pwc-archive/papers-with-abstracts huggingface dataset:
Spatiotemporal Trajectory Tracking Method for Vehicles Incorporating Lead-Lag Judgement
Authors: Yuan Li, Xiang Dong, Tao Li, Junfeng Hao, Xiaoxue Xu, Sana Ullaha, Yincai Cai, Peng Wu, Ting Peng
Date: 2025-02-06 00:00:00

In the domain of intelligent transportation systems, especially within the context of autonomous vehicle control, the preemptive holistic collaborative system has been presented as a promising solution to bring a remarkable enhancement in traffic efficiency and a substantial reduction in the accident rate, demonstrating a great potential of development. In order to ensure this system operates as intended, accurate tracking of the spatiotemporal trajectory is of crucial significance. Moreover, minimizing the tracking error is a necessary step in this process. To this end, a novel lead-lag judgment mechanism is proposed. This mechanism precisely quantifies the longitudinal positional deviation between the vehicle and the target trajectory over time, then the deviation is corrected with a real - time acceleration compensation strategy, as a result, the accuracy and reliability of trajectory tracking are significantly enhanced. Real - vehicle experiments were conducted in a dedicated test field to validate the feasibility of this innovative approach empirically. Subsequently, the obtained tracking data was subsequent processed using the lead-lag judgment mechanism. In this step, we carefully analyzed the spatiotemporal error patterns between the vehicle and the target trajectory under different alignments and speeds. Finally, using real highway speed and alignment data, we conducted comprehensive spatiotemporal trajectory tracking simulations. Through experiments and simulations, tracking errors maintained in an acceptable range and reasonable spatiotemporal distance is given during the preemptive merging process on highway ramps. Overall, this study offers valuable insights for highway ramp emerging safety. Future work can expand on these findings.
Exact Covariance Characterization for Controlled Linear Systems subject to Stochastic Parametric and Additive Uncertainties
Authors: Kaouther Moussa, Mirko Fiacchini
Date: 2025-02-06 00:00:00

This work addresses the exact characterization of the covariance dynamics related to linear discrete-time systems subject to both additive and parametric stochastic uncertainties that are potentially unbounded. The derived exact representation allows to understand how the covariance of the multiplicative parametric uncertainties affects the stability of the state covariance dynamics through a transformation of the parameters covariance matrix, allowing therefore to address the problem of control design for state covariance dynamics in this context. Numerical results assess this new characterization by comparing it to the empirical covariance and illustrating the control design problem.
On the existence of strong functional observer
Authors: Michael Di Loreto, Damien Eberard
Date: 2025-02-06 00:00:00

For arbitrary linear time-invariant systems, the existence of a strong functional observer is investigated. Such observer determines, from the available measurement on the plant, an estimate of a function of the state and the input. This estimate converges irrespective to initial state and input. This formulation encompass the cases of observer existence for known or unknown inputs and generalizes state-of-art. Necessary and sufficient conditions for such an existence are proposed, in the framework of state-space representation. These conditions are based on functional detectability property and its generalizations for arbitrary input, which include considerations on convergence of the estimation, irrespective to the initial state and the input. Known results on state detectability, input reconstruction or functional detectability are retrieved by particularizing the proposed conditions.
Geometric Stabilization of Virtual Nonlinear Nonholonomic Constraints
Authors: Efstratios Stratoglou, Alexandre Anahory Simoes, Anthony Bloch, Leonardo Colombo
Date: 2025-02-06 00:00:00

In this paper, we address the problem of stabilizing a system around a desired manifold determined by virtual nonlinear nonholonomic constraints. Virtual constraints are relationships imposed on a control system that are rendered invariant through feedback control. Virtual nonholonomic constraints represent a specific class of virtual constraints that depend on the system's velocities in addition to its configurations. We derive a control law under which a mechanical control system achieves exponential convergence to the virtual constraint submanifold, and rendering it control-invariant. The proposed controller's performance is validated through simulation results in two distinct applications: flocking motion in multi-agent systems and the control of an unmanned surface vehicle (USV) navigating a stream.
Stabilizing scheduling logic for networked control systems under limited capacity and lossy communication networks
Authors: Anubhab Dasgupta
Date: 2025-02-06 00:00:00
Tasks: Scheduling

In this paper we address the problem of designing scheduling logic for stabilizing Networked Control Systems (NCSs) with plants and controllers remotely-located over a limited capacity communication network subject to data losses. Our specific contributions include characterization of stability under worst case data loss using an inequality associated with a cycle on a graph. This is eventually formulated as a feasibility problem to solve for certain parameters (\(T\)-factors) used to design a periodic scheduling logic. We show that given a solution to the feasibility problem, the designed scheduling logic guarantees \emph{global asymptotic stability} for all plants of the network under all admissible data losses. We also derive sufficient conditions on the number of plants and the capacity of the network for the existence of a solution to the feasibility problem. Given that a sufficient condition is satisfied, we discuss the procedure to obtain the feasible \(T\)-factors. We use tools from switched systems theory and graph theory in this work. A numerical experiment is provided to verify our results.
On the Number of Control Nodes in Boolean Networks with Degree Constraints
Authors: Liangjie Sun, Wai-Ki Ching, Tatsuya Akutsu
Date: 2025-02-06 00:00:00
Tasks: Negation

This paper studies the minimum control node set problem for Boolean networks (BNs) with degree constraints. The main contribution is to derive the nontrivial lower and upper bounds on the size of the minimum control node set through combinatorial analysis of four types of BNs (i.e., $k$-$k$-XOR-BNs, simple $k$-$k$-AND-BNs, $k$-$k$-AND-BNs with negation and $k$-$k$-NC-BNs, where the $k$-$k$-AND-BN with negation is an extension of the simple $k$-$k$-AND-BN that considers the occurrence of negation and NC means nested canalyzing). More specifically, four bounds for the size of the minimum control node set: general lower bound, best case upper bound, worst case lower bound, and general upper bound are studied, where the general lower bound is a value that is not less than the size of the control node set for any BN, the general upper bound is the maximum value of the size of the minimum control node set for any BN, while the best case upper bound (resp., the worst case lower bound) is the minimum (resp., maximum) value currently found, which is obtained from some BN. By dividing nodes into three disjoint sets, extending the time to reach the target state, and utilizing necessary conditions for controllability, these bounds are obtained, and further meaningful results and phenomena are discovered. Notably, all of the above results involving the AND function also apply to the OR function.
Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning
Authors: Bokeng Zheng, Bo Rao, Tianxiang Zhu, Chee Wei Tan, Jingpu Duan, Zhi Zhou, Xu Chen, Xiaoxi Zhang
Date: 2025-02-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, point of interests

Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living.Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities. In particular, ride-hailing vehicles can effectively facilitate flexible data collection and contribute towards urban intelligence, despite resource limitations. Therefore, this work explores a promising scenario, where edge-assisted vehicles perform joint tasks of order serving and the emerging foundation model fine-tuning using various urban data. However, integrating the VCS AI task with the conventional order serving task is challenging, due to their inconsistent spatio-temporal characteristics: (i) The distributions of ride orders and data point-of-interests (PoIs) may not coincide in geography, both following a priori unknown patterns; (ii) they have distinct forms of temporal effects, i.e., prolonged waiting makes orders become instantly invalid while data with increased staleness gradually reduces its utility for model fine-tuning.To overcome these obstacles, we propose an online framework based on multi-agent reinforcement learning (MARL) with careful augmentation. A new quality-of-service (QoS) metric is designed to characterize and balance the utility of the two joint tasks, under the effects of varying data volumes and staleness. We also integrate graph neural networks (GNNs) with MARL to enhance state representations, capturing graph-structured, time-varying dependencies among vehicles and across locations. Extensive experiments on our testbed simulator, utilizing various real-world foundation model fine-tuning tasks and the New York City Taxi ride order dataset, demonstrate the advantage of our proposed method.
Enhancing Hallucination Detection through Noise Injection
Authors: Litian Liu, Reza Pourreza, Sunny Panchal, Apratim Bhattacharyya, Yao Qin, Roland Memisevic
Date: 2025-02-06 00:00:00
Tasks: Hallucination

Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the safe deployment of LLMs. Recent research has linked hallucinations to model uncertainty, suggesting that hallucinations can be detected by measuring dispersion over answer distributions obtained from a set of samples drawn from a model. While drawing from the distribution over tokens defined by the model is a natural way to obtain samples, in this work, we argue that it is sub-optimal for the purpose of detecting hallucinations. We show that detection can be improved significantly by taking into account model uncertainty in the Bayesian sense. To this end, we propose a very simple and efficient approach that perturbs an appropriate subset of model parameters, or equivalently hidden unit activations, during sampling. We demonstrate its effectiveness across a wide range of datasets and model architectures.
Replacing K-infinity Function with Leaky ReLU in Barrier Function Design: A Union of Invariant Sets Approach for ReLU-Based Dynamical Systems
Authors: Pouya Samanipour, Hasan Poonawala
Date: 2025-02-06 00:00:00

In this paper, a systematic framework is presented for determining piecewise affine PWA barrier functions and their corresponding invariant sets for dynamical systems identified via Rectified Linear Unit (ReLU) neural networks or their equivalent PWA representations. A common approach to determining the invariant set is to use Nagumo's condition, or to utilize the barrier function with a class K-infinity function. It may be challenging to find a suitable class K-infinity function in some cases. We propose leaky ReLU as an efficient substitute for the complex nonlinear K-infinity function in our formulation. Moreover, we propose the Union of Invariant Sets (UIS) method, which combines information from multiple invariant sets in order to compute the largest possible PWA invariant set. The proposed framework is validated through multiple examples, showcasing its potential to enhance the analysis of invariant sets in ReLU-based dynamical systems. Our code is available at: https://github.com/PouyaSamanipour/UIS.git.
On the effects of angular acceleration in orientation estimation using inertial measurement units
Authors: Felix Brändle, David Meister, Marc Seidel, Robin Strässer, Frank Allgöwer
Date: 2025-02-06 00:00:00
Tasks: Sensor Fusion

Determining the orientation of a rigid body using an inertial measurement unit is a common problem in many engineering applications. However, sensor fusion algorithms suffer from performance loss when other motions besides the gravitational acceleration affect the accelerometer. In this paper, we show that linear accelerations caused by rotational accelerations lead to additional zeros in the linearized transfer functions, which are strongly dependent on the operating point. These zeros lead to non-minimum phase systems, which are known to be challenging to control. In addition, we demonstrate how Mahony and Madgwick filters can mitigate the effects of the additional acceleration, but at the cost of reduced bandwidth. This generates insights into a fundamental problem in estimation, that are transferable to many practical applications.
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
Authors: Bo Wen, Xin Zhang
Date: 2025-02-05 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Layout Design, Prompt Engineering, Spatial Reasoning

This paper presents SOLOMON, a novel Neuro-inspired Large Language Model (LLM) Reasoning Network architecture that enhances the adaptability of foundation models for domain-specific applications. Through a case study in semiconductor layout design, we demonstrate how SOLOMON enables swift adaptation of general-purpose LLMs to specialized tasks by leveraging Prompt Engineering and In-Context Learning techniques. Our experiments reveal the challenges LLMs face in spatial reasoning and applying domain knowledge to practical problems. Results show that SOLOMON instances significantly outperform their baseline LLM counterparts and achieve performance comparable to state-of-the-art reasoning model, o1-preview. We discuss future research directions for developing more adaptive AI systems that can continually learn, adapt, and evolve in response to new information and changing requirements.
Deep Reinforcement Learning-Based Optimization of Second-Life Battery Utilization in Electric Vehicles Charging Stations
Authors: Rouzbeh Haghighi, Ali Hassan, Van-Hai Bui, Akhtar Hussain, Wencong Su
Date: 2025-02-05 00:00:00
Tasks: Deep Reinforcement Learning

The rapid rise in electric vehicle (EV) adoption presents significant challenges in managing the vast number of retired EV batteries. Research indicates that second-life batteries (SLBs) from EVs typically retain considerable residual capacity, offering extended utility. These batteries can be effectively repurposed for use in EV charging stations (EVCS), providing a cost-effective alternative to new batteries and reducing overall planning costs. Integrating battery energy storage systems (BESS) with SLBs into EVCS is a promising strategy to alleviate system overload. However, efficient operation of EVCS with integrated BESS is hindered by uncertainties such as fluctuating EV arrival and departure times and variable power prices from the grid. This paper presents a deep reinforcement learning-based (DRL) planning framework for EV charging stations with BESS, leveraging SLBs. We employ the advanced soft actor-critic (SAC) approach, training the model on a year's worth of data to account for seasonal variations, including weekdays and holidays. A tailored reward function enables effective offline training, allowing real-time optimization of EVCS operations under uncertainty.
Verification and Synthesis Methods for High-Order Control Barrier Functions
Authors: Ellie Pond, Matthew Hale
Date: 2025-02-05 00:00:00

High-order control barrier functions (HOCBFs) can be used to provide autonomous systems with safety, though computational methods to verify and synthesize these functions remain lacking. In this work, we address this need by formulating SOS programs that verify and synthesize HOCBFs, such that continued safety is always guaranteed forward in time. We first propose a verification SOS program for systems with (i) one or multiple HOCBFs, (ii) a control Lyapunov function (CLF), and (iii) input constraints, and we show that a solution to this problem guarantees that the online implementation of the system is always safe. Next, we propose a sequence of SOS programs that synthesize the class K functions used in an HOCBF, and we show that this sequence of problems ensures that a system is guaranteed to remain safe while running. After that, a synthesis framework is given that ensures real-time safety for systems with (i) multiple HOCBFs, (ii) a CLF, and (iii) input constraints. Our developments are illustrated in numerical simulations for a system with seven HOCBFs of maximum relative degree two, with 14 total unknown class K functions, all of which are successfully synthesized in a way that produces safe autonomy.
Optimal PMU Placement for Kalman Filtering of DAE Power System Models
Authors: Milos Katanic, Yi Guo, John Lygeros, Gabriela Hug
Date: 2025-02-05 00:00:00
Tasks: Benchmarking, State Estimation

Optimal sensor placement is essential for minimizing costs and ensuring accurate state estimation in power systems. This paper introduces a novel method for optimal sensor placement for dynamic state estimation of power systems modeled by differential-algebraic equations. The method identifies optimal sensor locations by minimizing the steady-state covariance matrix of the Kalman filter, thus minimizing the error of joint differential and algebraic state estimation. The problem is reformulated as a mixed-integer semidefinite program and effectively solved using off-the-shelf numerical solvers. Numerical results demonstrate the merits of the proposed approach by benchmarking its performance in phasor measurement unit placement in comparison to greedy algorithms.
Fault-Tolerant Control for System Availability and Continuous Operation in Heavy-Duty Wheeled Mobile Robots
Authors: Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
Date: 2025-02-05 00:00:00

When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a certain extent, ensuring continuous operation. To this end, this paper introduces a model-free hierarchical control with fault accommodation (MFHCA) framework designed to address sensor and actuator faults in hydraulically powered HD-WMRs with independently controlled wheels. To begin, a novel mathematical representation of the motion dynamics of HD-WMRs, incorporating both sensor and actuator fault modes, is investigated. Subsequently, the MFHCA framework is proposed to manage all wheels under various fault modes, ensuring that each wheel tracks the reference driving velocities and steering angles, which are inverse kinematically mapped from the angular and linear velocities commanded in the HD-WMR's base frame. To do so, this framework generates appropriate power efforts in independently valve-regulated wheels to accommodate the adaptively isolated faults, thereby ensuring exponential stability. The experimental analysis of a 6,500-kg hydraulic-powered HD-WMR under various fault modes and rough terrains demonstrates the validity of the MFHCA framework.
Model Reference-Based Control with Guaranteed Predefined Performance for Uncertain Strict-Feedback Systems
Authors: Mehdi Heydari Shahna, Jukka-Pekka Humaloja, Jouni Mattila
Date: 2025-02-05 00:00:00

To address the complexities posed by time- and state-varying uncertainties and the computation of analytic derivatives in strict-feedback form (SFF) systems, this study introduces a novel model reference-based control (MRBC) framework which applies locally to each subsystem (SS), to ensure output tracking performance within the specified transient and steady-state response criteria. This framework includes 1) novel homogeneous adaptive estimators (HAEs) designed to match the uncertain nonlinear SFF system to a reference model, enabling easier analysis and control design at the $SS$ level, and 2) model-based homogeneous adaptive controllers enhanced by logarithmic barrier Lyapunov functions (HAC-BLFs), intended to control the reference model provided by HAEs in each SS, while ensuring the prescribed tracking responses under control amplitude saturation. The inherently robust MRBC achieves uniformly exponential stability using a generic stability connector term, which addresses dynamic interactions between the adjacent SSs. The parameter sensitivities of HAEs and HAC-BLFs in the MRBC framework are analyzed, focusing on the system's robustness and responsiveness. The proposed MRBC framework is experimentally validated through several scenarios involving an electromechanical linear actuator system with an uncertain SFF, subjected loading disturbance forces challenging 0-95% of its capacity.
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
Authors: Saba Sanami, Amir G. Aghdam
Date: 2025-02-05 00:00:00
Tasks: Anomaly Detection, Reinforcement Learning (RL), Time Series, Unsupervised Anomaly Detection

This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant challenge is the limited availability of anomalous data, often leading to misclassifying anomalies as normal events, thus raising false negatives. RL can help overcome this limitation by promoting exploration and balancing exploitation during training, effectively preventing overfitting. Wavelet analysis is also utilized to enhance anomaly detection, enabling time-series data decomposition into both time and frequency domains. This approach captures anomalies at multiple resolutions, with wavelet coefficients extracted to detect both sudden and subtle shifts in the data, thereby refining the anomaly detection process. We calibrate the decision boundary by generating synthetic anomalies and embedding a supervised framework within the model. This supervised element aids the unsupervised learning process by fine-tuning the decision boundary and increasing the model's capacity to distinguish between normal and anomalous patterns effectively.
Low-cost analog signal chain for transmit-receive circuits of passive induction-based resonators
Authors: Fabian Mohn, Florian Thieben, Tobias Knopp
Date: 2025-02-05 00:00:00

Passive wireless sensors are crucial in modern medical and industrial settings to monitor procedures and conditions. We demonstrate a circuit to inductively excite passive resonators and to conduct their decaying signal response to a low noise amplifier. Two design variations of a generic transmit-receive signal chain are proposed, measured, and described in detail for the purpose of facilitating replication. Instrumentation and design aim to be scalable for multi-channel array configurations, using either off-the-shelf class-D audio amplifiers or a custom full H-bridge. Measurements are conducted on miniature magneto-mechanical resonators in the ultra low frequency range to enable sensing and tracking applications of such devices in different environments.