pwc-archive/papers-with-abstracts huggingface dataset:
On the Steady-State Distributionally Robust Kalman Filter
Authors: MinHyuk Jang, Astghik Hakobyan, Insoon Yang
Date: 2025-03-31 00:00:00
Tasks: Computational Efficiency, State Estimation

State estimation in the presence of uncertain or data-driven noise distributions remains a critical challenge in control and robotics. Although the Kalman filter is the most popular choice, its performance degrades significantly when distributional mismatches occur, potentially leading to instability or divergence. To address this limitation, we introduce a novel steady-state distributionally robust (DR) Kalman filter that leverages Wasserstein ambiguity sets to explicitly account for uncertainties in both process and measurement noise distributions. Our filter achieves computational efficiency by requiring merely the offline solution of a single convex semidefinite program, which yields a constant DR Kalman gain for robust state estimation under distributional mismatches. Additionally, we derive explicit theoretical conditions on the ambiguity set radius that ensure the asymptotic convergence of the time-varying DR Kalman filter to the proposed steady-state solution. Numerical simulations demonstrate that our approach outperforms existing baseline filters in terms of robustness and accuracy across both Gaussian and non-Gaussian uncertainty scenarios, highlighting its significant potential for real-world control and estimation applications.
Energy-efficient UAV movement and user-UAV association in multi-UAV networks
Authors: Subhadip Ghosh, Priyadarshi Mukherjee, Sasthi C. Ghosh
Date: 2025-03-28 00:00:00

These days, unmanned aerial vehicle (UAV)-based millimeter wave (mmWave) communication systems have drawn a lot of attention due to the increasing demand for faster data rates. Given the susceptibility of mmWave signals to obstacles and high propagation loss of mmWaves, ensuring line-of-sight (LoS) connectivity is critical for maintaining robust and efficient communication. Furthermore, UAVs have limited power resource and limited capacity in terms of number of users it can serve. Most significantly different users have different delay requirements and they keep moving while interacting with the UAVs. In this paper, first, we have provided an efficient solution for the optimal movement of the UAVs, by taking into account the energy efficiency of the UAVs as well as the mobility and delay priority of the users. Next, we have proposed a greedy solution for the optimal user-UAV assignment. After that, the numerical results show how well the suggested solution performs in comparison to the current benchmarks in terms of delay suffered by the users, number of unserved users, and energy efficiency of the UAVs.
Stability and Controllability of Revenue Systems via the Bode Approach
Authors: Yichuan Niu, Jianhui Chen
Date: 2025-03-31 00:00:00

In online revenue systems, e.g. an advertising system, budget pacing plays a critical role in ensuring that the spend aligns with desired financial objectives. Pacing systems dynamically control the velocity of spending to balance auction intensity, traffic fluctuations, and other stochastic variables. Current industry practices rely heavily on trial-and-error approaches, often leading to inefficiencies and instability. This paper introduces a principled methodology rooted in Classical Control Theory to address these challenges. By modeling the pacing system as a linear time-invariant (LTI) proxy and leveraging compensator design techniques using Bode methodology, we derive a robust controller to minimize pacing errors and enhance stability. The proposed methodology is validated through simulation and tested by our in-house auction system, demonstrating superior performance in achieving precise budget allocation while maintaining resilience to traffic and auction dynamics. Our findings bridge the gap between traditional control theory and modern advertising systems in modeling, simulation, and validation, offering a scalable and systematic approach to budget pacing optimization.
Intent-Aware MPC for Aircraft Detect-and-Avoid with Response Delay: A Comparative Study with ACAS Xu
Authors: Arash Bahari Kordabad, Arabinda Ghosh, Sybert Stroeve, Sadegh Soudjani
Date: 2025-03-30 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

In this paper, we propose an intent-aware Model Predictive Control (MPC) approach for the remain-well-clear (RWC) functionality of a multi-agent aircraft detect-and-avoid (DAA) system and compare its performance with the standardized Airborne Collision Avoidance System Xu (ACAS Xu). The aircraft system is modeled as a linear system for horizontal maneuvering, with advisories on the rate of turn as the control input. Both deterministic and stochastic time delays are considered to account for the lag between control guidance issuance and the response of the aircraft. The capability of the MPC scheme in producing an optimal control profile over the entire horizon is used to mitigate the impact of the delay. We compare the proposed MPC method with ACAS Xu using various evaluation metrics, including loss of DAA well-clear percentage, near mid-air collision percentage, horizontal miss distance, and additional flight distance across different encounter scenarios. It is shown that the MPC scheme achieves better evaluation metrics than ACAS Xu for both deterministic and stochastic scenarios.
Loss-aware Pricing Strategies for Peer-to-Peer Energy Trading
Authors: Varsha N. Behrunani, Philipp Heer, Roy S. Smith, John Lygeros
Date: 2025-03-30 00:00:00
Tasks: energy trading

Peer-to-peer(P2P) energy trading may increase efficiency and reduce costs, but introduces significant challenges for network operators such as maintaining grid reliability, accounting for network losses, and redistributing costs equitably. We propose a novel loss-aware pricing strategy for P2P energy markets that addresses these challenges while incentivizing participation in the cooperative energy trading market. The problem is formulated as a hierarchical Stackelberg game, where a grid operator determines network tariffs while prosumers optimize their trades based on these tariffs while guaranteeing that network constraints are satisfied. The algorithm is designed to minimize and recover their cost from the trading parties, while also minimizing the total cost of the hubs. The mechanism dynamically adjusts tariffs based on location and network topology, discouraging loss-intensive trades. Finally, the complete framework includes the computation of fair trading prices, ensuring all market participants benefit equitably. An ADMM-based hyper-gradient descent method is proposed for solving this problem. Extensive numerical simulations using the benchmark IEEE 33-bus system demonstrate significant cost reductions and improved network efficiency through reduction in network losses compared to constant tariff schemes. Results highlight the adaptability and scalability of the proposed mechanism to varying network configurations and size, demand profiles, and seasonal conditions.
Distributed Design of Ultra Large-Scale Control Systems: Progress, Challenges, and Prospects
Authors: Leonardo Pedroso, Pedro Batista, W. P. M. H. Heemels
Date: 2025-03-30 00:00:00

The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as ultra large-scale systems (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions.
Physics-Informed Adaptive Deep Koopman Operator Modeling for Autonomous Vehicle Dynamics
Authors: Jianhua Zhang, Yansong He, Hao Chen
Date: 2025-03-30 00:00:00
Tasks: Autonomous Vehicles

Koopman operator has been recognized as an ongoing data-driven modeling method for vehicle dynamics which lifts the original state space into a high-dimensional linear state space. The deep neural networks (DNNs) are verified to be useful for the approximation of Koopman operator. To further improve the accuracy of Koopman operator approximation, this paper introduces a physical loss function term from the concept of physics-informed neural networks (PINNs), i.e., the acceleration loss between neural network output and sensor measurements, to improve the efficiency of network learning and its interpretability. Moreover, we utilize the sliding window least squares (SWLS) to update the system matrix and input matrix online in the lifted space, therefore enabling the deep Koopman operator to adapt to the rapid dynamics of autonomous vehicles in real events. The data collection and validation are conducted on CarSim/Simlink co-simulation platform. With comparison to other physics-based and data-driven approaches on various scenarios, the results reveal that the acceleration loss-informed network refines the accuracy of Koopman operator approximation and renders it with inherent generalization, and the SWLS enforces the deep Koopman operator's capability to cope with changes in vehicle parameters, road conditions, and rapid maneuvers. This indicates the proposed physics-informed adaptive deep Koopman operator is a performant and efficient data-driven modeling tool.
A Time Splitting Based Optimization Method for Nonlinear MHE
Authors: Shuting Wu, Yifei Wang, Jingzhe Wang, Xu Du
Date: 2025-03-30 00:00:00
Tasks: Computational Efficiency

Moving Horizon Estimation~(MHE) is essentially an optimization-based approach designed to estimate the states of dynamic systems within a moving time horizon. Traditional MHE solutions become computationally prohibitive due to the \textit{curse of dimensionality} arising from increasing problem complexity and growing length of time horizon. To address this issue, we propose novel computationally efficient algorithms for solving nonlinear MHE problems. Specifically, we first introduce a distributed reformulation utilizing a time-splitting technique. Leveraging this reformulation, we develop the Efficient Gauss-Newton Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) to achieve computational efficiency. Additionally, to accommodate limited computational capabilities inherent in some sub-problem solvers, we propose the Efficient Sensitivity Assisted ALADIN, which enables sub-problems to be solved inexactly without hindering computational efficiency. Furthermore, recognizing scenarios where sub-problem solvers possess no computational power, we propose a Distributed Sequential Quadratic Programming (SQP) that relies solely on first- and second-order information of local objective functions. We demonstrate the performance and advantages of our proposed methods through numerical experiments on differential drive robots case, a practical nonlinear MHE problem. Our results demonstrate that the three proposed algorithms achieve computational efficiency while preserving high accuracy, thereby satisfying the real-time requirements of MHE.
Ensuring Safe and Smooth Control in Safety-Critical Systems via Filtered Control Barrier Functions
Authors: Shuo Liu, Wei Xiao, Calin A. Belta
Date: 2025-03-30 00:00:00
Tasks: continuous-control, Continuous Control

In safety-critical control systems, ensuring both system safety and smooth control input is essential for theoretical guarantees and practical deployment. Existing Control Barrier Function (CBF) frameworks, especially High-Order CBFs (HOCBFs), effectively enforce safety constraints but often lead to nonsmooth or discontinuous control inputs that can degrade system performance or violate actuator limitations. This paper introduces Filtered Control Barrier Functions (FCBFs), which extend HOCBFs by incorporating an auxiliary dynamic system - referred to as input regularization filter - to produce Lipschitz continuous control inputs. The proposed framework ensures safety, control bounds, and smoothness simultaneously by integrating FCBFs and HOCBFs within a unified quadratic program (QP). Theoretical guarantees are provided and simulations on a unicycle model demonstrate the effectiveness of the proposed method compared to standard and smoothness-penalized HOCBF approaches.
Iterative VCG-based Mechanism Fosters Cooperation in Multi-Regional Network Design
Authors: Mingjia He, Yannik Werner, Andrea Censi, Emilio Frazzoli, Gioele Zardini
Date: 2025-03-29 00:00:00
Tasks: Decision Making

Transportation network design often involves multiple stakeholders with diverse priorities. We consider a system with a hierarchical multi-agent structure, featuring self-optimized subnetwork operators at the lower level and a central organization at the upper level. Independent regional planning can lead to inefficiencies due to the lack of coordination, hindering interregional travel and cross-border infrastructure development, while centralized methods may struggle to align local interests and can be impractical to implement. To support decision making for such a system, we introduce an iterative VCG-based mechanism for multi-regional network design that fosters cooperation among subnetwork operators. By leveraging the Vickery-Clarke-Groves (VCG) mechanism, the framework determines collective investment decisions and the necessary payments from both operators and the central organization to achieve efficient outcomes. A case study on the European Railway System validates the effectiveness of the proposed method, demonstrating significant improvements in overall network performance through enhanced cross-region cooperation.
Attitude Synchronization for Multi-Agent Systems on SO(3) Using Vector Measurements
Authors: Mouaad Boughellaba, Soulaimane Berkane, Abdelhamid Tayebi
Date: 2025-03-28 00:00:00

In this paper, we address the problem of leaderless attitude synchronization for a group of rigid body systems evolving on SO(3), relying on local measurements of some inertial (unit-length) vectors. The interaction graph among agents is assumed to be undirected, acyclic, and connected. We first present a distributed attitude synchronization scheme designed at the kinematic level of SO(3), followed by an extended scheme designed at the dynamic level. Both schemes are supported by a rigorous stability analysis, which establishes their almost global asymptotic stability properties. Finally, numerical simulations demonstrate the effectiveness of both distributed attitude synchronization schemes.
Markov Potential Game Construction and Multi-Agent Reinforcement Learning with Applications to Autonomous Driving
Authors: Huiwen Yan, Mushuang Liu
Date: 2025-03-28 00:00:00
Tasks: Autonomous Driving, Multi-agent Reinforcement Learning

Markov games (MGs) serve as the mathematical foundation for multi-agent reinforcement learning (MARL), enabling self-interested agents to learn their optimal policies while interacting with others in a shared environment. However, due to the complexities of an MG problem, seeking (Markov perfect) Nash equilibrium (NE) is often very challenging for a general-sum MG. Markov potential games (MPGs), which are a special class of MGs, have appealing properties such as guaranteed existence of pure NEs and guaranteed convergence of gradient play algorithms, thereby leading to desirable properties for many MARL algorithms in their NE-seeking processes. However, the question of how to construct MPGs has been open. This paper provides sufficient conditions on the reward design and on the Markov decision process (MDP), under which an MG is an MPG. Numerical results on autonomous driving applications are reported.
Sensorless Field Oriented Control of CSI-Fed PMSM Drives Used in Submersible Pumps
Authors: Milad Bahrami-Fard, Majid Ghasemi Korrani, Babak Fahimi
Date: 2025-03-28 00:00:00

This paper proposes a practical startup strategy for current source inverter (CSI)-fed Permanent Magnet Synchronous Motor (PMSM) drives in submersible pump applications, focusing on ensuring a seamless shift to sensorless field-oriented control (FOC). The method effectively manages the transition to sensorless operation without requiring precise current or alignment error calculations, thereby simplifying implementation. By addressing speed and current oscillations directly during the startup and transition stages, the approach significantly enhances overall system stability and responsiveness. Validation through simulation and experimental testing demonstrates the strategy's success in maintaining low oscillation levels across various operating conditions, confirming its reliability for high-performance industrial applications.
Passivity, No-Regret, and Convergent Learning in Contractive Games
Authors: Hassan Abdelraouf, Georgios Piliouras, Jeff S. Shamma
Date: 2025-03-28 00:00:00

We investigate the interplay between passivity, no-regret, and convergence in contractive games for various learning dynamic models and their higher-order variants. Our setting is continuous time. Building on prior work for replicator dynamics, we show that if learning dynamics satisfy a passivity condition between the payoff vector and the difference between its evolving strategy and any fixed strategy, then it achieves finite regret. We then establish that the passivity condition holds for various learning dynamics and their higher-order variants. Consequentially, the higher-order variants can achieve convergence to Nash equilibrium in cases where their standard order counterparts cannot, while maintaining a finite regret property. We provide numerical examples to illustrate the lack of finite regret of different evolutionary dynamic models that violate the passivity property. We also examine the fragility of the finite regret property in the case of perturbed learning dynamics. Continuing with passivity, we establish another connection between finite regret and passivity, but with the related equilibrium-independent passivity property. Finally, we present a passivity-based classification of dynamic models according to the various passivity notions they satisfy, namely, incremental passivity, $\delta$-passivity, and equilibrium-independent passivity. This passivity-based classification provides a framework to analyze the convergence of learning dynamic models in contractive games.
A Cooperative Compliance Control Framework for Socially Optimal Mixed Traffic Routing
Authors: Anni Li, Ting Bai, Yingqing Chen, Christos G. Cassandras, Andreas A. Malikopoulos
Date: 2025-03-28 00:00:00

In mixed traffic environments, where Connected and Autonomed Vehicles (CAVs) coexist with potentially non-cooperative Human-Driven Vehicles (HDVs), the self-centered behavior of human drivers may compromise the efficiency, optimality, and safety of the overall traffic network. In this paper, we propose a Cooperative Compliance Control (CCC) framework for mixed traffic routing, where a Social Planner (SP) optimizes vehicle routes for system-wide optimality while a compliance controller incentivizes human drivers to align their behavior with route guidance from the SP through a "refundable toll" scheme. A key challenge arises from the heterogeneous and unknown response models of different human driver types to these tolls, making it difficult to design a proper controller and achieve desired compliance probabilities over the traffic network. To address this challenge, we employ Control Lyapunov Functions (CLFs) to adaptively correct (learn) crucial components of our compliance probability model online, construct data-driven feedback controllers, and demonstrate that we can achieve the desired compliance probability for HDVs, thereby contributing to the social optimality of the traffic network.
Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures
Authors: Samuel I. Akinwande, Chelsea Sidrane, Mykel J. Kochenderfer, Clark Barrett
Date: 2025-03-28 00:00:00

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art.
Neural Identification of Feedback-Stabilized Nonlinear Systems
Authors: Mahrokh G. Boroujeni, Laura Meroi, Leonardo Massai, Clara L. Galimberti, Giancarlo Ferrari-Trecate
Date: 2025-03-28 00:00:00

Neural networks have demonstrated remarkable success in modeling nonlinear dynamical systems. However, identifying these systems from closed-loop experimental data remains a challenge due to the correlations induced by the feedback loop. Traditional nonlinear closed-loop system identification methods struggle with reliance on precise noise models, robustness to data variations, or computational feasibility. Additionally, it is essential to ensure that the identified model is stabilized by the same controller used during data collection, ensuring alignment with the true system's closed-loop behavior. The dual Youla parameterization provides a promising solution for linear systems, offering statistical guarantees and closed-loop stability. However, extending this approach to nonlinear systems presents additional complexities. In this work, we propose a computationally tractable framework for identifying complex, potentially unstable systems while ensuring closed-loop stability using a complete parameterization of systems stabilized by a given controller. We establish asymptotic consistency in the linear case and validate our method through numerical comparisons, demonstrating superior accuracy over direct identification baselines and compatibility with the true system in stability properties.