pwc-archive/papers-with-abstracts huggingface dataset:
A Two-Stage Optimization Method for Real-Time Parameterization of PV-Farm Digital Twin
Authors: Jong Ha Woo, Qi Xiao, Victor Daldegan Paduani, Ning Lu
Date: 2024-10-05 00:00:00

Digital twins (DTs) are high-fidelity virtual models of physical systems. This paper details a novel two-stage optimization method for real-time parameterization of photovoltaic digital twins (PVDTs) using field measurements. Initially, the method estimates equivalent irradiance from PV power, voltage, and current data, eliminating the need for direct irradiance sensors. This is crucial for tuning the DT's parameters to actual environmental conditions, thereby improving power prediction accuracy. The second stage focuses on refining these parameters by minimizing discrepancies between measured and predicted outputs. This optimization utilizes the estimated equivalent irradiance as a model input, maintaining synchronization with real-world conditions. Parameter updates are event-trigger, launched when deviations exceed predefined thresholds. This strategy optimizes prediction accuracy and manages communication loads efficiently. Validated with extensive data from a PV farm, this approach outperforms existing methodologies in predictive accuracy and operational efficiency, significantly improving the performance DTs in real-time grid operations.
Compositional Planning for Logically Constrained Multi-Agent Markov Decision Processes
Authors: Krishna C. Kalagarla, Matthew Low, Rahul Jain, Ashutosh Nayyar, Pierluigi Nuzzo
Date: 2024-10-05 00:00:00

Designing control policies for large, distributed systems is challenging, especially in the context of critical, temporal logic based specifications (e.g., safety) that must be met with high probability. Compositional methods for such problems are needed for scalability, yet relying on worst-case assumptions for decomposition tends to be overly conservative. In this work, we use the framework of Constrained Markov Decision Processes (CMDPs) to provide an assume-guarantee based decomposition for synthesizing decentralized control policies, subject to logical constraints in a multi-agent setting. The returned policies are guaranteed to satisfy the constraints with high probability and provide a lower bound on the achieved objective reward. We empirically find the returned policies to achieve near-optimal rewards while enjoying an order of magnitude reduction in problem size and execution time.
Smart Air Quality Monitoring for Automotive Workshop Environments
Authors: Kauan Divino Pouso Mariano, Fabrycio Leite Nakano Almada, Maykon Adriell Dutra
Date: 2024-10-05 00:00:00

Air quality monitoring in automotive workshops is crucial for occupational health and regulatory compliance. This study presents the development of an environmental monitoring system based on Internet of Things (IoT) and Artificial Intelligence (AI) technologies. DHT-11 and MQ-135 sensors were employed to measure temperature, humidity, and toxic gas concentrations, with real-time data transmission to the ThingSpeak platform via the MQTT protocol. Machine learning algorithms, including Linear Regression, Decision Trees, and SVM, were applied to analyze the data and compute an air salubrity index based on Gaussian functions. The system proved effective in detecting pollutant peaks and issuing automatic alerts, significantly improving worker health and safety. Workshops that implemented the system reported greater regulatory compliance and reduced occupational risks. The study concludes that the combination of IoT and AI provides an efficient and replicable solution for environmental monitoring in industrial settings.
A Machine Learning-Based Reference Governor for Nonlinear Systems With Application to Automotive Fuel Cells
Authors: Mostafaali Ayubirad, Hamid R. Ossareh
Date: 2024-10-04 00:00:00

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.
A Generic Observer Design for Inertial Navigation Systems Using an LTV Framework
Authors: Sifeddine Benahmed, Soulaimane Berkane, Tarek Hamel
Date: 2024-10-04 00:00:00
Tasks: Pose Estimation, Position

This paper addresses the problem of accurate pose estimation-position, velocity, and orientation-of a rigid body using an Inertial Measurement Unit (IMU) in combination with generic exteroceptive measurements. By reformulating the vehicle's dynamics and measurement models within a linear time-varying (LTV) framework, we enable the application of a linear Kalman filter, significantly simplifying observer design for inertial navigation systems (INS). A key strength of this approach lies in its generality: rather than relying on specific measurement modalities, our framework accommodates a broad class of exteroceptive measurements. To illustrate its effectiveness, we conduct a uniform observability (UO) analysis for two fundamental benchmark cases-GPS-aided INS and landmark-aided INS-deriving sufficient conditions that guarantee the global uniform exponential stability of the proposed filter. Simulations for both applications confirm the versatility and robustness of our approach.
HiL Demonstration of Online Battery Capacity and Impedance Estimation with Minimal a Priori Parametrization Effort
Authors: Camiel J. J. Beckers, Feye S. J. Hoekstra, Frank Willems
Date: 2024-10-04 00:00:00

Uncertainty in the aging of batteries in battery electric vehicles impacts both the daily driving range as well as the expected economic lifetime. This paper presents a method to determine online the capacity and internal resistance of a battery cell based on real-world data. The method, based on a Joint Extended Kalman Filter combined with Recursive Least Squares, is computationally efficient and does not a priori require a fully characterized cell model. Offline simulation of the algorithm on data from differently aged cells shows convergence of the algorithm and indicates that capacity and resistance follow the expected trends. Furthermore, the algorithm is tested online on a Hardware-in-the-Loop setup to demonstrate real-time parameter updates in a realistic driving scenario.
Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation
Authors: Mohammad Mirtaba, Parham Oveissi, Juan Augusto Paredes Salaza, Ankit Goel
Date: 2024-10-04 00:00:00

This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based on Simulink for performance verification. Finally, the resulting gains are implemented in a physical quadrotor and the results for waypoint and trajectory tracking are reported in this paper. The proposed control approach is the continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm, which is simpler to implement within continuous-time simulation environments and whose performance does not depend on appropriate sampling time choice.
Strategic Utilization of Cellular Operator Energy Storages for Smart Grid Frequency Regulation
Authors: Narges Gholipoor, Farid Hamzeh Aghdam, Mehdi Rasti
Date: 2024-10-04 00:00:00
Tasks: energy management

The innovative use of cellular operator energy storage enhances smart grid resilience and efficiency. Traditionally used to ensure uninterrupted operation of cellular base stations (BSs) during grid outages, these storages can now dynamically participate in the energy flexibility market. This dual utilization enhances the economic viability of BS storage systems and supports sustainable energy management. In this paper, we explore the potential of BS storages for supporting grid ancillary services by allocating a portion of their capacity while ensuring Ultra Reliable Low Latency (URLLC) requirements, such as meeting delay and reliability requirements. This includes feeding BS stored energy back into the grid during high-demand periods or powering BSs to regulate grid frequency. We investigate the impacts of URLLC requirements on grid frequency regulation, formulating a joint resource allocation problem. This problem maximizes total revenues of cellular networks, considering both the total sum rate in the communication network and BS storages participation in frequency regulation, while considering battery aging and cycling constraints. Simulation results show that a network with 1500 BSs can increase power vacancy compensation from 31% to 46% by reducing reliability from 10^(-8) to 10^(-3). For a power vacancy of -30 MW, this varies from 9.3 MW to 13.5 MW, exceeding a wind turbines capacity.
A large synthetic dataset for machine learning applications in power transmission grids
Authors: Marc Gillioz, Guillaume Dubuis, Philippe Jacquod
Date: 2024-10-04 00:00:00

With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript presents a large synthetic dataset of power injections in an electric transmission grid model of continental Europe, and describes the algorithm developed for its generation. The method allows one to generate arbitrarily large time series from the knowledge of the grid -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data.
Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty
Authors: Jennie Couchman, Orestis Kaparounakis, Chatura Samarakoon, Phillip Stanley-Marbell
Date: 2024-10-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of ICA, the effect of measurement uncertainty on the artifact removal process has not been thoroughly investigated. We first characterize the measurement uncertainty distribution of a common ADC and show that it quantitatively conforms to a Gaussian distribution. We then evaluate the effect of measurement uncertainty on the artifact identification process through several computer simulations. These computer simulations evaluate the performance of two different ICA algorithms, FastICA and Infomax, in removing eyeblink artifacts from five different electrode configurations with varying levels of measurement uncertainty. FastICA and Infomax show similar performance in identifying the eyeblink artifacts for a given uncertainty level and electrode configuration. We quantify the correlation performance degradation with respect to SNR and show that in general, an SNR of greater than 15 dB results in less than a 5% degradation in performance. The biggest difference in performance between the two algorithms is in their execution time. FastICA's execution time is dependent on the amount of measurement uncertainty, with a 50% to 85% reduction in execution time over an SNR range of 20 dB. This contrasts with Infomax's execution time, which is unaffected by measurement uncertainty.
Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate
Authors: Jihun Kim, Javad Lavaei
Date: 2024-10-04 00:00:00

This paper is concerned with the online bandit nonlinear control, which aims to learn the best stabilizing controller from a pool of stabilizing and destabilizing controllers of unknown types for a given nonlinear dynamical system. We develop an algorithm, named Dynamic Batch length and Adaptive learning Rate (DBAR), and study its stability and regret. Unlike the existing Exp3 algorithm requiring an exponentially stabilizing controller, DBAR only needs a significantly weaker notion of controller stability, in which case substantial time may be required to certify the system stability. Dynamic batch length in DBAR effectively addresses this issue and enables the system to attain asymptotic stability, where the algorithm behaves as if there were no destabilizing controllers. Moreover, adaptive learning rate in DBAR only uses the state norm information to achieve a tight regret bound even when none of the stabilizing controllers in the pool are exponentially stabilizing.
Optimized Topology Control for IoT Networks using Graph-based Localization
Authors: Indrakshi Dey, Nicola Marchetti
Date: 2024-10-04 00:00:00

The key research question we are addressing in this paper, is how local distance information can be integrated into the global structure determination, in the form of network graphs realization for IoT networks. IoT networks will be pervading every walk of life over the next few years with the aim of improving quality of life and enhancing surrounding living conditions, while balancing available resources, like energy and computational power. As we deal with massive number of heterogeneous devices contributing to each IoT network, it is of paramount importance that the IoT network topology can be designed and controlled in such a way that coverage and throughput can be maximized using a minimum number of devices, while tackling challenges like poor link quality and interference. We tackle the above-mentioned problem of topology design and control through our designed graph-realization concept. End-nodes and gateways are identified and placed within neighborhood sub-graphs and their own coordinate system, which are stitched together to form the global graph. The stitching is done in a way that transmit power and information rate are optimized while reducing error probability.
Analyzing Fitts' Law using Offline and Online Optimal Control with Motor Noise
Authors: Riley Bridges, Ethan Parham, Jing Shuang Li
Date: 2024-10-03 00:00:00
Tasks: Model Predictive Control

The cause of the speed-accuracy tradeoff (typically quantified via Fitts' Law) is a debated topic of interest in motor neuroscience, and is commonly studied using tools from control theory. Two prominent theories involve the presence of signal dependent motor noise and planning variability -- these factors are generally incorporated separately. In this work, we study how well the simultaneous presence of both factors explains the speed-accuracy tradeoff. A human arm reaching model is developed with bio-realistic signal dependent motor noise, and a Gaussian noise model is used to deterministically approximate the motor noise. Both offline trajectory optimization and online model predictive control are used to simulate the planning and execution of several different reaching tasks with varying target sizes and movement durations. These reaching trajectories are then compared to experimental human reaching data, revealing that both models produce behavior consistent with humans, and the speed-accuracy tradeoff is present in both online and offline control. These results suggest the speed-accuracy tradeoff is likely caused by a combination of these two factors, and also that it plays a role in both offline and online computation.
Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics
Authors: Lyes Smaili, Zhiqi Tang, Soulaimane Berkane, Tarek Hamel
Date: 2024-10-03 00:00:00
Tasks: Position, Robot Navigation

This paper addresses the problem of autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics by proposing a Dissipative Avoidance Feedback (DAF). Compared to the Artificial Potential Field (APF), which primarily uses repulsive forces based on position, DAF employs a dissipative feedback mechanism that accounts for both position and velocity, contributing to smoother and more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by using the robot's state and local obstacle distance information. We show that the controller guarantees safe navigation in generic $n$-dimensional environments and that all undesired $\omega$-limit points are unstable under certain controlled curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces. Simulations in 2D and 3D environments are conducted to validate the theoretical results and to showcase the effectiveness of our approach.
Toward Neuronal Implementations of Delayed Optimal Control
Authors: Jing Shuang Li
Date: 2024-10-03 00:00:00

Animal sensorimotor behavior is frequently modeled using optimal controllers. However, it is unclear how the neural circuits within the animal's nervous system implement optimal controller-like behavior. In this work, we study the question of implementing a delayed linear quadratic regulator with linear dynamical "neurons" on a muscle model. We show that for any second-order controller, there are three minimal neural circuit configurations that implement the same controller. Furthermore, the firing rate characteristics of each circuit can vary drastically, even as the overall controller behavior is preserved. Along the way, we introduce concepts that bridge controller realizations to neural implementations that are compatible with known neuronal delay structures.
Automated Music Therapy for Anxiety and Depression Management in Older People (AMITY)
Authors: Malik Faizan, P. J. White, Indrakshi Dey
Date: 2024-10-03 00:00:00
Tasks: Management

The onset of old age brings physiological and mental changes, with anxiety and depression being common mental disorders that can trigger other health issues and reduce lifespan. However, due to a global shortage of mental health professionals, combined with a growing population and limited awareness, these disorders often go undiagnosed. Music therapy offers a reliable method to address psychological, emotional, and cognitive needs. This paper presents an approach that monitors anxiety and depression symptoms in real time using low-complexity body sensors, followed by automated personalised music therapy, reducing the dependence on therapists and improving mental health care accessibility.
Cross-Domain Comparative Analysis of Digital Twins and Universalised Solutions
Authors: Guanyu Xiong, Yan Gao, Haijiang Li
Date: 2024-10-03 00:00:00

Digitalisation is one of the main drivers of most economic sectors nowadays and the digital twin, as a reification of digitalisation for complex systems has attracted much attention from both academics and industry. There have been studies focusing on digital twins in a specific sector while there are few exercising insightful comparisons of digital twins from different domains. Considering the digital twinning is a cross-domain transformation, it is beneficial to establish the principles of universality and variation that can explain similarities and differences in any digital twins. This paper first delivers a comparative analysis of digital twins in five domains through a six-dimensional characterisation framework. Then, by departing from the correlations among the domain-specific DT development, a cross-domain Digital Twin Platform-as-a-Service (DT-PaaS) is proposed to universalise the common process, tools and applications, meanwhile being inclusive of variations of every digital twin instance. As a centralised data, modeling and service platform, it is expected to break the barriers between domains by enabling the cross-domain digital twin data sharing, interoperability and development synergy and tackle some complex global challenges such as climate challenge, net zero, pandemics, etc.
Equivalence between Geometric Frequency and Lagrange Derivative
Authors: Federico Milano
Date: 2024-10-03 00:00:00

The paper shows the equivalence between the geometric frequency of an electric quantity, namely, voltage and current, and the Lagrange derivative of a stream-line of a fluid. The geometric frequency is a concept recently proposed by the author and is a generalization of the instantaneous frequency, a quantity that is particularly important for the analysis and the control of electric power systems. On the other hand, the Lagrange derivative is mostly utilized in fluid dynamics and helps decomposing the time derivative into various components. The paper shows how these components relate to the elements of the geometric frequency. The paper also shows, through a variety of numerical examples, how the decomposition of the Lagrange derivative helps identifying the distortion of the waveform of a measured electric quantity and how this information can be utilized to classify system operating conditions.
Physics-Constrained Taylor Neural Networks for Learning and Control of Dynamical Systems
Authors: Nam T. Nguyen, Juan C. Tique
Date: 2024-10-03 00:00:00

Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generalize well. In this paper, we propose a novel method for system identification by integrating a neural network as the first-order derivative of a Taylor series expansion instead of learning a dynamical function directly. This approach, called Monotonic Taylor Neural Networks (MTNN), aims to ensure monotonic properties of dynamical systems by constraining the conditions for the output of the neural networks model to be either always non-positive or non-negative. These conditions are constructed in two ways: by designing a new neural network architecture or by regularizing the loss function for training. The proposed method demonstrates better performance compared to methods without constraints on the monotonic properties of the systems when tested with experimental data from two real-world systems, including HVAC and TCLab. Furthermore, MTNN shows good performance in an actual control application when using a model predictive controller for a nonlinear MIMO system, illustrating the practical applications of this method.