pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Pricing for Optimal Coordination in Networked Energy Systems with Nonsmooth Cost Functions
Authors: Jiayi Li, Jiale Wei, Matthew Motoki, Yan Jiang, Baosen Zhang
Date: 2025-04-01 00:00:00

Incentive-based coordination mechanisms for distributed energy consumption have shown promise in aligning individual user objectives with social welfare, especially under privacy constraints. Our prior work proposed a two-timescale adaptive pricing framework, where users respond to prices by minimizing their local cost, and the system operator iteratively updates the prices based on aggregate user responses. A key assumption was that the system cost need to smoothly depend on the aggregate of the user demands. In this paper, we relax this assumption by considering the more realistic model of where the cost are determined by solving a DCOPF problem with constraints. We present a generalization of the pricing update rule that leverages the generalized gradients of the system cost function, which may be nonsmooth due to the structure of DCOPF. We prove that the resulting dynamic system converges to a unique equilibrium, which solves the social welfare optimization problem. Our theoretical results provide guarantees on convergence and stability using tools from nonsmooth analysis and Lyapunov theory. Numerical simulations on networked energy systems illustrate the effectiveness and robustness of the proposed scheme.
Probabilistically safe and efficient model-based Reinforcement Learning
Authors: Filippo Airaldi, Bart De Schutter, Azita Dabiri
Date: 2025-04-01 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a samplebased, model-based approach. At the core of the method lies a Model Predictive Control (MPC) scheme that acts as function approximation, providing a model-based predictive control policy. To ensure safety, a probabilistic Control Barrier Function (CBF) is integrated into the MPC controller. A sample-based approach with guarantees is employed to approximate the effects of stochasticies in the optimal control formulation and to guarantee the probabilistic CBF condition. A learnable terminal cost formulation is included in the MPC objective to counterbalance the additional computational burden due to sampling. An RL algorithm is deployed to learn both the terminal cost and the CBF constraint. Results from our numerical experiment on a constrained LTI problem corroborate the effectiveness of the proposed methodology in reducing computation time while preserving control performance and safety.
Symmetry-based observers for ODE systems
Authors: Stefano Battilotti
Date: 2025-04-01 00:00:00

In this paper we introduce an observer design framework for ordinary differential equation (ODE) systems based on various types of existing or even novel one-parameter symmetries (exact, asymptotic and variational) ending up with a certain number of semi-global and global observers, with bounded or unbounded system's solutions and with infinite- or finite-time convergence. We compare some of these symmetry-based observers with existing observers, recovering for instance the same performances of high-gain semiglobal observers and the finite-time convergence capabilities of sliding mode observers, while obtaining novel global observers where existing techniques are not able to provide any.
Perturbation-Based Pinning Control Strategy for Enhanced Synchronization in Complex Networks
Authors: Ziang Mao, Tianlong Fan, Linyuan Lü
Date: 2025-04-01 00:00:00

Synchronization is essential for the stability and coordinated operation of complex networked systems. Pinning control, which selectively controls a subset of nodes, provides a scalable solution to enhance network synchronizability. However, existing strategies face key limitations: heuristic centrality-based methods lack a direct connection to synchronization dynamics, while spectral approaches, though effective, are computationally intensive. To address these challenges, we propose a perturbation-based optimized strategy (PBO) that dynamically evaluates each node's spectral impact on the Laplacian matrix, achieving improved synchronizability with significantly reduced computational costs (with complexity O(kM)). Extensive experiments demonstrate that the proposed method outperforms traditional strategies in synchronizability, convergence rate, and pinning robustness to node failures. Notably, in all the empirical networks tested and some generated networks, PBO significantly outperforms the brute-force greedy strategy, demonstrating its ability to avoid local optima and adapt to complex connectivity patterns. Our study establishes the theoretical relationship between network synchronizability and convergence rate, offering new insights into efficient synchronization strategies for large-scale complex networks.
Control Barrier Function Synthesis for Nonlinear Systems with Dual Relative Degree
Authors: Gilbert Bahati, Ryan K. Cosner, Max H. Cohen, Ryan M. Bena, Aaron D. Ames
Date: 2025-04-01 00:00:00

Control barrier functions (CBFs) are a powerful tool for synthesizing safe control actions; however, constructing CBFs remains difficult for general nonlinear systems. In this work, we provide a constructive framework for synthesizing CBFs for systems with dual relative degree -- where different inputs influence the outputs at two different orders of differentiation; this is common in systems with orientation-based actuation, such as unicycles and quadrotors. In particular, we propose dual relative degree CBFs (DRD-CBFs) and show that these DRD-CBFs can be constructively synthesized and used to guarantee system safety. Our method constructs DRD-CBFs by leveraging the dual relative degree property -- combining a CBF for an integrator chain with a Lyapunov function certifying the tracking of safe inputs generated for this linear system. We apply these results to dual relative degree systems, both in simulation and experimentally on hardware using quadruped and quadrotor robotic platforms.
A Hybrid Systems Model of Feedback Optimization for Linear Systems
Authors: Oscar Jed Chuy, Matthew Hale, Ricardo Sanfelice
Date: 2025-04-01 00:00:00

Feedback optimization algorithms compute inputs to a system in real time, which helps mitigate the effects of unknown disturbances. However, existing work models both system dynamics and computations in either discrete or continuous time, which does not faithfully model some applications. In this work, we model linear system dynamics in continuous time, and we model the computations of inputs in discrete time. Therefore, we present a novel hybrid systems framework for modeling feedback optimization of linear time-invariant systems that are subject to unknown, constant disturbances. For this setup, we first establish the well-posedness of the hybrid model and establish completeness of solutions while ruling out Zeno behavior. Then, our main result derives a convergence rate and an error bound for the full hybrid computation-in-theloop system and shows that it converges exponentially towards a ball of known radius about a desired fixed point. Simulation results show that this approach successfully mitigates the effects of disturbances, with the magnitude of steady-state error being 81% less than the magnitude of the disturbances in the system.
Inverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery
Authors: Abhigyan Majumdar, Navid Mojahed, Shima Nazari
Date: 2025-04-01 00:00:00
Tasks: GPR, Operator learning, Uncertainty Quantification

The Koopman Operator Theory opens the door for application of rich linear systems theory for computationally efficient modeling and optimal control of nonlinear systems by providing a globally linear representation for complex nonlinear systems. However, methodologies for Koopman Operator discovery struggle with the dependency on the set of selected observable functions and meaningful uncertainty quantification. The primary objective of this work is to leverage Gaussian process regression (GPR) to develop a probabilistic Koopman linear model while removing the need for heuristic observable specification. In this work, we present inverted Gaussian process optimization based Koopman Operator learning (iGPK), an automatic differentiation-based approach to simultaneously learn the observable-operator combination. We show that the proposed iGPK method is robust to observation noise in the training data, while also providing good uncertainty quantification, such that the predicted distribution consistently encapsulates the ground truth, even for noisy training data.
Directional excitability in Hilbert spaces
Authors: Gustave Bainier, Alessio Franci
Date: 2025-03-31 00:00:00

We introduce a generalized excitable system in which spikes can happen in a continuum of directions, therefore drastically enriching the expressivity and control capability of the spiking dynamics. In this generalized excitable system, spiking trajectories happen in a Hilbert space with an excitable resting state at the origin and spike responses that can be triggered in any direction as a function of the system's state and inputs. State-dependence of the spiking direction provide the system with a vanishing spiking memory trace, which enables robust tracking and integration of inputs in the spiking direction history. The model exhibits generalized forms of both Hodgkin's Type I and Type II excitability, capturing their usual bifurcation behaviors in an abstract setting. When used as the controller of a two-dimensional navigation task, this model facilitates both the sparseness of the actuation and its sensitivity to environmental inputs. These results highlight the potential of the proposed generalized excitable model for excitable control in high- and infinite-dimensional spaces.
On-the-fly Surrogation for Complex Nonlinear Dynamics
Authors: E. Javier Olucha, Rajiv Singh, Amritam Das, Roland Tóth
Date: 2025-03-31 00:00:00

High-fidelity models are essential for accurately capturing nonlinear system dynamics. However, simulation of these models is often computationally too expensive and, due to their complexity, they are not directly suitable for analysis, control design or real-time applications. Surrogate modelling techniques seek to construct simplified representations of these systems with minimal complexity, but adequate information on the dynamics given a simulation, analysis or synthesis objective at hand. Despite the widespread availability of system linearizations and the growing computational potential of autograd methods, there is no established approach that systematically exploits them to capture the underlying global nonlinear dynamics. This work proposes a novel surrogate modelling approach that can efficiently build a global representation of the dynamics on-the-fly from local system linearizations without ever explicitly computing a model. Using radial basis function interpolation and the second fundamental theorem of calculus, the surrogate model is only computed at its evaluation, enabling rapid computation for simulation and analysis and seamless incorporation of new linearization data. The efficiency and modelling capabilities of the method are demonstrated on simulation examples.
Learning from Disengagements: An Analysis of Safety Driver Interventions during Remote Driving
Authors: Ole Hans, Jürgen Adamy
Date: 2025-03-31 00:00:00

This study investigates disengagements of Remote Driving Systems (RDS) based on interventions by an in-vehicle Safety Drivers (SD) in real-world Operational Design Domains (ODD) with a focus on Remote Driver (RD) performance during their driving training. Based on an analysis of over 14,000 km on remote driving data, the relationship between the driving experience of 25 RD and the frequency of disengagements is systematically investigated. The results show that the number of SD interventions decreases significantly within the first 400 km of driving experience, which illustrates a clear learning curve of the RD. In addition, the most common causes for 183 disengagements analyzed are identified and categorized, whereby four main scenarios for SD interventions were identified and illustrated. The results emphasize the need for experience-based and targeted training programs aimed at developing basic driving skills early on, thereby increasing the safety, controllability and efficiency of RDS, especially in complex urban environment ODDs.
Distributed Model Predictive Control for Dynamic Cooperation of Multi-Agent Systems
Authors: Matthias Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2025-03-31 00:00:00
Tasks: Model Predictive Control

We propose a distributed model predictive control (MPC) framework for coordinating heterogeneous, nonlinear multi-agent systems under individual and coupling constraints. The cooperative task is encoded as a shared objective function minimized collectively by the agents. Each agent optimizes an artificial reference as an intermediate step towards the cooperative objective, along with a control input to track it. We establish recursive feasibility, asymptotic stability, and transient performance bounds under suitable assumptions. The solution to the cooperative task is not predetermined but emerges from the optimized interactions of the agents. We demonstrate the framework on numerical examples inspired by satellite constellation control, collision-free narrow-passage traversal, and coordinated quadrotor flight.
Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints
Authors: Lukas Schwenkel, Johannes Köhler, Matthias A. Müller, Frank Allgöwer
Date: 2025-03-31 00:00:00
Tasks: Model Predictive Control

In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadratic constraints (IQC). By leveraging IQCs, a large class of nonlinear and dynamic uncertainties can be addressed. We leverage recent IQC synthesis tools to design a dynamic controller and an observer that are robust to these uncertainties and minimize the size of the resulting constraint tightening in the MPC. Thereby, we show that the robust estimation problem using IQCs with peak-to-peak performance can be convexified. We guarantee recursive feasibility, robust constraint satisfaction, and input-to-state stability of the resulting MPC scheme.
Nuclear Microreactor Control with Deep Reinforcement Learning
Authors: Leo Tunkle, Kamal Abdulraheem, Linyu Lin, Majdi I. Radaideh
Date: 2025-03-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional-integral-derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL's strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance -- an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with less control effort.
Performance analysis of metasurface-based spatial multimode transmission for 6G wireless communications
Authors: Ju Yong Lee, Seung-Won Keum, Sang Min Oh, Dang-Oh Kim, Dong-Ho Cho
Date: 2025-03-31 00:00:00

In 6th generation wireless communication technology, it is important to utilize space resources efficiently. Recently, holographic multiple-input multiple-output (HMIMO) and meta-surface technology have attracted attention as technologies that maximize space utilization for 6G mobile communications. However, studies on HMIMO communications are still in an initial stage and its fundamental limits are yet to be unveiled. It is well known that the Fourier transform relationship can be obtained using a lens in the optical field, but research to apply it to the mobile communication field is in the early stages. In this paper, we show that the Fourier transform relationship between signals can be obtained when two metasurfaces are aligned or unaligned, and analyze the transmission and reception power, and the maximum number of spatial multimodes that can be transmitted. In addition, to reduce transmission complexity, we propose a spatial multimode transmission system using three metasurfaces and analyze signal characteristics on the meta-surfaces. In numerical results, we provide the performance of spatial multimode transmission in case of using rectangular and Gaussian signals.
Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation
Authors: Mattia Scarpa, Francesco Pase, Ruggero Carli, Mattia Bruschetta, Franscesco Toso
Date: 2025-03-31 00:00:00
Tasks: Computational Efficiency

Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications. This paper presents a novel hybrid framework that combines physics-based thermal modeling with data-driven techniques to identify and correct power losses accurately using only temperature measurements. Our approach leverages a cascaded architecture where a neural network learns to correct the outputs of a nominal power loss model by backpropagating through a reduced-order thermal model. We explore two neural architectures, a bootstrapped feedforward network, and a recurrent neural network, demonstrating that the bootstrapped feedforward approach achieves superior performance while maintaining computational efficiency for real-time applications. Between the interconnection, we included normalization strategies and physics-guided training loss functions to preserve stability and ensure physical consistency. Experimental results show that our hybrid model reduces both temperature estimation errors (from 7.2+-6.8{\deg}C to 0.3+-0.3{\deg}C) and power loss prediction errors (from 5.4+-6.6W to 0.2+-0.3W) compared to traditional physics-based approaches, even in the presence of thermal model uncertainties. This methodology allows us to accurately estimate power losses without direct measurements, making it particularly helpful for real-time industrial applications where sensor placement is hindered by cost and physical limitations.
Set-based state estimation of nonlinear discrete-time systems using constrained zonotopes and polyhedral relaxations
Authors: Brenner S. Rego, Guilherme V. Raffo, Marco H. Terra, Joseph K. Scott
Date: 2025-03-31 00:00:00
Tasks: State Estimation

This paper presents a new algorithm for set-based state estimation of nonlinear discrete-time systems with bounded uncertainties. The novel method builds upon essential properties and computational advantages of constrained zonotopes (CZs) and polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is usually done using conservative linearization in the literature. The new method also refines the propagated enclosure using nonlinear measurements. To achieve this, a lifted polyhedral relaxation is computed for the composite nonlinear function of the system dynamics and measurement equations, in addition to incorporating the measured output through equality constraints. Polyhedral relaxations of trigonometric functions are enabled for the first time, allowing to address a broader class of nonlinear systems than our previous works. Additionally, an approach to obtain an equivalent enclosure with fewer generators and constraints is developed. Thanks to the advantages of the polyhedral enclosures based on factorable representations, the new state estimation method provides better approximations than those resulting from linearization procedures. This led to significant improvements in the computation of convex sets enclosing the system states consistent with measured outputs. Numerical examples highlight the advantages of the novel algorithm in comparison to existing CZ methods based on the Mean Value Theorem and DC programming principles.
Correct-by-construction requirement decomposition
Authors: Minghui Sun, Georgios Bakirtzis, Hassan Jafarzadeh, Cody Fleming
Date: 2019-09-04 00:00:00

In systems engineering, accurately decomposing requirements is crucial for creating well-defined and manageable system components, particularly in safety-critical domains. Despite the critical need, rigorous, top-down methodologies for effectively breaking down complex requirements into precise, actionable sub-requirements are scarce, especially compared to the wealth of bottom-up verification techniques. Addressing this gap, we introduce a formal decomposition for contract-based design that guarantees the correctness of decomposed requirements if specific conditions are met. Our (semi-)automated methodology augments contract-based design with reachability analysis and constraint programming to systematically identify, verify, and validate sub-requirements representable by continuous bounded sets -- continuous relations between real-valued inputs and outputs. We demonstrate the efficacy and practicality of a correct-by-construction approach through a comprehensive case study on a cruise control system, highlighting how our methodology improves the interpretability, tractability, and verifiability of system requirements.