pwc-archive/papers-with-abstracts huggingface dataset:
Environment-Aware Scheduling of URLLC and Sensing Services for Smart Industries
Authors: Navid Keshtiarast, Pradyumna Kumar Bishoyi, Marina Petrova
Date: 2025-03-07 00:00:00
Tasks: Integrated sensing and communication, ISAC, Scheduling

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) data in a time-sharing manner. Scheduling fixed time slots for both sensing and communication is inefficient for the InF environment, as the instantaneous environmental changes necessitate a higher frequency of sensing operations to accurately detect the AGV. To address this issue, we propose an environment-aware scheduling scheme, in which we first formulate an optimization problem to maximize the probability of detection of AGV while considering the survival time constraint of URLLC data. Subsequently, utilizing the Nash bargaining theory, we propose an adaptive time-sharing scheme that assigns sensing duration in accordance with the environmental clutter density and distributes time to URLLC depending on the incoming traffic rate. Using our own Python-based discrete-event link-level simulator, we demonstrate the effectiveness of our proposed scheme over the baseline scheme in terms of probability of detection and downlink latency.
Identification of Minimally Restrictive Assembly Sequences using Supervisory Control Theory
Authors: Martina Vinetti, Martin Fabian
Date: 2025-03-07 00:00:00
Tasks: Blocking, Management, valid

Modern assembly processes require flexibility and adaptability to handle increasing product variety and customization. Traditional assembly planning methods often prioritize finding an optimal assembly sequence, overlooking the requirements of contemporary manufacturing. This work uses Supervisory Control Theory to systematically generate all feasible assembly sequences while ensuring compliance with precedence and process constraints. By synthesizing a controllable, non-blocking, and minimally restrictive supervisor, the proposed method guarantees that only valid sequences are allowed, balancing flexibility and constraint enforcement. The obtained sequences can serve as a basis for further optimization or exception management, improving responsiveness to disruptions.
On reconstructing high derivatives of noisy time-series with confidence intervals
Authors: Mazen Alamir
Date: 2025-03-07 00:00:00
Tasks: Time Series

Reconstructing high derivatives of noisy measurements is an important step in many control, identification and diagnosis problems. In this paper, a heuristic is proposed to address this challenging issue. The framework is based on a dictionary of identified models indexed by the bandwidth, the noise level and the required degrees of derivation. Each model in the dictionary is identified via cross-validation using tailored learning data. It is also shown that the proposed approach provides heuristically defined confidence intervals on the resulting estimation. The performance of the framework is compared to the state-of-the-art available algorithms showing noticeably higher accuracy. Although the results are shown for up to the 4-th derivative, higher derivation orders can be used with comparable results.
Sparse identification of nonlinear dynamics with high accuracy and reliability under noisy conditions for applications to industrial systems
Authors: Shuichi Yahagi, Ansei Yonezawa, Hiroki Seto, Heisei Yonezawa, Itsuro Kajiwara
Date: 2025-03-07 00:00:00
Tasks: Ensemble Learning

This paper proposes a sparse identification of nonlinear dynamics (SINDy) with control and exogenous inputs for highly accurate and reliable prediction. The method is applied to the diesel engine airpath systems, which are known as a nonlinear complicated industrial system. Although SINDy is recognized as a remarkable approach for identifying nonlinear systems, several challenges remain. Its application to industrial systems remains limited, and multi-step predictions are not guaranteed due to overfitting and noisy data. This phenomenon is often caused by the increase in basis functions resulting from the extension of coordinates, such as time-delay embedding. To address these problems, we propose an emphasized SINDy by incorporating ensemble learning, elite gathering, and classification techniques while keeping convex calculation. The proposed method employs library bagging and extracts elites with an R-squared greater than 90%. Then, clustering is performed on the surviving elites because physically motivated basis functions are not always available, and the elites obtained do not always show the same trends. After the classification, discrete model candidates are obtained by taking the mean of each classified elite. Finally, the best model is selected. The simulation results show that the proposed method realizes multi-step prediction for the airpath system, which is known to be a complicated industrial system under noisy conditions.
Enhanced Koopman Operator Approximation for Nonlinear Systems Using Broading Learning System
Authors: Yangjun Sun, Zhiliang Liu
Date: 2025-03-07 00:00:00

Traditional control methods often show limitations in dealing with complex nonlinear systems, especially when it is difficult to accurately obtain the exact system model, and the control accuracy and stability are difficult to guarantee. To solve this problem, the Koopman operator theory provides an effective method to linearise nonlinear systems, which simplifies the analysis and control of the system by mapping the nonlinear dynamics into a high-dimensional space. However, the existing extended dynamical mode decomposition (EDMD) methods suffer from randomness in the selection of basis functions, which leads to bias in the finite-dimensional approximation to the Koopman operator, thus affecting the accuracy of model prediction. To solve this problem, this paper proposes a BLS-EDMD method based on the Broad learning system (BLS) network. The method achieves a high-precision approximation to the Koopman operator by learning more accurate basis functions, which significantly improves the prediction ability of the model. Building on this, we further develop a model predictive controller (MPC) called BE-MPC. This controller directly utilises the high-dimensional and high-precision predictors generated by BLS-EDMD to predict the system state more accurately, thus achieving precise control of the underwater unmanned vehicle (UUV), and its effectiveness is verified by simulation.
Optimal and Robust Multivariable Reaching Time Sliding Mode Control Design
Authors: J. C. Geromel, L. Hsu, E. V. L. Nunes
Date: 2025-03-07 00:00:00
Tasks: Unity

This paper addresses two minimum reaching time control problems within the context of finite stable systems. The well-known Variable Structure Control (VSC) and Unity Vector Control (UVC) strategies are analyzed, with the primary objective of designing optimal and robust state feedback gains that ensure minimum finite time convergence to the origin. This is achieved in the presence of convex bounded parameter uncertainty and norm-bounded exogenous disturbances. In both cases, the optimality conditions are expressed through Linear Matrix Inequalities (LMIs), which are solved efficiently within the framework of multivariable systems using existing numerical tools. The theoretical results are demonstrated with two practically motivated examples.
ISC-POMDPs: Partially Observed Markov Decision Processes with Initial-State Dependent Costs
Authors: Timothy L. Molloy
Date: 2025-03-06 00:00:00
Tasks: Robot Navigation

We introduce a class of partially observed Markov decision processes (POMDPs) with costs that can depend on both the value and (future) uncertainty associated with the initial state. These Initial-State Cost POMDPs (ISC-POMDPs) enable the specification of objectives relative to a priori unknown initial states, which is useful in applications such as robot navigation, controlled sensing, and active perception, that can involve controlling systems to revisit, remain near, or actively infer their initial states. By developing a recursive Bayesian fixed-point smoother to estimate the initial state that resembles the standard recursive Bayesian filter, we show that ISC-POMDPs can be treated as POMDPs with (potentially) belief-dependent costs. We demonstrate the utility of ISC-POMDPs, including their ability to select controls that resolve (future) uncertainty about (past) initial states, in simulation.
Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review
Authors: Arash J. Khabbazi, Elias N. Pergantis, Levi D. Reyes Premer, Panagiotis Papageorgiou, Alex H. Lee, James E. Braun, Gregor P. Henze, Kevin J. Kircher
Date: 2025-03-06 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL)

A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information -- such as experiment scopes, locations, and durations -- this paper analyzes experiment protocols and their influence on performance estimates. We find that 71% of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29% that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16% in residential buildings and 13% in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.
Dissipativity-Based Distributed Control and Communication Topology Co-Design for Voltage Regulation and Current Sharing in DC Microgrids
Authors: Mohammad Javad Najafirad, Shirantha Welikala
Date: 2025-03-06 00:00:00

This paper presents a novel dissipativity-based distributed droop-free control approach for voltage regulation and current sharing in DC microgrids (MGs) comprised of an interconnected set of distributed generators (DGs), loads, and power lines. First, we describe the closed-loop DC MG as a networked system where the DGs and lines (i.e., subsystems) are interconnected via a static interconnection matrix. This interconnection matrix demonstrates how the inputs, outputs, and disturbances of DGs and lines are connected in a DC MG. Each DG has a local controller and a distributed global controller. To design the controllers, we use the dissipativity properties of the subsystems and formulate a linear matrix inequality (LMI) problem. To support the feasibility of this problem, we identify a set of necessary local and global conditions that we then enforce in a specifically developed LMI-based local controller design process. In contrast to existing DC MG control solutions, our approach proposes a unified framework for co-designing the distributed controller and communication topology. As the co-design process is LMI-based, it can be efficiently implemented and evaluated using existing convex optimization tools. The effectiveness of the proposed solution was verified by simulating an islanded DC MG in a MATLAB/Simulink environment under different scenarios, such as load changes and topological constraint changes, and then comparing the performance with a recent droop control algorithm.
An Extended State Space Model of Aggregated Electric Vehicles for Flexibility Estimation and Power Control
Authors: Yiping Liu, Xiaozhe Wang, Geza Joos
Date: 2025-03-06 00:00:00

The increasing penetration of electric vehicles (EVs) can provide substantial electricity to the grid, supporting the grids' stability. The state space model (SSM) has been proposed as an effective modeling method for power prediction and centralized control of aggregated EVs, offering low communication requirements and computational complexity. However, the SSM may overlook specific scenarios, leading to significant prediction and control inaccuracies. This paper proposes an extended state space model (eSSM) for aggregated EVs and develops associated control strategies. By accounting for the limited flexibility of fully charged and discharged EVs, the eSSM more accurately captures the state transition dynamics of EVs in various states of charge (SOC). Comprehensive simulations show that the eSSM will provide more accurate predictions of the flexibility and power trajectories of aggregated EVs, and more effectively tracks real-time power references compared to the conventional SSM method.
Multistage Economic MPC for Systems with a Cyclic Steady State: A Gas Network Case Study
Authors: Sakshi S. Naik, Lavinia M. Ghilardi, Robert B. Parker, Lorenz T. Biegler
Date: 2025-03-06 00:00:00
Tasks: Model Predictive Control

Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. In this paper, we develop a formulation for multistage economic MPC which directly minimizes an economic objective function. The multistage economic MPC framework is extended for systems with a cyclic steady state (CSS) and stability is guaranteed by employing a Lyapunov-based stability constraint. The multistage economic MPC framework is validated on two natural gas network case studies to minimize the net energy consumption during gas transmission. In both instances, the multistage economic MPC effectively manages uncertain demands by preventing constraint violations and guides the network to its optimal cyclic operating conditions. The Lyapunov function remains bounded in both instances, validating the robust stability of the controller.
Grid-Aware Islanding and Resynchronisation of AC/DC Microgrids
Authors: Willem Lambrichts, Jules Mace, Drazen Dujic, Mario Paolone
Date: 2025-03-06 00:00:00

This paper proposes an optimal, grid-aware control framework for the islanding, island-operation and resynchronisation of hybrid AC/DC microgrids. The optimal control framework is based on a formally derived linearized load-flow model for multiterminal hybrid AC/DC networks. The load flow model integrates the AC grid, DC grid, and interfacing converters (IC) into a unified representation. This work extends an existing load flow model to include the ICs' grid-forming operation. In traditional islanding control frameworks, the grid-forming converter is typically interfaced with an energy storage system that can provide bidirectional power to maintain the power balance. The proposed framework, however, allows the ICs to operate as the grid-forming unit while being connected to a DC grid rather than a single resource. This configuration allows for a wider operating range and, thus, a more flexible control. Furthermore, the optimal grid-aware control framework can steer the system to ensure a feasible operation without any grid constraint violations before, during, and after the islanding manoeuvre. The framework also guarantees smooth transitions, i.e., without any significant transient behaviour, when transitioning between grid-connected and islanding operations. The optimal control framework is experimentally validated on a 27-bus hybrid AC/DC network consisting of 3 ICs that interface the AC and DC networks. The hybrid grid hosts various controllable and stochastic resources.
AOLO: Analysis and Optimization For Low-Carbon Oriented Wireless Large Language Model Services
Authors: Xiaoqi Wang, Hongyang Du, Yuehong Gao, Dong In Kim
Date: 2025-03-06 00:00:00
Tasks: Deep Reinforcement Learning, Language Modeling, Language Modelling, Large Language Model

Recent advancements in large language models (LLMs) have led to their widespread adoption and large-scale deployment across various domains. However, their environmental impact, particularly during inference, has become a growing concern due to their substantial energy consumption and carbon footprint. Existing research has focused on inference computation alone, overlooking the analysis and optimization of carbon footprint in network-aided LLM service systems. To address this gap, we propose AOLO, a framework for analysis and optimization for low-carbon oriented wireless LLM services. AOLO introduces a comprehensive carbon footprint model that quantifies greenhouse gas emissions across the entire LLM service chain, including computational inference and wireless communication. Furthermore, we formulate an optimization problem aimed at minimizing the overall carbon footprint, which is solved through joint optimization of inference outputs and transmit power under quality-of-experience and system performance constraints. To achieve this joint optimization, we leverage the energy efficiency of spiking neural networks (SNNs) by adopting SNN as the actor network and propose a low-carbon-oriented optimization algorithm, i.e., SNN-based deep reinforcement learning (SDRL). Comprehensive simulations demonstrate that SDRL algorithm significantly reduces overall carbon footprint, achieving an 18.77% reduction compared to the benchmark soft actor-critic, highlighting its potential for enabling more sustainable LLM inference services.
Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy
Authors: Donglai Ma, Xiaoyu Cao, Bo Zeng, Qing-Shan Jia, Chen Chen, Qiaozhu Zhai, Xiaohong Guan
Date: 2025-03-06 00:00:00
Tasks: Scheduling, Stochastic Optimization

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitudes.
Stability analysis for nonlinear compressor system and active adaptive controller against surge with antisurge valve
Authors: Seyed Mohammad Hosseindokht
Date: 2025-03-06 00:00:00

In this paper, a compressor system is analyzed in order to show its characteristics and design a control scheme to improve its efficiency. A mathematical technique has been created to forecast the onset of surge and instability in a compressor chart, drawing from the nonlinear Greitzer and Moore model. This approach employs the phase plane and Jacobian matrix to identify both stable and unstable regions within the compressor, as well as to capture the limit cycle within the unstable region. A predictive analytical approach for anticipating compressor surge and instability is of great importance in system instrumentation and control. State space model is built up by nonlinear Greitzer equations. Validation from previous study about especial compressor will be considered for evaluation of mathematic method. Upstream flow acts as a disturbance to control loop and controller cannot satisfy desired requirements with flow variances, ergo it is essential that controller is adapted to new conditions. Since control signal is linearly related to system output, a PD controller is used to control compressor system. An adaptive PD controller is designed with MRAS method based on a reference model. Adaptive controller can stabilize compressor and increase its efficiency in the presence of any disturbances. Simulation results shows that an adaptive controller can provide good performance and convergence in case of speed changes by adapting gain parameters, and adaptive will be compared with normal PID. Finally, controller stability is investigated.
Performance Analysis of Multirate Systems: A Direct Frequency-Domain Identification Approach
Authors: Max van Haren, Lennart Blanken, Tom Oomen
Date: 2025-03-06 00:00:00

Frequency-domain performance analysis of intersample behavior in sampled-data and multirate systems is challenging due to the lack of a frequency-separation principle, and systematic identification techniques are lacking. The aim of this \manuscript is to develop an efficient technique for identifying the full intersample performance in the frequency-domain for closed-loop multirate systems, in particular the Performance Frequency Gain (PFG). Through local modeling techniques, aliased frequency components are effectively disentangled when identifying the PFG, which is directly facilitated by frequency-lifting the multirate system to a multivariable time-invariant representation. The developed method accurately and directly identifies the PFG in a single identification experiment. Finally, the developed method is experimentally validated on a prototype motion system, showing accurate identification of frequency-domain representations for the multirate system, including the PFG.