pwc-archive/papers-with-abstracts huggingface dataset:
Input Delay Compensation for a Class of Switched Linear Systems via Averaging Exact Predictor Feedbacks
Authors: Andreas Katsanikakis, Nikolaos Bekiaris-Liberis
Date: 2025-03-11 00:00:00

The key challenges in design of predictor-based control laws for switched systems with arbitrary switching and long input delay are the potential unavailability of the future values of the switching signal (at current time) and the fact that dwell time may be arbitrary. In the present paper, we resolve these challenges developing a new predictor-based control law that is, essentially, an average of exact predictor feedbacks, each one corresponding to an exact predictor-feedback law for a system that operates only in a single mode. Because the predictor state in our control design does not correspond to an exact predictor, stability can be guaranteed under a restriction on the differences among the system's matrices and controller's gains. This is an unavoidable limitation, for a switching signal whose future values may be unavailable, when no constraint is imposed on the values of delay and dwell time (as it is the case here). We establish (uniform) stability of the closed-loop system employing a Lyapunov functional. The key step in the stability proof is constructive derivation of an estimate of the mismatch between an exact predictor feedback and the average of predictor feedbacks constructed. We illustrate the performance of the proposed predictor-based control law in simulation, including comparisons with alternative, predictor-based control laws.
Spatiotemporal Tubes based Controller Synthesis against Omega-Regular Specifications for Unknown Systems
Authors: Ratnangshu Das, Aiman Aatif Bayezeed, Pushpak Jagtap
Date: 2025-03-11 00:00:00
Tasks: Robot Navigation

This paper provides a discretization-free solution to the synthesis of approx-imation-free closed-form controllers for unknown nonlinear systems to enforce complex properties expressed by $\omega$-regular languages, as recognized by Non-deterministic B\"uchi Automata (NBA). In order to solve this problem, we first decompose NBA into a sequence of reach-avoid problems, which are solved using the Spatiotemporal Tubes (STT) approach. Controllers for each reach-avoid task are then integrated into a hybrid policy that ensures the fulfillment of the desired $\omega$-regular properties. We validate our method through omnidirectional robot navigation and manipulator control case studies.
Geometric Nonlinear Filtering with Almost Global Convergence for Attitude and Bias Estimation on the Special Orthogonal Group
Authors: Farooq Aslam, Muhammad Farooq Haydar, Suhail Akhtar
Date: 2025-03-11 00:00:00

This paper proposes a novel geometric nonlinear filter for attitude and bias estimation on the Special Orthogonal Group $SO(3)$ using matrix measurements. The structure of the proposed filter is similar to that of the continuous-time deterministic multiplicative extended Kalman filter (MEKF). The main difference with the MEKF is the inclusion of curvature correction terms in both the filter gain and gain update equations. These terms ensure that the proposed filter, named the Generalized $SO(3)$-MEKF, renders the desired equilibrium of the estimation error system to be almost globally uniformly asymptotically stable (AGUAS). More precisely, the attitude and bias estimation errors converge uniformly asymptotically to zero for almost all initial conditions except those where the initial angular estimation error equals $\pi$ radians. Moreover, in the case of small estimation errors, the proposed generalized $SO(3)$-MEKF simplifies to the standard $SO(3)$-MEKF with matrix measurements. Simulation results indicate that the proposed filter has similar performance compared to the latter. Thus, the main advantage of the proposed filter over the MEKF is the guarantee of (almost) global uniform asymptotic stability.
Generation and Balancing Capacity in Future Electric Power Systems -- Scenario Analysis Using Bayesian Networks
Authors: Seppo Borenius, Pekka Kekolahti, Petri Mähönen, Matti Lehtonen
Date: 2025-03-11 00:00:00
Tasks: Management

This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyse the evolution of power generation capacity mix, assess the likelihood of different grid management scenarios, and understand the causal relationships underlying these scenarios. A target optimisation was carried out using the constructed Bayesian Network to explore possibilities to minimise grid management complexity. The results of the optimisation reveal that the authorities and stakeholders should prioritise increasing demand response, gas power, and battery storage capacities. These mature technologies are well-suited to guarantee energy adequacy during peak consumption periods, which in Finland typically occur during consecutive cold, dark and windless winter weeks. Although this study focuses on the evolution of the Finnish power grid, the constructed Bayesian Network approach is broadly applicable and can be utilised to explore causal relationships in other countries by employing the designed questionnaire and engaging a panel of experts specific to the country's energy infrastructure.
New Co-Simulation Variants for Emissions and Cost Reduction of Sustainable District Heating Planning
Authors: Haozhen Cheng, Verena Buccoliero, Alexander Kocher, Veit Hagenmeyer, Hüseyin K. Çakmak
Date: 2025-03-11 00:00:00

Classical heating of residential areas is very energy-intensive, so alternatives are needed, including renewable energies and advanced heating technologies. Thus, the present paper introduces a new methodology for comprehensive variant analysis for future district heating planning, aiming at optimizing emissions and costs. For this, an extensive Modelica-based modeling study comprising models of heating center, heat grid pipelines and heating interface units to buildings are coupled in co-simulations. These enable a comparative analysis of the economic feasibility and sustainability for various technologies and energy carriers to be carried out. The new modular and highly parameterizable building model serves for validation of the introduced heat grid model. The results show that bio-methane as an energy source reduces carbon equivalent emissions by nearly 70% compared to conventional natural gas heating, and the use of hydrogen as an energy source reduces carbon equivalent emissions by 77% when equipped with a heat pump. In addition, the use of ground source heat pumps has a high economic viability when economic benefits are taken into account. The study findings highlight the importance of strategic planning and flexible design in the early stages of district development in order to achieve improved energy efficiency and a reduced carbon footprint.
Construction and Control of Validated Highly Configurable Multi-Physics Building Models for Multi-Energy System Analysis in a Co-Simulation Setup
Authors: Haozhen Cheng, Jan stock, André Xhonneux, Hüseyin K. Çakmak, Veit Hagenmeyer
Date: 2025-03-11 00:00:00

Improving energy efficiency by monitoring system behavior and predicting future energy scenarios in light of increased penetration of renewable energy sources are becoming increasingly important, especially for energy systems that distribute and provide heat. On this background, digital twins of cities become paramount in advancing urban energy system planning and infrastructure management. The use of recorded energy data from sensors in district digital twins in collaborative co-simulation platforms is a promising way to analyze detailed system behavior and estimate future scenarios. However, the development and coupling of multi-physics energy system models need to be validated before they can be used for further in-depth analyses. In the present paper, a new multi-physics/-modal and highly configurable building model is presented. Its accuracy and reliability are validated by comparison with data from the TABULA project, ensuring its relevance and applicability to real-world scenarios. The modularity and flexibility with regard to the system configurability of the developed building model is evaluated on various real building types. In addition, the applicability of the building model in a multi-energy system is highlighted by implementing the model in a collaborative co-simulation setup and by coupling it to a district heating grid model in yearly co-simulations. The simulation results for the proposed multi-physical/-modal building modeling concept show a very high level of agreement compared to published reference building data and can therefore be used individually as flexible and modular building models including both thermal and electrical systems for future sector-coupled energy system analyses in view of sustainability.
Ultra-low Power AMOLED Displays for Smart Wearable Applications: Theory and Practice
Authors: Bojia Lyu
Date: 2025-03-11 00:00:00

With the continuous advancement and maturity of AMOLED (Active-Matrix Organic Light Emitting Diode) technology, smart wearable products such as watches and bracelets are increasingly incorporating related technologies as display screen implementation solutions. Using standby time is the most critical product performance measurement indicator at the moment, according to the power supply system design of smart wearable products and customer usage habits. AMOLED displays, as one of the major power-consuming components in smart wearable products, are also subject to extremely stringent power consumption requirements. This paper divides an AMOLED display into five parts: the power chip, the driver chip, the array substrate, the light-emitting structure, and the light-transmitting structure. In this paper, we propose targeted power-saving solutions for each component based on their respective operating principles, subject areas, and the most recent advances in related fields, and we provide the best overall solution by combining the interactions between each component and even the entire system. The relevant solutions have been validated in practice, and there is clear verification data to demonstrate their feasibility.
Coordinated Path Following of UAVs using Event-Triggered Communication over Networks with Digraph Topologies
Authors: Hyungsoo Kang, Isaac Kaminer, Venanzio Cichella, Naira Hovakimyan
Date: 2025-03-11 00:00:00

This article presents a novel time-coordination algorithm based on event-triggered communication to ensure multiple UAVs progress along their desired paths in coordination with one another. In the proposed algorithm, a UAV transmits its progression information to its neighbor UAVs only when a decentralized trigger condition is satisfied. Consequently, it significantly reduces the volume of inter-vehicle communications required to achieve the goal compared with the existing algorithms based on continuous communication. With such intermittent communications, it is shown that a decentralized coordination controller guarantees exponential convergence of the coordination error to a neighborhood of zero. Furthermore, a lower bound on the difference between two consecutive event-triggered times is provided showing that the Zeno behavior is excluded with the proposed algorithm. Lastly, simulation results validate the efficacy of the proposed algorithm.
Forecast-Driven Scenario Generation for Building Energy Management Using Stochastic Optimization
Authors: Hossein Nourollahi Hokmabad, Tala Hemmati Shahsavar, Pedro P. Vergara, Oleksandr Husev, Juri Belikov
Date: 2025-03-11 00:00:00
Tasks: energy management, Management, Stochastic Optimization

Buildings are essential components of power grids, and their energy performance directly affects overall power system operation. This paper presents a novel stochastic optimization framework for building energy management systems, aiming to enhance buildings' energy performance and facilitate their effective integration into emerging intelligent power grids. In this method, solar power generation and building electricity demand forecasts are combined with historical data, leveraging statistical characteristics to generate probability matrices and corresponding scenarios with associated probabilities. These scenarios are then used to solve the stochastic optimization problem, optimizing building energy flow while accounting for existing uncertainties. The results demonstrate that the proposed methodology effectively manages inherent uncertainties while maintaining performance and outperforming rule-based and custom build reinforcement learning based solutions.
Enhancing Vehicle Platooning Safety via Control Node Placement and Sizing under State and Input Bounds
Authors: Yifei She, Shen Wang, Ahmad Taha, Xiaofeng Tao
Date: 2025-03-11 00:00:00

Vehicle platooning with Cooperative Adaptive Cruise Control improves traffic efficiency, reduces energy consumption, and enhances safety but remains vulnerable to cyber-attacks that disrupt communication and cause unsafe actions. To address these risks, this paper investigates control node placement and input bound optimization to balance safety and defense efficiency under various conditions. We propose a two-stage actuator placement and actuator saturation approach, which focuses on identifying key actuators that maximize the system's controllability while operating under state and input constraints. By strategically placing and limiting the input bounds of critical actuators, we ensure that vehicles maintain safe distances even under attack. Simulation results show that our method effectively mitigates the impact of attacks while preserving defense efficiency, offering a robust solution to vehicle platooning safety challenges.
TRUST: Stability and Safety Controller Synthesis for Unknown Dynamical Models Using a Single Trajectory
Authors: Jamie Gardner, Ben Wooding, Amy Nejati, Abolfazl Lavaei
Date: 2025-03-11 00:00:00
Tasks: LEMMA

TRUST is an open-source software tool developed for data-driven controller synthesis of dynamical systems with unknown mathematical models, ensuring either stability or safety properties. By collecting only a single input-state trajectory from the unknown system and satisfying a rank condition that ensures the system is persistently excited according to the Willems et al.'s fundamental lemma, TRUST aims to design either control Lyapunov functions (CLF) or control barrier certificates (CBC), along with their corresponding stability or safety controllers. The tool implements sum-of-squares (SOS) optimization programs solely based on data to enforce stability or safety properties across four system classes: (i) continuous-time nonlinear polynomial systems, (ii) continuous-time linear systems, (iii) discrete-time nonlinear polynomial systems, and (iv) discrete-time linear systems. TRUST is a Python-based web application featuring an intuitive, reactive graphic user interface (GUI) built with web technologies. It can be accessed at https://trust.tgo.dev or installed locally, and supports both manual data entry and data file uploads. Leveraging the power of the Python backend and a JavaScript frontend, TRUST is designed to be highly user-friendly and accessible across desktop, laptop, tablet, and mobile devices. We apply TRUST to a set of physical benchmarks with unknown dynamics, ensuring either stability or safety properties across the four supported classes of models.
Electrifying Heavy-Duty Trucks: Battery-Swapping vs Fast Charging
Authors: Ruiting Wang, Antoine Martinez, Zaid Allybokus, Wente Zeng, Nicolas Obrecht, Scott Moura
Date: 2025-03-11 00:00:00

The advantages and disadvantages of Battery Swapping Stations (BSS) for heavy-duty trucks are poorly understood, relative to Fast Charging Stations (FCS) systems. This study evaluates these two charging mechanisms for electric heavy-duty trucks, aiming to compare the systems' efficiency and identify their optimal design. A model was developed to address the planning and operation of BSS in a charging network, considering in-station batteries as assets for various services. We assess performance metrics including transportation efficiency and battery utilization efficiency. Our evaluation reveals that BSS significantly increased transportation efficiency by reducing vehicle downtime compared to fast charging, but may require more batteries. BSS with medium-sized batteries offers improved transportation efficiency in terms of time and labor. FCS-reliant trucks require larger batteries to compensate for extended charging times. To understand the trade-off between these two metrics, a cost-benefit analysis was performed under different scenarios involving potential shifts in battery prices and labor costs. Additionally, BSS shows potential for significant $\text{CO}_2$ emission reductions and increased profitability through energy arbitrage and grid ancillary services. These findings emphasize the importance of integrating BSS into future electric truck charging networks and adopting carbon-aware operational frameworks.
From Data to Global Asymptotic Stability of Unknown Large-Scale Networks with Provable Guarantees
Authors: Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
Date: 2025-03-11 00:00:00

We offer a compositional data-driven scheme for synthesizing controllers that ensure global asymptotic stability (GAS) across large-scale interconnected networks, characterized by unknown mathematical models. In light of each network's configuration composed of numerous subsystems with smaller dimensions, our proposed framework gathers data from each subsystem's trajectory, enabling the design of local controllers that ensure input-to-state stability (ISS) properties over subsystems, signified by ISS Lyapunov functions. To accomplish this, we require only a single input-state trajectory from each unknown subsystem up to a specified time horizon, fulfilling certain rank conditions. Subsequently, under small-gain compositional reasoning, we leverage ISS Lyapunov functions derived from data to offer a control Lyapunov function (CLF) for the interconnected network, ensuring GAS certificate over the network. We demonstrate that while the computational complexity for designing a CLF increases polynomially with the network dimension using sum-of-squares (SOS) optimization, our compositional data-driven approach significantly mitigates it to \emph{linear} with respect to the number of subsystems. We showcase the efficacy of our data-driven approach over a set of benchmarks, involving physical networks with diverse interconnection topologies.
Data-Driven Dynamic Controller Synthesis for Discrete-Time General Nonlinear Systems
Authors: Behrad Samari, Abolfazl Lavaei
Date: 2025-03-11 00:00:00

Synthesizing safety controllers for general nonlinear systems is a highly challenging task, particularly when the system models are unknown, and input constraints are present. While some recent efforts have explored data-driven safety controller design for nonlinear systems, these approaches are primarily limited to specific classes of nonlinear dynamics (e.g., polynomials) and are not applicable to general nonlinear systems. This paper develops a direct data-driven approach for discrete-time general nonlinear systems, facilitating the simultaneous learning of control barrier certificates (CBCs) and dynamic controllers to ensure safety properties under input constraints. Specifically, by leveraging the adding-one-integrator approach, we incorporate the controller's dynamics into the system dynamics to synthesize a virtual static-feedback controller for the augmented system, resulting in a dynamic safety controller for the actual dynamics. We collect input-state data from the augmented system during a finite-time experiment, referred to as a single trajectory. Using this data, we learn augmented CBCs and the corresponding virtual safety controllers, ensuring the safety of the actual system and adherence to input constraints over a finite time horizon. We demonstrate that our proposed conditions boil down to some data-dependent linear matrix inequalities (LMIs), which are easy to satisfy. We showcase the effectiveness of our data-driven approach through two case studies: one exhibiting significant nonlinearity and the other featuring high dimensionality.
A Three-Dimensional Pursuit-Evasion Game Based on Fuzzy Actor-Critic Learning Algorithm
Authors: Penglin Hu
Date: 2025-03-11 00:00:00

Most of the existing research on pursuit-evasion game (PEG) is conducted in a two-dimensional (2D) environment. In this paper, we investigate the PEG in a 3D space. We extend the Apollonius circle (AC) to the 3D space and introduce its detailed analytical form. To enhance the capture efficiency, we derive the optimal motion space for both the pursuer and the evader. To address the issue arising from a discrete state space, we design a fuzzy actor-critic learning (FACL) algorithm to obtain the agents' strategies. To improve learning performance, we devise a reward function for the agents, which enables obstacle avoidance functionality. The effectiveness of the proposed algorithm is validated through simulation experiments.
Integrated Energy Management for Operational Cost Optimization in Community Microgrids
Authors: Moslem Uddin, Huadong Mo, Daoyi Dong
Date: 2025-03-11 00:00:00
Tasks: energy management, Management

This study presents an integrated energy management strategy for cost optimization in multi-energy community microgrids (MGs). The proposed approach combines storage-based peak shaving, economic dispatch of diesel generators, and efficient utilization of renewable energy sources to enhance energy management in community MGs. The efficacy of the energy management system (EMS) was validated through a simulation case study for a rural Australian community. The results demonstrate that the proposed EMS effectively reduces the peak energy demand by up to 43%, lowers operational costs by 84.63% (from $189,939/year to $29,188/year), and achieves a renewable energy utilization of 92.3%, up from 47.8% in the base system. Furthermore, the levelized cost of energy was reduced by 14.21% to $0.163/kWh. The strategy ensures an uninterrupted power supply during grid outages by utilizing DGs and battery energy storage systems. The environmental benefits included a 196.4% reduction in CO2 emissions and 100% reductions in CO, unburned hydrocarbons, and particulate matter. These findings validate the feasibility of the proposed EMS in achieving cost-effective, reliable, and sustainable energy management in community MGs. These findings contribute to the field by introducing a novel approach and demonstrating the practical feasibility of multi-energy MGs.