pwc-archive/papers-with-abstracts huggingface dataset:
The Reliability of Remotely Piloted Aircraft System Performance under Communication Loss and Latency Uncertainties
Authors: Yutian Pang, Andrew Paul Kendall, John-Paul Clarke
Date: 2025-01-13 00:00:00

Mission-critical use of highly maneuverable Remotely Piloted Aircraft Systems (RPAS) requires a thorough understanding of the reliability of their communication systems. Investigations into system-level performance under stochastic aviation communication conditions are critical for estimating mission success rates and assessing the risks associated with integrating RPAS into existing airspace, ensuring overall aviation safety. This study aims to quantify the impact of communication latency and complete signal loss on the mission completion performance of a highly maneuverable RPAS. The mission is defined as a static waypoint tracking task in three-dimensional airspace. We start with examining and deriving mathematical formulations of key reliability metrics of Required Communication Performance (RCP). These stochastic factors are then embedded into flight control simulations (i.e., communication availability and latency) to examine the system behavior. Lastly, we generate mission success rate and mission completion time envelopes through extensive multiprocessing Monte Carlo simulations through high-performance computing. We discover a drastic deterioration in flight performance while latency or availability erodes the stability margin. In addition, we propose a new reliability metric, namely \textit{communicability}, which integrates three key RCP metrics and helps understanding the maximum tolerable latency to flight control. The procedure and results obtained from this research inform engineers designing RPAS with better trade-off between communication capability and flight control performance. Future works includes exploring alternative flight simulators (i.e., nonlinear dynamic inversion) with other missions (i.e., dynamic waypoint following), or develop delay-compensated optimal controls. The analysis on stability margin is also desired for theoretical verification.
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems
Authors: Shahab Ataei, Dipankar Maity, Debdipta Goswami
Date: 2025-01-13 00:00:00
Tasks: Model Predictive Control, Quantization

Koopman-based lifted linear identification have been widely used for data-driven prediction and model predictive control (MPC) of nonlinear systems. It has found applications in flow-control, soft robotics, and unmanned aerial vehicles (UAV). For autonomous systems, this system identification method works by embedding the nonlinear system in a higher-dimensional linear space and computing a finite-dimensional approximation of the corresponding Koopman operator with the Extended Dynamic Mode Decomposition (EDMD) algorithm. EDMD is a data-driven algorithm that estimates an approximate linear system by lifting the state data-snapshots via nonlinear dictionary functions. For control systems, EDMD is further modified to utilize both state and control data-snapshots to estimate a lifted linear predictor with control input. This article investigates how the estimation process is affected when the data is quantized. Specifically, we examine the fundamental connection between estimates of the linear predictor matrices obtained from unquantized data and those from quantized data via modified EDMD. Furthermore, using the law of large numbers, we demonstrate that, under a large data regime, the quantized estimate can be considered a regularized version of the unquantized estimate. We also explore the relationship between the two estimates in the finite data regime. We further analyze the effect of nonlinear lifting functions on this regularization due to quantization. The theory is validated through repeated numerical experiments conducted on several control systems. The effect of quantization on the MPC performance is also demonstrated.
The Ingenuity Mars Helicopter Specified and Analyzed with the Real-time Mode-aware Dataflow Model
Authors: Guillaume Roumage, Selma Azaiez, Cyril Faure, Stéphane Louise
Date: 2025-01-13 00:00:00
Tasks: Ingenuity

Ingenuity is an autonomous Cyber-Pysical System (CPS) that has successfully completed more than 70 flights over Mars between 2021 and 2024. Ensuring the safety of its mission is paramount, as any failure could result in catastrophic economic damage and significant financial losses. Dataflow Models of Computation and Communication (DF MoCCs) serve as a formal framework for specifying and analyzing the timing behavior of such CPSs. In particular, the Real-time Mode-aware Dataflow (RMDF) model is highly suitable to specify and analyze real-time and mode-dependent Cyber-Physical Systems (CPSs) like Ingenuity. This paper showcases the application of RMDF for the specification and analysis of Ingenuity. We propose a dataflow specification of Ingenuity, analyze its timing behavior, and provide a feasibility test. Finally, we proposed a plausible explanation of the timing anomaly that occurred during the sixth flight of Ingenuity.
Real-time Mode-Aware Dataflow: A Dataflow Model to Specify and Analyze Mode-dependent CPSs under Relaxed Timing Constraints
Authors: Guillaume Roumage, Selma Azaiez, Cyril Faure, Stéphane Louise
Date: 2025-01-13 00:00:00
Tasks: Ingenuity

Modern Cyber-Physical Systems (CPS) often exhibit both relaxed real-time constraints and a mode-dependent execution. Relaxed real-time constraints mean that only a subset of the processes of a CPS have real-time constraints, and a mode-dependent CPS has conditional execution branches. Static analysis tools, such as the PolyGraph model (a formalism extending the Cyclo-Static Dataflow model with real-time constraints), can specify and analyze systems with relaxed real-time constraints. However, PolyGraph is limited in its ability to specify and analyze mode-dependent CPSs. This paper extends PolyGraph with routing actors, yielding the Routed PolyGraph model. This model is further extended to the Real-time Mode-Aware Dataflow (RMDF), which both leverages routing actors and incorporates a new dataflow actor to specify mode-dependent CPSs under relaxed real-time constraints. This paper also extends the static analyses of PolyGraph to RMDF. We showcase the application of RMDF with a specification and an analysis (derivation of timing constraints at the job-level and a feasibility test) of the vision processing system of the Ingenuity Mars helicopter.
IEEE_TIE25: Analysis and Synthesis of DOb-based Robust Motion Controllers
Authors: Emre Sariyildiz
Date: 2025-01-13 00:00:00

By employing a unified state-space design framework, this paper proposes a novel systematic analysis and synthesis method that facilitates the implementation of both conventional zero-order (ZO) and high-order (HO) DObs. Furthermore, this design method supports the development of advanced DObs (e.g., the proposed High-Performance (HP) DOb in this paper), enabling more accurate disturbance estimation and, consequently, enhancing the robust stability and performance of motion control systems. Lyapunov direct method is employed in the discrete-time domain to analyse the stability of the proposed digital robust motion controllers. The analysis demonstrates that the proposed DObs are stable in the sense that the estimation error is uniformly ultimately bounded when subjected to bounded disturbances. Additionally, they are proven to be asymptotically stable under specific disturbance conditions, such as constant disturbances for the ZO and HP DObs. Stability constraints on the design parameters of the DObs are analytically derived, providing effective synthesis tools for the implementation of the digital robust motion controllers. The discrete-time analysis facilitates the derivation of more practical design constraints. The proposed analysis and synthesis methods have been rigorously validated through experimental evaluations, confirming their effectiveness.
Optimizing Phase Allocation in Unbalanced Power Distribution Networks using a Linearized DistFlow Formulation
Authors: Rahul K. Gupta, Daniel K. Molzahn
Date: 2025-01-12 00:00:00

Power distribution networks, especially in North America, are often unbalanced but are designed to keep unbalance levels within the limits specified by IEEE, IEC, and NEMA standards. However, rapid integration of unbalanced devices, such as electric vehicle (EV) chargers and single-phase solar plants, can exacerbate these imbalances. This increase can trigger protection devices, increase losses, and potentially damage devices. To address this issue, phase swapping (or phase allocation) has been proposed. Existing approaches predominantly rely on heuristic methods. In this work, we develop a mixed integer linear programming (MILP) approach for phase allocation. Our approach uses linearized DistFlow equations to represent the distribution network and incorporates a phase consistency constraint, enforced with binary variables, to ensure that downstream phase configurations align with upstream configurations. We validate the proposed approach on multiple benchmark test cases and demonstrate that it effectively improves network balance, as quantified by various metrics.
Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs
Authors: Jialong Chen, Jimin Wang, Ji-Feng Zhang
Date: 2025-01-12 00:00:00
Tasks: Stochastic Optimization

This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent's state and tracking variable to prevent information leakage, and then perturbed states and tracking variables are transmitted to neighbors. We design two novel schemes of the iteration step-sizes and the sampling number for the algorithm. By using the sampling parameter-controlled subsampling method, both schemes enhance the differential privacy level, and achieve the finite cumulative privacy budget even over infinite iterations. The convergence rate of the algorithm is shown for both nonconvex with the Polyak-Lojasiewicz condition and strongly convex objectives: Scheme (S1) achieves the polynomial convergence rate, and Scheme (S2) achieves the exponential convergence rate. The trade-off between the privacy and the convergence rate is presented. The algorithm's effectiveness and superior performance over the existing works are demonstrated through numerical examples of distributed training on benchmark datasets "MNIST" and "CIFAR-10".
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach
Authors: Changyuan Zhao, Guangyuan Liu, Bin Xiang, Dusit Niyato, Benoit Delinchant, Hongyang Du, Dong In Kim
Date: 2025-01-12 00:00:00
Tasks: Anomaly Detection, Denoising

With advancements in physical power systems and network technologies, integrated Cyber-Physical Power Systems (CPPS) have significantly enhanced system monitoring and control efficiency and reliability. This integration, however, introduces complex challenges in designing coherent CPPS, particularly as few studies concurrently address the deployment of physical layers and communication connections in the cyber layer. This paper addresses these challenges by proposing a framework for robust sensor placement to optimize anomaly detection in the physical layer and enhance communication resilience in the cyber layer. We model the CPPS as an interdependent network via a graph, allowing for simultaneous consideration of both layers. Then, we adopt the Log-normal Shadowing Path Loss (LNSPL) model to ensure reliable data transmission. Additionally, we leverage the Fiedler value to measure graph resilience against line failures and three anomaly detectors to fortify system safety. However, the optimization problem is NP-hard. Therefore, we introduce the Experience Feedback Graph Diffusion (EFGD) algorithm, which utilizes a diffusion process to generate optimal sensor placement strategies. This algorithm incorporates cross-entropy gradient and experience feedback mechanisms to expedite convergence and generate higher reward strategies. Extensive simulations demonstrate that the EFGD algorithm enhances model convergence by 18.9% over existing graph diffusion methods and improves average reward by 22.90% compared to Denoising Diffusion Policy Optimization (DDPO) and 19.57% compared to Graph Diffusion Policy Optimization (GDPO), thereby significantly bolstering the robustness and reliability of CPPS operations.
Coordinated Deliverable Energy Flexibility from EV Aggregators in Distribution Networks
Authors: Arash Baharvandi, Duong Tung Nguyen
Date: 2025-01-12 00:00:00

This paper presents a coordinated framework to optimize electric vehicle (EV) charging considering grid constraints and system uncertainties. The proposed framework consists of two optimization models. In particular, the distribution system operator (DSO) solves the first model to optimize the amount of deliverable energy flexibility that can be obtained from EV aggregators. To address the uncertainties of loads and solar energy generation, a hybrid robust/stochastic approach is employed, enabling the transformation of uncertainty-related constraints into a set of equivalent deterministic constraints. Once the DSO has computed the optimal energy flexibility, each aggregator utilizes the second optimization model to optimize the charging schedule for its respective fleet of EVs. Numerical simulations are performed on a modified IEEE 33-bus distribution network to illustrate the efficiency of the proposed framework.
A Geometric Analysis-Based Safety Assessment Framework for MASS Route Decision-Making in Restricted Waters
Authors: Zilong Xu, ZiHao Wang, He Li, Dingli Yu, Zaili Yang, Jin Wang
Date: 2025-01-11 00:00:00
Tasks: Decision Making

To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways. Utilizing line and point geometric elements to define waterway boundaries, the framework enables to construct a dynamic width characterization function to quantify spatial safety along intricate waterways. An iterative method is developed to calculate this function, enabling an abstracted spatial property representation of the waterways. Based on this, we introduce a navigational safety index that balances global navigational safety and local risk to determine the safest route. To accommodate ship kinematic constraints, path modifications are applied using a dynamic window approach. A case study in a simulated Port of Hamburg environment shows that GARSA effectively identifies safe routes and avoids the risk of entering narrow waterways in an autonomous manner, thereby prioritizing safety in route decision-making for MASS in confined waters.
Modeling the residual queue and queue-dependent capacity in a static traffic assignment problem
Authors: Hao Fu, William H. K. Lam, Wei Ma, Yuxin Shi, Rui Jiang, Huijun Sun, Ziyou Gao
Date: 2025-01-11 00:00:00

The residual queue during a given study period (e.g., peak hour) is an important feature that should be considered when solving a traffic assignment problem under equilibrium for strategic traffic planning. Although studies have focused extensively on static or quasi-dynamic traffic assignment models considering the residual queue, they have failed to capture the situation wherein the equilibrium link flow passing through the link is less than the link physical capacity under congested conditions. To address this critical issue, we introduce a novel static traffic assignment model that explicitly incorporates the residual queue and queue-dependent link capacity. The proposed model ensures that equilibrium link flows remain within the physical capacity bounds, yielding estimations more aligned with data observed by traffic detectors, especially in oversaturated scenarios. A generalized link cost function considering queue-dependent capacity, with an additional queuing delay term is proposed. The queuing delay term represents the added travel cost under congestion, offering a framework wherein conventional static models, both with and without physical capacity constraints, become special cases of our model. Our study rigorously analyzes the mathematical properties of the new model, establishing the theoretical uniqueness of solutions for link flow and residual queue under certain conditions. We also introduce a gradient projection-based alternating minimization algorithm tailored for the proposed model. Numerical examples are conducted to demonstrate the superiority and merit of the proposed model and solution algorithm.
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis
Authors: Dongdong Li, Jiuxiang Dong
Date: 2025-01-11 00:00:00
Tasks: Q-Learning

In this paper, two model-free optimal output tracking frameworks based on policy iteration for discrete-time multi-agent systems are proposed. First, we establish a framework of stabilizing policy iteration that can start from any initial feedback control policy, relaxing the dependence of traditional policy iteration on the initial stabilizing control policy. Then, another efficient and equivalent $Q$-learning policy iteration framework is developed, which is shown to require only less system data to get the same results as the stabilizing policy iteration. Both frameworks obtain stabilizing control policy by iterating the stabilizing virtual closed-loop system step-by-step to the actual closed-loop system. Multiple explicit schemes for the iteration step-size/coefficient are designed and their stability during the above iterations is analyzed. By using the generated closed-loop stabilizing control policy and two frameworks, the optimal feedback control gain is obtained. The approximate solution of the regulator equations is found by model-free iteration, which leads to the optimal feedforward gain. Finally, the cooperative optimal output tracking is realized by a distributed feedforward-feedback controller. The proposed algorithms are validated by simulation.
A Comparison of Strategies to Embed Physics-Informed Neural Networks in Nonlinear Model Predictive Control Formulations Solved via Direct Transcription
Authors: Carlos Andrés Elorza Casas, Luis A. Ricardez-Sandoval, Joshua L. Pulsipher
Date: 2025-01-10 00:00:00
Tasks: Model Predictive Control

This study aims to benchmark candidate strategies for embedding neural network (NN) surrogates in nonlinear model predictive control (NMPC) formulations that are subject to systems described with partial differential equations and that are solved via direct transcription (i.e., simultaneous methods). This study focuses on the use of physics-informed NNs and physics-informed convolutional NNs as the internal (surrogate) models within the NMPC formulation. One strategy embeds NN models as explicit algebraic constraints, leveraging the automatic differentiation (AD) of an algebraic modelling language (AML) to evaluate the derivatives. Alternatively, the solver can be provided with derivatives computed external to the AML via the AD routines of the machine learning environment the NN is trained in. The three numerical experiments considered in this work reveal that replacing mechanistic models with NN surrogates may not always offer computational advantages when smooth activation functions are used in conjunction with a local nonlinear solver (e.g., Ipopt), even with highly nonlinear systems. Moreover, in this context, the external function evaluation of the NN surrogates often outperforms the embedding strategies that rely on explicit algebraic constraints, likely due to the difficulty in initializing the auxiliary variables and constraints introduced by explicit algebraic reformulations.
Best Response Convergence for Zero-sum Stochastic Dynamic Games with Partial and Asymmetric Information
Authors: Yuxiang Guan, Iman Shames, Tyler H. Summers
Date: 2025-01-10 00:00:00

We analyze best response dynamics for finding a Nash equilibrium of an infinite horizon zero-sum stochastic linear quadratic dynamic game (LQDG) with partial and asymmetric information. We derive explicit expressions for each player's best response within the class of pure linear dynamic output feedback control strategies where the internal state dimension of each control strategy is an integer multiple of the system state dimension. With each best response, the players form increasingly higher-order belief states, leading to infinite-dimensional internal states. However, we observe in extensive numerical experiments that the game's value converges after just a few iterations, suggesting that strategies associated with increasingly higher-order belief states eventually provide no benefit. To help explain this convergence, our numerical analysis reveals rapid decay of the controllability and observability Gramian eigenvalues and Hankel singular values in higher-order belief dynamics, indicating that the higher-order belief dynamics become increasingly difficult for both players to control and observe. Consequently, the higher-order belief dynamics can be closely approximated by low-order belief dynamics with bounded error, and thus feedback strategies with limited internal state dimension can closely approximate a Nash equilibrium.
Nonlinear port-Hamiltonian system identification from input-state-output data
Authors: Karim Cherifi, Achraf El Messaoudi, Hannes Gernandt, Marco Roschkowski
Date: 2025-01-10 00:00:00

A framework for identifying nonlinear port-Hamiltonian systems using input-state-output data is introduced. The framework utilizes neural networks' universal approximation capacity to effectively represent complex dynamics in a structured way. We show that using the structure helps to make long-term predictions compared to baselines that do not incorporate physics. We also explore different architectures based on MLPs, KANs, and using prior information. The technique is validated through examples featuring nonlinearities in either the skew-symmetric terms, the dissipative terms, or the Hamiltonian.
Optimizing Experiments for Accurate Battery Circuit Parameters Estimation: Reduction and Adjustment of Frequency Set Used in Electrochemical Impedance Spectroscopy
Authors: Vladimir Sovljanski, Mario Paolone, Sylvain Tant, Damien Pierre Sainflou
Date: 2025-01-10 00:00:00
Tasks: Experimental Design

In this paper, we study a suitable experimental design of electrochemical impedance spectroscopy (EIS) to reduce the number of frequency points while not significantly affecting the uncertainties of the estimated cell's equivalent circuit model (ECM) parameters. It is based on an E-optimal experimental design that aims to maximize the information about the ECM parameters collected by EIS measurements and, at the same time, minimize the overall uncertainty. In a numerical experiment, we first analyze to which extent reducing the number of measurement points at low frequencies affects the uncertainty of the estimated parameters. Secondly, we show that applying the frequency adjustments can lead to the same or even improved global uncertainty of ECM parameter estimates as with a higher number of measurements. This is numerically verified through a case study using the ECM parameters of a commercial battery cell.