree well with the simulated results. The simulated and measured results indicate that the proposed design can generate reconfigurable multibeam in both forward and backward directions, either separately or simultaneously. In the unidirectional states, forward and backward beam scanning is achieved within an angular range of +/-30{\deg} and +/-22{\deg}, respectively, with peak gains of 23.6 dBi and 23.1 dBi. A simultaneous forward and backward beam scanning of +/-40{\deg} and +/-22{\deg} is achieved in the hybrid radiation state, with peak gains of 19.4 dBi and 19.3 dBi, respectively. The proposed antenna array design offers several advantages, including bidirectional low-loss beam scanning, a simple structure, low power consumption, and a low profile.
Characterization of the Dynamical Properties of Safety Filters for Linear Planar Systems
Authors: Yiting Chen, Pol Mestres, Emiliano Dall'Anese, Jorge Cortes
Date: 2024-08-02 00:00:00

This paper studies the dynamical properties of closed-loop systems obtained from control barrier function-based safety filters. We provide a sufficient and necessary condition for the existence of undesirable equilibria and show that the Jacobian matrix of the closed-loop system evaluated at an undesirable equilibrium always has a nonpositive eigenvalue. In the special case of linear planar systems and ellipsoidal obstacles, we give a complete characterization of the dynamical properties of the corresponding closed-loop system. We show that for underactuated systems, the safety filter always introduces a single undesirable equilibrium, which is a saddle-point. We prove that all trajectories outside the global stable manifold of such equilibrium converge to the origin. In the fully actuated case, we discuss how the choice of nominal controller affects the stability properties of the closed-loop system. Various simulations illustrate our results.
Stop-and-go wave super-resolution reconstruction via iterative refinement
Authors: Junyi Ji, Alex Richardson, Derek Gloudemans, Gergely Zachár, Matthew Nice, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work
Date: 2024-08-01 00:00:00
Tasks: Denoising, Super-Resolution

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, I24-WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement enhancement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data enhancement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-sourced our trained model and code to facilitate further research and applications.
Safety-Critical Control with Offline-Online Neural Network Inference
Authors: Junhui Zhang, Sze Zheng Yong, Dimitra Panagou
Date: 2024-08-01 00:00:00
Tasks: Conformal Prediction, Prediction

This paper presents a safety-critical control framework for an ego agent moving among other agents. The approach infers the dynamics of the other agents, and incorporates the inferred quantities into the design of control barrier function (CBF)-based controllers for the ego agent. The inference method combines offline and online learning with radial basis function neural networks (RBFNNs). The RBFNNs are initially trained offline using collected datasets. To enhance the generalization of the RBFNNs, the weights are then updated online with new observations, without requiring persistent excitation conditions in order to enhance the applicability of the method. Additionally, we employ adaptive conformal prediction to quantify the estimation error of the RBFNNs for the other agents' dynamics, generating prediction sets to cover the true value with high probability. Finally, we formulate a CBF-based controller for the ego agent to guarantee safety with the desired confidence level by accounting for the prediction sets of other agents' dynamics in the sampled-data CBF conditions. Simulation results are provided to demonstrate the effectiveness of the proposed method.