pwc-archive/papers-with-abstracts huggingface dataset:
Controllability Test for Nonlinear Datatic Systems
Authors: Yujie Yang, Letian Tao, Likun Wang, Shengbo Eben Li
Date: 2024-05-15 00:00:00

Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending it to datatic (i.e., data-driven) control systems is still a challenging task due to the absence of system models. In this study, we propose a general controllability test method for nonlinear systems with datatic description, where the system behaviors are merely described by data. In this situation, the state transition information of a dynamic system is available only at a limited number of data points, leaving the behaviors beyond these points unknown. Different from traditional exact controllability, we introduce a new concept called $\epsilon$-controllability, which extends the definition from point-to-point form to point-to-region form. Accordingly, our focus shifts to checking whether the system state can be steered to a closed state ball centered on the target state, rather than exactly at that target state. On its basis, we propose a tree search algorithm called maximum expansion of controllable subset (MECS) to identify controllable states in the dataset. Starting with a specific target state, our algorithm can iteratively propagate controllability from a known state ball to a new one. This iterative process gradually enlarges the $\epsilon$-controllable subset by incorporating new controllable balls until all $\epsilon$-controllable states are searched. Besides, a simplified version of MECS is proposed by solving a special shortest path problem, called Floyd expansion with radius fixed (FERF). FERF maintains a fixed radius of all controllable balls based on a mutual controllability assumption of neighboring states. The effectiveness of our method is validated in three datatic control systems whose dynamic behaviors are described by sampled data.
Autonomous Cooperative Levels of Multiple-Heterogeneous Unmanned Vehicle Systems
Authors: Yoo-Bin Bae, Yeong-Ung Kim, Jun-Oh Park, Hyo-Sung Ahn
Date: 2024-05-15 00:00:00

As multiple and heterogenous unmanned vehicle systems continue to play an increasingly important role in addressing complex missions in the real world, the need for effective cooperation among unmanned vehicles becomes paramount. The concept of autonomous cooperation, wherein unmanned vehicles cooperate without human intervention or human control, offers promising avenues for enhancing the efficiency and adaptability of intelligence of multiple-heterogeneous unmanned vehicle systems. Despite the growing interests in this domain, as far as the authors are concerned, there exists a notable lack of comprehensive literature on defining explicit concept and classifying levels of autonomous cooperation of multiple-heterogeneous unmanned vehicle systems. In this aspect, this article aims to define the explicit concept of autonomous cooperation of multiple-heterogeneous unmanned vehicle systems. Furthermore, we provide a novel criterion to assess the technical maturity of the developed unmanned vehicle systems by classifying the autonomous cooperative levels of multiple-heterogeneous unmanned vehicle systems.
DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation
Authors: Jingwen Yang, Ping Wang, Fatemeh Golpayegani, Shen Wang
Date: 2024-05-15 00:00:00
Tasks: Deep Reinforcement Learning

Variable speed limit (VSL) control is an established yet challenging problem to improve freeway traffic mobility and alleviate bottlenecks by customizing speed limits at proper locations based on traffic conditions. Recent advances in deep reinforcement learning (DRL) have shown promising results in solving VSL control problems by interacting with sophisticated environments. However, the modeling of these methods ignores the inherent graph structure of the traffic state which can be a key factor for more efficient VSL control. Graph structure can not only capture the static spatial feature but also the dynamic temporal features of traffic. Therefore, we propose the DVS-RG: DRL-based differential variable speed limit controller with graph state representation. DVS-RG provides distinct speed limits per lane in different locations dynamically. The road network topology and traffic information(e.g., occupancy, speed) are integrated as the state space of DVS-RG so that the spatial features can be learned. The normalization reward which combines efficiency and safety is used to train the VSL controller to avoid excessive inefficiencies or low safety. The results obtained from the simulation study on SUMO show that DRL-RG achieves higher traffic efficiency (the average waiting time reduced to 68.44\%) and improves the safety measures (the number of potential collision reduced by 15.93\% ) compared to state-of-the-art DRL methods.
Modeling and Design Optimization of Looped Water Distribution Networks using MS Excel: Developing the Open-Source X-WHAT Model
Authors: Marcus Nóbrega Gomes Jr., Igor Matheus Benites, Salma M. Elsherif, Ahmad F. Taha, Marcio H. Giacomoni
Date: 2024-05-15 00:00:00

Cost-effective water distribution network (WDN) design with acceptable pressure performance is crucial for the management of drinking water in cities. This paper presents a Microsoft Excel tool to model, simulate, and optimize WDNs with looped pipelines under steady-state incompressible flow simulations. Typically, the hardy-cross method is applied using spreadsheet calculations to estimate discharges. This method requires mass-conservative initial estimates and requires successive iterations to converge. In this paper, however, we develop an alternative method that uses the built-in solver capabilities of Excel, does not require initial mass-conservative estimation, and is free of flow corrections. The main objective of this paper is to develop an open-source accessible tool for simulating hydraulic networks also adapted for teaching and learning purposes. The governing equations and the mathematical basis for the hydraulic modeling of the system are mathematically described, considering the topology of the network, mass and energy conservation, cost of tank material, foundation, and cost of pumping energy to fill the tank. The use of this tool is encouraged at the undergraduate and graduate engineering levels, as it offers the opportunity to address complex concepts in a comprehensive way using a spreadsheet that does not require coding expertise. Hence, users can debug all cells and understand all equations used in the hydraulic model, as well as modify them. To demonstrate the model capabilities, three practical examples are presented, with the first one solved step by step, and the results are compared with the EPANET and with the results reported in the literature. Using the optimization method presented in this paper, it was possible to achieve a cost reduction of 151,790 USD (9.8% of the total cost) in a network that supplies a 44,416 population.
IoT-enabled Stability Chamber for the Pharmaceutical Industry
Authors: Nitol Saha, Md Masruk Aulia, Dibakar Das, Md. Mostafizur Rahman
Date: 2024-05-15 00:00:00

A stability chamber is a critical piece of equipment for any pharmaceutical facility to retain the manufactured product for testing the stability and quality of the products over a certain period of time by keeping the products in different sets of environmental conditions. In this paper, we proposed an IoT-enabled stability chamber for the pharmaceutical industry. We developed four stability chambers by using the existing utilities of a manufacturing facility. The state-of-the-art automatic PID controlling system of Siemens S7-1200 PLC was used to control each chamber. PC-based Siemens WinCC Runtime Advanced visualization platform was used to visualize the data of the chamber which is FDA 21 CFR Part 11 Compliant. Additionally, an Internet of Things-based (IoT-based) application was also developed to monitor the sensor's data remotely using any client application.
Joint Instantaneous Amplitude-Frequency Analysis of Vibration Signals for Vibration-Based Condition Monitoring of Rolling Bearings
Authors: Sulaiman Aburakhia, Ismail Hamieh, Abdallah Shami
Date: 2024-05-14 00:00:00

Vibrations of damaged bearings are manifested as modulations in the amplitude of the generated vibration signal, making envelope analysis an effective approach for discriminating between healthy and abnormal vibration patterns. Motivated by this, we introduce a low-complexity method for vibration-based condition monitoring (VBCM) of rolling bearings based on envelope analysis. In the proposed method, the instantaneous amplitude (envelope) and instantaneous frequency of the vibration signal are jointly utilized to facilitate three novel envelope representations: instantaneous amplitude-frequency mapping (IAFM), instantaneous amplitude-frequency correlation (IAFC), and instantaneous energy-frequency distribution (IEFD). Maintaining temporal information, these representations effectively capture energy-frequency variations that are unique to the condition of the bearing, thereby enabling the extraction of discriminative features with high sensitivity to variations in operational conditions. Accordingly, six new highly discriminative features are engineered from these representations, capturing and characterizing their shapes. The experimental results show outstanding performance in detecting and diagnosing various fault types, demonstrating the effectiveness of the proposed method in capturing unique variations in energy and frequency between healthy and faulty bearings. Moreover, the proposed method has moderate computational complexity, meeting the requirements of real-time applications. Further, the Python code of the proposed method is made public to support collaborative research efforts and ensure the reproducibility of the presented work
Estimation of Participation Factors for Power System Oscillation from Measurements
Authors: Tianwei Xia, Zhe Yu, Kai Sun, Di Shi, Kaiyang Huang
Date: 2024-05-14 00:00:00

In a power system, when the participation factors of generators are computed to rank their participations into an oscillatory mode, a model-based approach is conventionally used on the linearized system model by means of the corresponding right and left eigenvectors. This paper proposes a new approach for estimating participation factors directly from measurement data on generator responses under selected disturbances. The approach computes extended participation factors that coincide with accurate model-based participation factors when the measured responses satisfy an ideally symmetric condition. This paper relaxes this symmetric condition with the original measurement space by identifying and utilizing a coordinate transformation to a new space optimally recovering the symmetry. Thus, the optimal estimates of participation factors solely from measurements are achieved, and the accuracy and influencing factors are discussed. The proposed approach is first demonstrated in detail on a two-area system and then tested on an NPCC 48-machine power system. The penetration of inverter-based resources is also considered.
Design and Analysis of Resilient Vehicular Platoon Systems over Wireless Networks
Authors: Tingyu Shui, Walid Saad
Date: 2024-05-14 00:00:00

Connected vehicular platoons provide a promising solution to improve traffic efficiency and ensure road safety. Vehicles in a platoon utilize on-board sensors and wireless vehicle-to-vehicle (V2V) links to share traffic information for cooperative adaptive cruise control. To process real-time control and alert information, there is a need to ensure clock synchronization among the platoon's vehicles. However, adversaries can jeopardize the operation of the platoon by attacking the local clocks of vehicles, leading to clock offsets with the platoon's reference clock. In this paper, a novel framework is proposed for analyzing the resilience of vehicular platoons that are connected using V2V links. In particular, a resilient design based on a diffusion protocol is proposed to re-synchronize the attacked vehicle through wireless V2V links thereby mitigating the impact of variance of the transmission delay during recovery. Then, a novel metric named temporal conditional mean exceedance is defined and analyzed in order to characterize the resilience of the platoon. Subsequently, the conditions pertaining to the V2V links and recovery time needed for a resilient design are derived. Numerical results show that the proposed resilient design is feasible in face of a nine-fold increase in the variance of transmission delay compared to a baseline designed for reliability. Moreover, the proposed approach improves the reliability, defined as the probability of meeting a desired clock offset error requirement, by 45% compared to the baseline.
The distributed biased min-consensus protocol revisited: pre-specified finite time control strategies and small-gain based analysis
Authors: Yuanqiu Mo, He Wang
Date: 2024-05-14 00:00:00

Unlike the classical distributed consensus protocols enabling the group of agents as a whole to reach an agreement regarding a certain quantity of interest in a distributed fashion, the distributed biased min-consensus protocol (DBMC) has been proven to generate advanced complexity pertaining to solving the shortest path problem. As such a protocol is commonly incorporated as the first step of a hierarchical architecture in real applications, e.g., robots path planning, management of dispersed computing services, an impedance limiting the application potential of DBMC lies in, the lack of results regarding to its convergence within a user-assigned time. In this paper, we first propose two control strategies ensuring the state error of DBMC decrease exactly to zero or a desired level manipulated by the user, respectively. To compensate the high feedback gains incurred by these two control strategies, this paper further investigates the nominal DBMC itself. By leveraging small gain based stability tools, this paper also proves the global exponential input-to-state stability of DBMC, outperforming its current stability results. Simulations have been provided to validate the efficacy of our theoretical result.
Data-driven memory-dependent abstractions of dynamical systems via a Cantor-Kantorovich metric
Authors: Adrien Banse, Licio Romao, Alessandro Abate, Raphaël M. Jungers
Date: 2024-05-14 00:00:00

Abstractions of dynamical systems enable their verification and the design of feedback controllers using simpler, usually discrete, models. In this paper, we propose a data-driven abstraction mechanism based on a novel metric between Markov models. Our approach is based purely on observing output labels of the underlying dynamics, thus opening the road for a fully data-driven approach to construct abstractions. Another feature of the proposed approach is the use of memory to better represent the dynamics in a given region of the state space. We show through numerical examples the usefulness of the proposed methodology.
Magnetic-Guided Flexible Origami Robot toward Long-Term Phototherapy of H. pylori in the Stomach
Authors: Sishen Yuan, Baijia Liang, Po Wa Wong, Mingjing Xu, Chi Hsuan Li, Zhen Li, Hongliang Ren
Date: 2024-05-12 00:00:00

Helicobacter pylori, a pervasive bacterial infection associated with gastrointestinal disorders such as gastritis, peptic ulcer disease, and gastric cancer, impacts approximately 50% of the global population. The efficacy of standard clinical eradication therapies is diminishing due to the rise of antibiotic-resistant strains, necessitating alternative treatment strategies. Photodynamic therapy (PDT) emerges as a promising prospect in this context. This study presents the development and implementation of a magnetically-guided origami robot, incorporating flexible printed circuit units for sustained and stable phototherapy of Helicobacter pylori. Each integrated unit is equipped with wireless charging capabilities, producing an optimal power output that can concurrently illuminate up to 15 LEDs at their maximum intensity. Crucially, these units can be remotely manipulated via a magnetic field, facilitating both translational and rotational movements. We propose an open-loop manual control sequence that allows the formation of a stable, compliant triangular structure through the interaction of internal magnets. This adaptable configuration is uniquely designed to withstand the dynamic squeezing environment prevalent in real-world gastric applications. The research herein represents a significant stride in leveraging technology for innovative medical solutions, particularly in the management of antibiotic-resistant Helicobacter pylori infections.
Memory-Based Set Point Modulation for Improved Transient Response of Distributed Energy Resources
Authors: Milad Beikbabaei, Brady Alexander, Ashwin Venkataramanan, Ali Mehrizi-Sani
Date: 2024-05-11 00:00:00

As the composition of the power grid evolves to integrate more renewable generation, its reliance on distributed energy resources (DER) is increasing. Existing DERs are often controlled with proportional integral (PI) controllers that, if not properly tuned or if system parameters change, exhibit sluggish performance or large overshoot. The use of set point automatic adjustment with correction-enabled (SPAACE) with a linear predictor improves the transient response of these DERs without the need to access the PI controller parameters. The limitation of the existing SPAACE method is the high sampling rate needed for improved performance, which is not always practical. This paper proposes the addition of a memory term to the SPAACE with a linear predictor. This memory term is the integral of the errors of previous samples, which adds another layer to the prediction to improve the response at lower sampling rates and further reduces the overshoot and settling time compared to the existing SPAACE method. Time-domain simulation studies are performed in PSCAD/EMTDC to show the effectiveness of the proposed controller.
Real-Time Simulation of a Resilient Control Center for Inverter-Based Microgrids
Authors: Milad Beikbabaei, Ali Mehrizi-Sani
Date: 2024-05-11 00:00:00

The number of installed remote terminal units (RTU) is on the rise, increasing the observability and control of the power system. RTUs enable sending data to and receiving data from a control center in the power system. A distribution grid control center runs distribution management system (DMS) algorithms, where the DMS takes control actions during transients and outages, such as tripping a circuit breaker and disconnecting a controllable load to increase the resiliency of the grid. Relying on communication-based devices makes the control center vulnerable to cyberattacks, and attackers can send falsified data to the control center to cause disturbances or power outages. Previous work has conducted research on developing ways to detect a cyberattack and ways to mitigate the adverse effects of the attack. This work studies false data injection (FDI) attacks on the DMS algorithm of a fully inverter-based microgrid in real time. The fully inverter-based microgrid is simulated using an RTDS, an amplifier, an electronic load, a server, a network switch, and a router. The DMS is integrated into the server codes and exchanges data with RTDS through TCP/IP protocols. Moreover, a recurrent neural network (RNN) algorithm is used to detect and mitigate the cyberattack. The effectiveness of the detection and mitigation algorithm is tested under various scenarios using the real-time testbed.
Robust Online Convex Optimization for Disturbance Rejection
Authors: Joyce Lai, Peter Seiler
Date: 2024-05-11 00:00:00

Online convex optimization (OCO) is a powerful tool for learning sequential data, making it ideal for high precision control applications where the disturbances are arbitrary and unknown in advance. However, the ability of OCO-based controllers to accurately learn the disturbance while maintaining closed-loop stability relies on having an accurate model of the plant. This paper studies the performance of OCO-based controllers for linear time-invariant (LTI) systems subject to disturbance and model uncertainty. The model uncertainty can cause the closed-loop to become unstable. We provide a sufficient condition for robust stability based on the small gain theorem. This condition is easily incorporated as an on-line constraint in the OCO controller. Finally, we verify via numerical simulations that imposing the robust stability condition on the OCO controller ensures closed-loop stability.
Large Language Model-aided Edge Learning in Distribution System State Estimation
Authors: Renyou Xie, Xin Yin, Chaojie Li, Guo Chen, Nian Liu, Bo Zhao, ZhaoYang Dong
Date: 2024-05-11 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Missing Values, Multi-Task Learning, State Estimation

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. Firstly, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Secondly, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Thirdly, to alleviate the overfitting of the deep learning-based DSSE, it is reformulated as a multi-task learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.
Controlling network-coupled neural dynamics with nonlinear network control theory
Authors: Zhongye Xia, Weibin Li, Zhichao Liang, Kexin Lou, Quanying Liu
Date: 2024-05-11 00:00:00

This paper addresses the problem of controlling the temporal dynamics of complex nonlinear network-coupled dynamical systems, specifically in terms of neurodynamics. Based on the Lyapunov direct method, we derive a control strategy with theoretical guarantees of controllability. To verify the performance of the derived control strategy, we perform numerical experiments on two nonlinear network-coupled dynamical systems that emulate phase synchronization and neural population dynamics. The results demonstrate the feasibility and effectiveness of our control strategy.
Model Reference Control for Wind Turbine Systems in Full Load Region based on Takagi-Sugeno Fuzzy Systems
Authors: Johannes Brunner, Jens Fortmann, Horst Schulte
Date: 2024-05-10 00:00:00

This paper presents a novel Model Reference Control (MRC) approach for wind turbine (WT) systems in the full load region employing a fuzzy Parallel Distribution Compensation Controller (PDC-C) derived using a Takagi-Sugeno (TS) fuzzy System approach. Through first-order Taylor series expansion, local linear submodels are generated and combined via triangular membership functions to develop a TS descriptor model. From here, the MRC PDC-C is synthesized by a constrained LMI optimization procedure, including damping characteristics of the elastic drive train, to track the desired rotor speed and generator torque based on the reference model dynamics. The controller is tested on the nonlinear WT model in simulation studies under various wind conditions, such as turbulent wind, wind gusts, and a Fault Ride Through (FRT) scenario where the generator torque is set to 0 p.u. for 150 ms.