pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)
Authors: Farhad Mehdifar, Mani H. Dhullipalla, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
Date: 2025-03-25 00:00:00
Tasks: Distributed Optimization

This paper addresses the problem of collaboratively satisfying long-term spatial constraints in multi-agent systems. Each agent is subject to spatial constraints, expressed as inequalities, which may depend on the positions of other agents with whom they may or may not have direct communication. These constraints need to be satisfied asymptotically or after an unknown finite time. The agents' objective is to collectively achieve a formation that fulfills all constraints. The problem is initially framed as a centralized unconstrained optimization, where the solution yields the optimal configuration by maximizing an objective function that reflects the degree of constraint satisfaction. This function encourages collaboration, ensuring agents help each other meet their constraints while fulfilling their own. When the constraints are infeasible, agents converge to a least-violating solution. A distributed consensus-based optimization scheme is then introduced, which approximates the centralized solution, leading to the development of distributed controllers for single-integrator agents. Finally, simulations validate the effectiveness of the proposed approach.
Nordic perspective on System Integrity Protection Schemes in relation to capacity allocation
Authors: Gabriel Malmer, Arvid Rolander, Emil Hillberg, Olof Samuelsson, Susanne Ackeby, Lars Nordström
Date: 2025-03-25 00:00:00
Tasks: Relation

The urgent need to address climate change prompts societies worldwide to adopt carbon neutral energy and electrification. To facilitate this, a range of technologies and policies will be needed. Alternatives to traditional power grid reinforcement, such as grid-enhancing technologies and system automation, are particularly attractive due to their potentially low cost and fast deployment time. One alternative is System Integrity Protection Schemes (SIPS) - automatic and curative remedial actions (RAs) which can boost grid transfer capacities without compromising with reliability since they can act faster than manual control. The use of SIPS however is scattered, with limited coordination between countries, and the full potential of using SIPS for capacity enhancement is not yet realized. The aim of this paper is to provide a case study and comparison of SIPS in the Nordic countries, particularly in relation to capacity allocation. It also seeks to harmonize terminology relating to ancillary services, RAs, and SIPS. Finally, it examines and compares the inclusion of RAs and SIPS in different Capacity Calculation Methodologies (CCMs). In both main EU CCMs - Net Transfer Capacity (NTC) and Flow-Based (FB) - RAs play a pronounced role. The paper is based on a survey and interviews with Nordic stakeholders, along with a literature review and analysis of public data. The results indicate a large variation in SIPS use across the Nordics. Regarding terminology, we suggest that SIPS is a subcategory of RAs which overlaps with ancillary services. Concerning CCMs, NTC is unable to fully represent capacity constraints in meshed AC systems, which in turn hinders systematic capacity enhancement using RAs. FB on the other hand explicitly includes RAs in the capacity domain. A lower bound for the economic value of RAs can be calculated, amounting to 11.5 million EUR in the Nordics in Nov and Dec 2024.
Optimal Safe Sequencing and Motion Control for Mixed Traffic Roundabouts
Authors: Yingqing Chen, Christos G. Cassandras
Date: 2025-03-25 00:00:00
Tasks: Model Predictive Control

This paper develops an Optimal Safe Sequencing (OSS) control framework for Connected and Automated Vehicles (CAVs) navigating a single-lane roundabout in mixed traffic, where both CAVs and Human-Driven Vehicles (HDVs) coexist. The framework jointly optimizes vehicle sequencing and motion control to minimize travel time, energy consumption, and discomfort while ensuring speed-dependent safety guarantees and adhering to velocity and acceleration constraints. This is achieved by integrating (a) a Safe Sequencing (SS) policy that ensures merging safety without requiring any knowledge of HDV behavior, and (b) a Model Predictive Control with Control Lyapunov Barrier Functions (MPC-CLBF) framework, which optimizes CAV motion control while mitigating infeasibility and myopic control issues common in the use of Control Barrier Functions (CBFs) to provide safety guarantees. Simulation results across various traffic demands, CAV penetration rates, and control parameters demonstrate the framework's effectiveness and stability.
A multiobjective approach to robust predictive control barrier functions for discrete-time systems
Authors: Alexandre Didier, Melanie N. Zeilinger
Date: 2025-03-25 00:00:00

We present an optimisation-based approach to ensure robust asymptotic stability stability of a desired set in the state space of nonlinear dynamical systems, while optimising a general control objective. The approach relies on the decrease of a robust predictive control barrier function (PCBF), which is defined as the optimal value function of a slack minimisation problem with respect to the target set. We show continuity of the proposed robust PCBF, allowing the introduction of a decrease constraint in the control objective minimisation. The PCBF decrease is given with respect to a warmstart value based on a feasible solution at the prior time step. Thereby, the control objective can be optimised while ensuring robust asymptotic stability of the target set. We demonstrate the effectiveness of the proposed formulation on a linear space rendezvous and nonlinear lane changing problem.
Iterative Learning Predictive Control for Constrained Uncertain Systems
Authors: Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan, John Lygeros
Date: 2025-03-25 00:00:00
Tasks: Model Predictive Control

Iterative learning control (ILC) improves the performance of a repetitive system by learning from previous trials. ILC can be combined with Model Predictive Control (MPC) to mitigate non-repetitive disturbances, thus improving overall system performance. However, existing approaches either assume perfect model knowledge or fail to actively learn system uncertainties, leading to conservativeness. To address these limitations we propose a binary mixed-integer ILC scheme, combined with a convex MPC scheme, that ensures robust constraint satisfaction, non-increasing nominal cost, and convergence to optimal performance. Our scheme is designed for uncertain nonlinear systems subject to both bounded additive stochastic noise and additive uncertain components. We showcase the benefits of our scheme in simulation.
Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization
Authors: Shengbo Wang, Ke Li, Zheng Yan, Zhenyuan Guo, Song Zhu, Guanghui Wen, Shiping Wen
Date: 2025-03-25 00:00:00
Tasks: Bayesian Optimization

Safety is of paramount importance in control systems to avoid costly risks and catastrophic damages. The control barrier function (CBF) method, a promising solution for safety-critical control, poses a new challenge of enhancing control performance due to its direct modification of original control design and the introduction of uncalibrated parameters. In this work, we shed light on the crucial role of configurable parameters in the CBF method for performance enhancement with a systematical categorization. Based on that, we propose a novel framework combining the CBF method with Bayesian optimization (BO) to optimize the safe control performance. Considering feasibility/safety-critical constraints, we develop a safe version of BO using the barrier-based interior method to efficiently search for promising feasible configurable parameters. Furthermore, we provide theoretical criteria of our framework regarding safety and optimality. An essential advantage of our framework lies in that it can work in model-agnostic environments, leaving sufficient flexibility in designing objective and constraint functions. Finally, simulation experiments on swing-up control and high-fidelity adaptive cruise control are conducted to demonstrate the effectiveness of our framework.
A Framework for Predicting Runtime Savings from Discrete-Event Simulation Model Simplification Operations
Authors: Mohd Shoaib, Navonil Mustafee, Varun Ramamohan
Date: 2025-03-25 00:00:00

Abstraction or substitution and aggregation are the most widely used simulation model simplification operations. Abstraction involves replacing subsystems within a discrete-event simulation (DES) with one or more quantities - typically random variables - representing the lengths of stay in the subsystems(s) in question to create a `simplified' system comprising only of subsystems of interest to the analysis at hand. Aggregation involves replacing more than one subsystem of the original `parent' simulation with a single subsystem. However, the model simplification process itself can be expensive, in terms of the computational runtime and effort required to collect the data required to estimate the distributions of the length of stay variables, the distribution-fitting process, and testing and validation of the simplified model. Moreover, the savings in simulation runtime that the simplification process yields is \textit{a priori} unknown to the modeller. In this context, a method that predicts the runtime savings (RS) from DES model simplification operations before their execution - at the conceptualisation stage of the simplified model development process - may help judge whether its development is indeed worth undertaking. In this paper, we present a queueing-theoretic framework for the prediction of RS from model simplification operations. Our framework is applicable for DES models comprising $M/M/, M/G/ \text{ and } G/G/$ subsystems. The performance of the RS prediction framework is demonstrated using multiple computational experiments. Our proposed framework contributes to the literature around DES model complexity and more broadly to DES runtime prediction.
Bridging the Sim-to-real Gap: A Control Framework for Imitation Learning of Model Predictive Control
Authors: Seungtaek Kim, Jonghyup Lee, Kyoungseok Han, Seibum B. Choi
Date: 2025-03-25 00:00:00
Tasks: Imitation Learning, Model Predictive Control

To address the computational challenges of Model Predictive Control (MPC), recent research has studied on using Deep Neural Networks (DNNs) trained through imitation learning to approximate the MPC. However, this introduces a common issue in learning-based control: the simulation-to-reality (sim-to-real) gap. Therefore, Domain Randomization (DR) has been widely used to mitigate this gap by introducing perturbations in the source domain. However, this led to low data collection efficiency and an overly conservative control strategy. This study proposes a new control framework that deals with this issue from a control perspective inspired by Robust Tube MPC. The framework ensures the DNN operates in the same environment as the source domain, handling the sim-to-real gap with great data collection efficiency. Moreover, a parameter governor is introduced to address the DNN's inability to adapt to model parameter variations, enabling the system to satisfy MPC constraints more robustly under changing conditions. The proposed framework was validated through a cart-pole system case study compared by DR baselines, demonstrating that a single MPC-demonstrated trajectory in the source domain was sufficient for controlling the cart-pole in the target domain. Furthermore, the system effectively handled model parameter variations, allowing for a less conservative control.
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
Authors: Gautham Udayakumar Bekal, Ahmed Ghareeb, Ashish Pujari
Date: 2025-03-24 00:00:00
Tasks: Continual Learning, Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Buildings with Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in ensuring indoor comfort and efficiency. While traditionally governed by physics-based models, the emergence of big data has enabled data-driven methods like Deep Reinforcement Learning (DRL). However, Reinforcement Learning (RL)-based techniques often suffer from sample inefficiency and limited generalization, especially across varying HVAC systems. We introduce a model-based reinforcement learning framework that uses a Hypernetwork to continuously learn environment dynamics across tasks with different action spaces. This enables efficient synthetic rollout generation and improved sample usage. Our approach demonstrates strong backward transfer in a continual learning setting after training on a second task, minimal fine-tuning on the first task allows rapid convergence within just 5 episodes and thus outperforming Model Free Reinforcement Learning (MFRL) and effectively mitigating catastrophic forgetting. These findings have significant implications for reducing energy consumption and operational costs in building management, thus supporting global sustainability goals. Keywords: Deep Reinforcement Learning, HVAC Systems Control, Hypernetworks, Transfer and Continual Learning, Catastrophic Forgetting
Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
Authors: Chayan Banerjee, Kien Nguyen, Clinton Fookes
Date: 2025-03-24 00:00:00
Tasks: Benchmarking, OpenAI Gym, Reinforcement Learning (RL), Scheduling

Mining process optimization particularly truck dispatch scheduling is a critical factor in enhancing the efficiency of open pit mining operations However the dynamic and stochastic nature of mining environments characterized by uncertainties such as equipment failures truck maintenance and variable haul cycle times poses significant challenges for traditional optimization methods While Reinforcement Learning RL has shown promise in adaptive decision making for mining logistics its practical deployment requires rigorous evaluation in realistic and customizable simulation environments The lack of standardized benchmarking environments limits fair algorithm comparisons reproducibility and the real world applicability of RL based approaches in open pit mining settings To address this challenge we introduce Mining Gym a configurable open source benchmarking environment designed for training testing and comparing RL algorithms in mining process optimization Built on Discrete Event Simulation DES and seamlessly integrated with the OpenAI Gym interface Mining Gym provides a structured testbed that enables the direct application of advanced RL algorithms from Stable Baselines The framework models key mining specific uncertainties such as equipment failures queue congestion and the stochasticity of mining processes ensuring a realistic and adaptive learning environment Additionally Mining Gym features a graphical user interface GUI for intuitive mine site configuration a comprehensive data logging system a built in KPI dashboard and real time visual representation of the mine site These capabilities facilitate standardized reproducible evaluations across multiple RL strategies and baseline heuristics
Insights into the explainability of Lasso-based DeePC for nonlinear systems
Authors: Gianluca Giacomelli, Simone Formentin, Victor G. Lopez, Matthias A. Müller, Valentina Breschi
Date: 2025-03-24 00:00:00

Data-enabled Predictive Control (DeePC) has recently gained the spotlight as an easy-to-use control technique that allows for constraint handling while relying on raw data only. Initially proposed for linear time-invariant systems, several DeePC extensions are now available to cope with nonlinear systems. Nonetheless, these solutions mainly focus on ensuring the controller's effectiveness, overlooking the explainability of the final result. As a step toward explaining the outcome of DeePC for the control of nonlinear systems, in this paper, we focus on analyzing the earliest and simplest DeePC approach proposed to cope with nonlinearities in the controlled system, using a Lasso regularization. Our theoretical analysis highlights that the decisions undertaken by DeePC with Lasso regularization are unexplainable, as control actions are determined by data incoherent with the system's local behavior. This result is true even when the available input/output samples are grouped according to the different operating conditions explored during data collection. Our numerical study confirms these findings, highlighting the benefits of data grouping in terms of performance while showing that explainability remains a challenge in control design via DeePC.
QSID-MPC: Model Predictive Control with System Identification from Quantized Data
Authors: Shahab Ataei, Dipankar Maity, Debdipta Goswami
Date: 2025-03-24 00:00:00
Tasks: Model Predictive Control, Quantization

Least-square system identification is widely used for data-driven model-predictive control (MPC) of unknown or partially known systems. This letter investigates how the system identification and subsequent MPC is affected when the state and input data is quantized. Specifically, we examine the fundamental connection between model error and quantization resolution and how that affects the stability and boundedness of the MPC tracking error. Furthermore, we demonstrate that, with a sufficiently rich dataset, the model error is bounded by a function of quantization resolution and the MPC tracking error is also ultimately bounded similarly. The theory is validated through numerical experiments conducted on two different linear dynamical systems.
Temporally-Consistent Bilinearly Recurrent Autoencoders for Control Systems
Authors: Ananda Chakrabarti, Indranil Nayak, Debdipta Goswami
Date: 2025-03-24 00:00:00

This paper introduces the temporally-consistent bilinearly recurrent autoencoder (tcBLRAN), a Koopman operator based neural network architecture for modeling a control-affine nonlinear control system. The proposed method extends traditional Koopman autoencoders (KAE) by incorporating bilinear recurrent dynamics that are consistent across predictions, enabling accurate long-term forecasting for control-affine systems. This overcomes the roadblock that KAEs face when encountered with limited and noisy training datasets, resulting in a lack of generalizability due to inconsistency in training data. Through a blend of deep learning and dynamical systems theory, tcBLRAN demonstrates superior performance in capturing complex behaviors and control systems dynamics, providing a superior data-driven modeling technique for control systems and outperforming the state-of-the-art Koopman bilinear form (KBF) learned by autoencoder networks.
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
Authors: Yutong Liu, Mehrad Ansari, Robert Black, Jason Hattrick-Simpers
Date: 2025-03-25 00:00:00
Tasks: Experimental Design, Interpretable Machine Learning

Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, the lack of established synthesis routes for solid-state materials remains a significant challenge in inorganic chemistry. An interpretable machine learning model is therefore essential, as it provides insights into the key factors governing phase formation. Here, we focus on the formation of single-phase Fe$_2$(ZnCo)O$_4$, synthesized via a high-throughput co-precipitation method. We combined a kernel classification model with a novel application of global SHAP analysis to pinpoint the experimental features most critical to single phase synthesizability by interpreting the contributions of each feature. Global SHAP analysis reveals that precursor and precipitating agent contributions to single-phase spinel formation align closely with established crystal growth theories. These results not only underscore the importance of interpretable machine learning in refining synthesis protocols but also establish a framework for data-informed experimental design in inorganic synthesis.
Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine
Authors: Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan
Date: 2025-03-25 00:00:00

A long-standing challenge in tomography is the 'missing wedge' problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains, thereby achieving a physically coherent and visually enhanced result. We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees - a scenario where conventional methods fail. Notably, it also improves the reconstruction in case of sparse projections, despite the network not being specifically trained for that. This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview
Authors: Dilshod Nematov, Mirabbos Hojamberdiev
Date: 2025-03-22 00:00:00
Tasks: AutoML, Bayesian Optimization, Feature Engineering, Model Selection, Property Prediction

The rapid advancement of machine learning and artificial intelligence (AI)-driven techniques is revolutionizing materials discovery, property prediction, and material design by minimizing human intervention and accelerating scientific progress. This review provides a comprehensive overview of smart, machine learning (ML)-driven approaches, emphasizing their role in predicting material properties, discovering novel compounds, and optimizing material structures. Key methodologies ranging from deep learning, graph neural networks, and Bayesian optimization to automated generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs) enable the autonomous design of materials with tailored functionalities. By leveraging AutoML frameworks (e.g., AutoGluon, TPOT, and H2O.ai), researchers can automate the model selection, hyperparameter tuning, and feature engineering, significantly improving the efficiency of materials informatics. Furthermore, the integration of AI-driven robotic laboratories and high-throughput computing has established a fully automated pipeline for rapid synthesis and experimental validation, drastically reducing the time and cost of material discovery. This review highlights real-world applications of automated ML-driven approaches in predicting mechanical, thermal, electrical, and optical properties of materials, demonstrating successful cases in superconductors, catalysts, photovoltaics, and energy storage systems. We also address key challenges, such as data quality, interpretability, and the integration of AutoML with quantum computing, which are essential for future advancements. Ultimately, the synergy between AI, automated experimentation, and computational modeling transforms the way the materials are discovered, optimized, and designed, paving the way for next-generation innovations in energy, electronics, and nanotechnology.