pwc-archive/papers-with-abstracts huggingface dataset:
Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
Authors: Zhiyu He, Saverio Bolognani, Florian Dörfler, Michael Muehlebach
Date: 2025-03-10 00:00:00
Tasks: Recommendation Systems, Stochastic Optimization

Distribution shifts have long been regarded as troublesome external forces that a decision-maker should either counteract or conform to. An intriguing feedback phenomenon termed decision dependence arises when the deployed decision affects the environment and alters the data-generating distribution. In the realm of performative prediction, this is encoded by distribution maps parameterized by decisions due to strategic behaviors. In contrast, we formalize an endogenous distribution shift as a feedback process featuring nonlinear dynamics that couple the evolving distribution with the decision. Stochastic optimization in this dynamic regime provides a fertile ground to examine the various roles played by dynamics in the composite problem structure. To this end, we develop an online algorithm that achieves optimal decision-making by both adapting to and shaping the dynamic distribution. Throughout the paper, we adopt a distributional perspective and demonstrate how this view facilitates characterizations of distribution dynamics and the optimality and generalization performance of the proposed algorithm. We showcase the theoretical results in an opinion dynamics context, where an opportunistic party maximizes the affinity of a dynamic polarized population, and in a recommender system scenario, featuring performance optimization with discrete distributions in the probability simplex.
Communication-aware Multi-agent Systems Control Based on $k$-hop Distributed Observers
Authors: Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas
Date: 2025-03-10 00:00:00

We propose a distributed control strategy to allow the control of a multi-agent system requiring k-hop interactions based on the design of distributed state and input observers. In particular, we design for each agent a finite time convergent state and input observer that exploits only the communication with the 1-hop neighbors to reconstruct the information regarding those agents at a 2-hop distance or more. We then demonstrate that if the k-hop based control strategy is set-Input to State Stable with respect to the set describing the goal, then the observer information can be adopted to achieve the team objective with stability guarantees.
Hierarchical Neuro-Symbolic Decision Transformer
Authors: Ali Baheri, Cecilia O. Alm
Date: 2025-03-10 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

We present a hierarchical neuro-symbolic control framework that tightly couples a classical symbolic planner with a transformer-based policy to address long-horizon decision-making under uncertainty. At the high level, the planner assembles an interpretable sequence of operators that guarantees logical coherence with task constraints, while at the low level each operator is rendered as a sub-goal token that conditions a decision transformer to generate fine-grained actions directly from raw observations. This bidirectional interface preserves the combinatorial efficiency and explainability of symbolic reasoning without sacrificing the adaptability of deep sequence models, and it permits a principled analysis that tracks how approximation errors from both planning and execution accumulate across the hierarchy. Empirical studies in stochastic grid-world domains demonstrate that the proposed method consistently surpasses purely symbolic, purely neural and existing hierarchical baselines in both success and efficiency, highlighting its robustness for sequential tasks.
Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks
Authors: Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi, Yao Zhu, Yulin Hu, Marina Petrova, Anke Schmeink
Date: 2025-03-10 00:00:00

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.
Coordinated Energy-Trajectory Economic Model Predictive Control for Autonomous Surface Vehicles under Disturbances
Authors: Zhongqi Deng, YuAn Wang, Jian Huang, HUI ZHANG, Yaonan Wang
Date: 2025-03-10 00:00:00
Tasks: CPU, Model Predictive Control

The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances. By formulating lateral deviations as energy-equivalent penalties in the cost function, our method enables explicit trade-offs between tracking precision and energy consumption. Furthermore, a motion-dependent decomposition technique is proposed to estimate terminal energy costs based on vehicle dynamics. Compared with the existing EMPC method, simulations with real-world ocean disturbance data demonstrate the controller's energy consumption with a 0.06 energy increase while reducing cross-track errors by up to 18.61. Field experiments conducted on an ASV equipped with an Intel N100 CPU in natural lake environments validate practical feasibility, achieving 0.22 m average cross-track error at nearly 1 m/s and 10 Hz control frequency. The proposed scheme provides a computationally tractable solution for ASVs operating under resource constraints.
Explicit Solution of Tunable Input-to-State Safe-Based Controller Under High-Relative-Degree Constraints
Authors: Yan Wei, Yu Feng, Linlin Ou, Yueying Wang, Xinyi Yu
Date: 2025-03-10 00:00:00

This paper investigates the safety analysis and verification of nonlinear systems subject to high-relative-degree constraints and unknown disturbance. The closed-form solution of the high-order control barrier functions (HOCBF) optimization problem with and without a nominal controller is first provided, making it unnecessary to solve the quadratic program problem online and facilitating the analysis. Further, we introduce the concept of tunable input-to-state safety(ISSf), and a new tunable function in conjunction with HOCBF is provided. When combined with the existing ISSf theorem, produces controllers for constrained nonlinear systems with external disturbances. The theoretical results are proven and supported by numerical simulations.
EnCortex: A General, Extensible and Scalable Framework for Decision Management in New-age Energy Systems
Authors: Millend Roy, Vaibhav Balloli, Anupam Sobti, Srinivasan Iyengar, Shivkumar Kalyanaraman, Tanuja Ganu, Akshay Nambi
Date: 2025-03-10 00:00:00
Tasks: Management

With increased global warming, there has been a significant emphasis to replace fossil fuel-dependent energy sources with clean, renewable sources. These new-age energy systems are becoming more complex with an increasing proportion of renewable energy sources (like solar and wind), energy storage systems (like batteries), and demand side control in the mix. Most new-age sources being highly dependent on weather and climate conditions bring about high variability and uncertainty. Energy operators rely on such uncertain data to make different planning and operations decisions periodically, and sometimes in real-time, to maintain the grid stability and optimize their objectives (cost savings, carbon footprint, etc.). Hitherto, operators mostly rely on domain knowledge, heuristics, or solve point problems to take decisions. These approaches fall short because of their specific assumptions and limitations. Further, there is a lack of a unified framework for both research and production environments at scale. In this paper, we propose EnCortex to address these challenges. EnCortex provides a general, easy-to-use, extensible, and scalable energy decision framework that enables operators to plan, build and execute their real-world scenarios efficiently. We show that using EnCortex, we can define and compose complex new-age scenarios, owing to industry-standard abstractions of energy entities and the modularity of the framework. EnCortex provides a foundational structure to support several state-of-the-art optimizers with minimal effort. EnCortex supports both quick developments for research prototypes and scaling the solutions to production environments. We demonstrate the utility of EnCortex with three complex new-age real-world scenarios and show that significant cost and carbon footprint savings can be achieved.
Existence and Design of Target Output Controllers
Authors: Tyrone Fernando, Mohamed Darouach
Date: 2025-03-10 00:00:00

This paper introduces new conditions for target output controllability and provides existence conditions for placing a specific number of poles with a target output controller. Additionally, an algorithm is presented for the design of a target output controller. Controllability of the system under consideration is not required for designing target output controllers in this context. The findings in this paper extend the principles of full state feedback control. Moreover, we present conditions for static output feedback control under specific constraints. Several numerical examples are provided to illustrate the results.
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers
Authors: Zeinab Ebrahimi, Mohammad Deghat
Date: 2025-03-10 00:00:00
Tasks: Distributed Optimization

Distributed optimization aims to leverage the local computation and communication capabilities of each agent to achieve a desired global objective. This paper addresses the distributed pose graph optimization (PGO) problem under non-convex constraints, with the goal of approximating the rotation and translation of each pose given relevant noisy measurements. To achieve this goal, the splitting method based on the concepts of the alternating direction method of multipliers (ADMM) and Bregman iteration are applied to solve the rotation subproblems. The proposed approach enables the iterative resolution of constrained problems, achieved through solving unconstrained problems and orthogonality-constrained quadratic problems that have analytical solutions. The performance of the proposed algorithm is compared against two practical methods in pose graph optimization: the Distributed Gauss-Seidel (DGS) algorithm and the centralized pose graph optimizer with an optimality certificate (SE-Sync). The efficiency of the proposed method is verified through its application to several simulated and real-world pose graph datasets. Unlike the DGS method, our approach attempts to solve distributed PGO problems without relaxing the non-convex constraints.
Reinforcement Learning Based Symbolic Regression for Load Modeling
Authors: Ding Lin, Han Guo, Jianhui Wang, Meng Yue, Tianqiao Zhao
Date: 2025-03-10 00:00:00
Tasks: Computational Efficiency, regression, reinforcement-learning, Reinforcement Learning, Symbolic Regression

With the increasing penetration of renewable energy sources, growing demand variability, and evolving grid control strategies, accurate and efficient load modeling has become a critical yet challenging task. Traditional methods, such as fixed-form parametric models and data-driven approaches, often struggle to balance accuracy, computational efficiency, and interpretability. This paper introduces a novel symbolic regression algorithm based on the Actor-Critic reinforcement learning framework, specifically tailored for dynamic load modeling. The algorithm employs a trainable expression tree with controlled depth and a predefined set of operators to generate compact and interpretable mathematical expressions. The Actor network probabilistically selects operators for the symbolic expression, while the Critic evaluates the resulting expression tree through a loss function. To further enhance performance, a candidate pool mechanism is implemented to store high-performing expressions, which are subsequently fine-tuned using gradient descent. By focusing on simplicity and precision, the proposed method significantly reduces computational complexity while preserving interpretability. Experimental results validate its superior performance compared to existing benchmarks, which offers a robust and scalable solution for dynamic load modeling and system analysis in modern power systems.
Recursive Estimation for Dynamical Systems with Measurement Bias, Outliers and Constraints
Authors: Krishan Mohan Nagpal
Date: 2025-03-10 00:00:00
Tasks: State Estimation

This paper describes recursive algorithms for state estimation of linear dynamical systems when measurements are noisy with unknown bias and/or outliers. For situations with noisy and biased measurements, algorithms are proposed that minimize $\epsilon$ insensitive loss function. In this approach which is often used in Support Vector Machines, small errors are ignored making the algorithm less sensitive to measurement bias. Apart from $\epsilon$ insensitive quadratic loss function, estimation algorithms are also presented for $\epsilon$ insensitive Huber M loss function which provides good performance in presence of both small noises as well as outliers. The advantage of Huber cost function based estimator in presence of outliers is due to the fact the error penalty function switches from quadratic to linear for errors beyond a certain threshold. For both objective functions, estimation algorithms are extended to cases when there are additional constraints on states and exogenous signals such as known range of some states or exogenous signals or measurement noises. Interestingly, the filtering algorithms are recursive and structurally similar to Kalman filter with the main difference being that the updates based on the new measurement ("innovation term") are based on solution of a quadratic optimization problem with linear constraints.
Design Optimal Backstepping Controller for Quadrotor Based on Lyapunov Theory for Disturbances Environments
Authors: Dong LT Tran, Thanh C Vo, Hoang T Tran, Minh T Nguyen, Hai T. Do
Date: 2025-03-10 00:00:00
Tasks: Position

Various control methods have been studied to control the position and attitude of quadrotors. There are some differences in the mathematical equations between the two types of quadrotor configurations that lead to different control efficiency in disturbance environments. This paper described the nonlinear back stepping approach based on the Lyapunov function theory and LaSalle Principle for the quadrotor control system, which can provide the stability of all system states during the tracking of the desired trajectory. Accordingly, a mathematical model of the cross quadrotor configuration together with the controller has been built to stabilize the altitude and position of the quadrotor. To clarify the effectiveness of this method with the selected quadrotor configuration, we compare it with a traditional PID controller in an environment affected by disturbances. The simulation results in MATLAB show satisfactory stability of the quadrotor flight and following certain trajectories, confirming the accuracy and validity of the control method.
Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point
Authors: Travis Barrett, Amit Kumar Mishra
Date: 2025-03-09 00:00:00
Tasks: regression

In this paper, we describe the design of an inexpensive and agile climate sensor system which can be repurposed easily to measure various pollutants. We also propose the use of machine learning regression methods to calibrate CO2 data from this cost-effective sensing platform to a reference sensor at the South African Weather Service's Cape Point measurement facility. We show the performance of these methods and found that Random Forest Regression was the best in this scenario. This shows that these machine learning methods can be used to improve the performance of cost-effective sensor platforms and possibly extend the time between manual calibration of sensor networks.
Coordinated Ramp Metering Control based on Scalable Nonlinear Traffic Dynamics Model Discovery in a Large Network
Authors: Zihang Wei, Yang Zhou, Yunlong Zhang, Mihir Kulkarni
Date: 2025-03-09 00:00:00
Tasks: Model Discovery, Model Predictive Control

This study proposes a coordinated ramp metering control framework in large networks based on scalable nonlinear traffic dynamics model discovery. Existing coordinated ramp metering control methods often require accurate traffic dynamics models in real time, however, for large-scale highway networks, since these models are always nonlinear, they are extremely challenging to obtain. To overcome this limitation, this study utilizes the Sparse Identification of Nonlinear Dynamics with Control (SINDYc) to derive the accurate nonlinear traffic dynamics model from observed data. The discovered dynamics model is then integrated into a Model Predictive Control (MPC) coordinated ramp metering controller, enabling optimized control actions that enhance traffic flow and efficiency. The proposed framework is tested on a large-scale highway network that includes three intersecting highways and eight on-ramps, which outperforms the existing approaches, demonstrating its effectiveness and potential for real-time application. This framework can offer a scalable and robust solution for improving real-time traffic management in complex urban environments.
Transfer Learning for LQR Control
Authors: Taosha Guo, Fabio Pasqualetti
Date: 2025-03-09 00:00:00
Tasks: Transfer Learning

In this paper, we study a transfer learning framework for Linear Quadratic Regulator (LQR) control, where (i) the dynamics of the system of interest (target system) are unknown and only a short trajectory of impulse responses from the target system is provided, and (ii) impulse responses are available from $N$ source systems with different dynamics. We show that the LQR controller can be learned from a sufficiently long trajectory of impulse responses. Further, a transferable mode set can be identified using the available data from source systems and the target system, enabling the reconstruction of the target system's impulse responses for controller design. By leveraging data from source systems, we show that the sample complexity for synthesizing the LQR controller can be reduced by $50 \%$. Algorithms and numerical examples are provided to demonstrate the implementation of the proposed transfer control framework.
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game
Authors: Penglin Hu, Chunhui Zhao, Quan Pan
Date: 2025-03-09 00:00:00
Tasks: Multi-Objective Reinforcement Learning, Q-Learning, Reinforcement Learning (RL)

In practical application, the pursuit-evasion game (PEG) often involves multiple complex and conflicting objectives. The single-objective reinforcement learning (RL) usually focuses on a single optimization objective, and it is difficult to find the optimal balance among multiple objectives. This paper proposes a three-objective RL algorithm based on fuzzy Q-learning (FQL) to solve the PEG with different optimization objectives. First, the multi-objective FQL algorithm is introduced, which uses the reward function to represent three optimization objectives: evading pursuit, reaching target, and avoiding obstacle. Second, a multi-objective evaluation method and action selection strategy based on three-dimensional hypervolume are designed, which solved the dilemma of exploration-exploitation. By sampling the Pareto front, the update rule of the global strategy is obtained. The proposed algorithm reduces computational load while ensuring exploration ability. Finally, the performance of the algorithm is verified by simulation results.
Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach
Authors: Omar Mameche, Abdelhadi Abedou, Taqwa Mezaache, Mohamed Tadjine
Date: 2025-03-09 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning

This paper explores the application of reinforcement learning to optimize the parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to operate as an artificial pancreas for Type 1 diabetes. The primary challenge in diabetes management is the dynamic nature of blood glucose levels, which are influenced by several factors such as meal intake and timing. Traditional controllers often struggle to adapt to these changes, leading to suboptimal insulin administration. To address this issue, we employ a reinforcement learning agent tasked with adjusting 27 parameters of the Takagi-Sugeno fuzzy controller at each time step, ensuring real-time adaptability. The study's findings demonstrate that this approach significantly enhances the robustness of the controller against variations in meal size and timing, while also stabilizing glucose levels with minimal exogenous insulin. This adaptive method holds promise for improving the quality of life and health outcomes for individuals with Type 1 diabetes by providing a more responsive and precise management tool. Simulation results are given to highlight the effectiveness of the proposed approach.
Diffusion Model Based Probabilistic Day-ahead Load Forecasting
Authors: Ding Lin, Han Guo, Jianhui Wang
Date: 2025-03-09 00:00:00
Tasks: Denoising, Density Estimation, Load Forecasting

Accurate probabilistic load forecasting is crucial for maintaining the safety and stability of power systems. However, the mainstream approach, multi-step prediction, must be improved by cumulative errors and latency issues, which limits its effectiveness in probabilistic day-ahead load forecasting (PDALF). To overcome these challenges, we introduce DALNet, a novel denoising diffusion model designed to generate load curves rather than relying on direct prediction. By shifting the focus to curve generation, DALNet captures the complex distribution of actual load time-series data under specific conditions with greater fidelity. To further enhance DALNet, we propose the temporal multi-scale attention block (TMSAB), a mechanism designed to integrate both positional and temporal information for improved forecasting precision. Furthermore, we utilize kernel density estimation (KDE) to reconstruct the distribution of generated load curves and employ KL divergence to compare them with the actual data distribution. Experimental results demonstrate that DALNet excels in load forecasting accuracy and offers a novel perspective for other predictive tasks within power systems.