pwc-archive/papers-with-abstracts huggingface dataset:
Learning Robust Policies for Uncertain Parametric Markov Decision Processes
Authors: Luke Rickard, Alessandro Abate, Kostas Margellos
Date: 2023-12-11 00:00:00

Synthesising verifiably correct controllers for dynamical systems is crucial for safety-critical problems. To achieve this, it is important to account for uncertainty in a robust manner, while at the same time it is often of interest to avoid being overly conservative with the view of achieving a better cost. We propose a method for verifiably safe policy synthesis for a class of finite state models, under the presence of structural uncertainty. In particular, we consider uncertain parametric Markov decision processes (upMDPs), a special class of Markov decision processes, with parameterised transition functions, where such parameters are drawn from a (potentially) unknown distribution. Our framework leverages recent advancements in the so-called scenario approach theory, where we represent the uncertainty by means of scenarios, and provide guarantees on synthesised policies satisfying probabilistic computation tree logic (PCTL) formulae. We consider several common benchmarks/problems and compare our work to recent developments for verifying upMDPs.
Fast Whole-Body Strain Regulation in Continuum Robots
Authors: Lekan Molu
Date: 2023-12-10 00:00:00

We propose reaching steps towards the real-time strain control of multiphysics, multiscale continuum soft robots. To study this problem fundamentally, we ground ourselves in a model-based control setting enabled by mathematically precise dynamics of a soft robot prototype. Poised to integrate, rather than reject, inherent mechanical nonlinearities for embodied compliance, we first separate the original robot dynamics into separate subdynamics -- aided by a perturbing time-scale separation parameter. Second, we prescribe a set of stabilizing nonlinear backstepping controllers for regulating the resulting subsystems' strain dynamics. Third, we study the interconnected singularly perturbed system by analyzing and establishing its stability. Fourth, our theories are backed up by fast numerical results on a single arm of the Octopus robot arm. We demonstrate strain regulation to equilibrium, in a significantly reduced time, of the whole-body reduced-order dynamics of an infinite degrees-of-freedom soft robot. This paper communicates our thinking within the backdrop of embodied intelligence: it informs our conceptualization, formulation, computational setup, and yields improved control performance for infinite degrees-of-freedom soft robots.
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models
Authors: Anna Derington, Hagen Wierstorf, Ali Özkil, Florian Eyben, Felix Burkhardt, Björn W. Schuller
Date: 2023-12-11 00:00:00
Tasks: Emotion Recognition, Fairness, Speech Emotion Recognition

Machine learning models for speech emotion recognition (SER) can be trained for different tasks and are usually evaluated based on a few available datasets per task. Tasks could include arousal, valence, dominance, emotional categories, or tone of voice. Those models are mainly evaluated in terms of correlation or recall, and always show some errors in their predictions. The errors manifest themselves in model behaviour, which can be very different along different dimensions even if the same recall or correlation is achieved by the model. This paper introduces a testing framework to investigate behaviour of speech emotion recognition models, by requiring different metrics to reach a certain threshold in order to pass a test. The test metrics can be grouped in terms of correctness, fairness, and robustness. It also provides a method for automatically specifying test thresholds for fairness tests, based on the datasets used, and recommendations on how to select the remaining test thresholds. We evaluated a xLSTM-based and nine transformer-based acoustic foundation models against a convolutional baseline model, testing their performance on arousal, valence, dominance, and emotional category classification. The test results highlight, that models with high correlation or recall might rely on shortcuts -- such as text sentiment --, and differ in terms of fairness.
Data-Driven Bifurcation Analysis via Learning of Homeomorphism
Authors: Wentao Tang
Date: 2023-12-11 00:00:00

This work proposes a data-driven approach for bifurcation analysis in nonlinear systems when the governing differential equations are not available. Specifically, regularized regression with barrier terms is used to learn a homeomorphism that transforms the underlying system to a reference linear dynamics -- either an explicit reference model with desired qualitative behavior, or Koopman eigenfunctions that are identified from some system data under a reference parameter value. When such a homeomorphism fails to be constructed with low error, bifurcation phenomenon is detected. A case study is performed on a planar numerical example where a pitchfork bifurcation exists.
Implementing hosting capacity analysis in distribution networks: Practical considerations, advancements and future directions
Authors: U. Singh, A. Al-Durra
Date: 2023-12-11 00:00:00
Tasks: Benchmarking, Capacity Estimation, Uncertainty Quantification

Hosting capacity analysis is essential for effective integration of distributed energy resources into distribution systems. This paper discusses hosting capacity analysis with emphasis on various aspects affecting the process. This paper addresses key research gaps, aiming to improve the accuracy, scalability, and practicality of hosting capacity estimation. Standardized methodologies are highlighted as a need to ensure consistent hosting capacity estimation across distribution systems. Validation and benchmarking frameworks are emphasized for evaluating various estimation approaches. The potential of data-driven techniques, is also discussed for enhancing hosting capacity analysis. Real-time and dynamic estimation techniques which account for changing system conditions are explored, as well as the integration of hosting capacity analysis with distribution planning and operations. Uncertainty quantification and risk assessment in hosting capacity analysis are identified as crucial areas, advocating for probabilistic and stochastic modelling. This study also emphasizes the importance of considering multiple DER interactions and synergies in hosting capacity analysis, enabling a comprehensive understanding of system performance. This survey aims to serve as a valuable resource for researchers, practitioners, and policymakers, providing insights into advancements made and guiding future research efforts to address identified gaps.
Control Strategy for Virtual Synchronous Generator Based on Y-Z Source Inverter in Islanded Grids
Authors: Shirin Besati, Ali Mosallanejad, Madhav Manjrekar
Date: 2023-12-11 00:00:00

The Virtual Synchronous Machine (VSM) concept stands as a strategy for the seamless integration of renewable energy sources into the grid. In scenarios where symmetrical and sinusoidal operating conditions are lacking, VSMs demonstrate multifaceted capabilities. They can operate as power sources especially in islanded-systems, adeptly address harmonic distortions and imbalances, offer load power compensation, and elevate voltage quality at the grid. Even so, dead time variations, discontinuous input current, and non-single-power-stage configuration of conventional voltage/current source inverters and also, load variation can result in VSMs behaving as harmonic and imbalance absorbers. This paper utilizes a specific Y-Source Inverter (YZSI) for the first time as a solution to these challenges. Through integrating this innovative configuration with an exceptionally adaptable voltage gain and an enhanced multi-loop control strategy into an isolated power system accommodating diverse load profiles, it not only achieves high efficiency in power transfer but also enhances overall system performance quality. Furthermore, it contributes to extending the lifespan and preserving the quality of the input source. The controller is designed to regulate system frequency, voltage, and manage voltage and current for YZSI, delivering impressive performance even in non-ideal grid conditions. MATLAB/Simulink confirms the efficacy of the proposed YZSI VSG control strategy with a high accuracy achieved through a comprehensive examination of the internal component parameters.
Optimal Control of Fluid Dosing in Hemorrhage Resuscitation
Authors: Jacob Grant, Hossein Mirinejad
Date: 2023-12-11 00:00:00

Fluid resuscitation, also called fluid therapy, is commonly used in treatment of critical care patients. Of the variables inherent to fluid therapy treatment, fluid infusion types are well-studied, but volumetric optimization is not. Automated fluid resuscitation systems employ computational control algorithms along with physiological sensors and actuators to automatically adjust the fluid infusion dosage in hemorrhage scenarios. Most automated hemorrhage resuscitation control algorithms implemented to date are of empiric nature and not effective in the presence of clinical disturbances. This work presents preliminary results of a novel controller based on optimal control approach for automated fluid therapy systems. A receding horizon controller is designed to automatically adjust fluid infusion rate in hemorrhagic situations. The proposed control approach aims to address the inefficiency of current resuscitation control algorithms in finding the accurate fluid dosage adjustments. Performance of the proposed approach is compared against a proportional-integral-derivative (PID) controller for a simulated hemorrhage scenario.
Dissipativity-Based Decentralized Co-Design of Distributed Controllers and Communication Topologies for Vehicular Platoons
Authors: Shirantha Welikala, Zihao Song, Panos J. Antsaklis, Hai Lin
Date: 2023-12-11 00:00:00

Vehicular platoons provide an appealing option for future transportation systems. Most of the existing work on platoons separated the design of the controller and its communication topologies. However, it is beneficial to design both the platooning controller and the communication topology simultaneously, i.e., controller and topology co-design, especially in the cases of platoon splitting and merging. We are, therefore, motivated to propose a co-design framework for vehicular platoons that maintains both the compositionality of the controller and the string stability of the platoon, which enables the merging and splitting of the vehicles in a platoon. To this end, we first formulate the co-design problem as a centralized linear matrix inequality (LMI) problem and then decompose it using Sylvester's criterion to obtain a set of smaller decentralized LMI problems that can be solved sequentially at individual vehicles in the platoon. Moreover, in the formulated decentralized LMI problems, we encode a specifically derived local LMI to enforce the $L_2$ stability of the closed-loop platooning system, further implying the $L_2$ weak string stability of the vehicular platoon. Finally, to validate the proposed co-design method and its features in terms of merging/splitting, we provide an extensive collection of simulation results generated from a specifically developed simulation framework. Available in GitHub: HTTP://github.com/NDzsong2/Longitudinal-Vehicular-Platoon-Simulator.git that we have made publicly available.
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Authors: Christian Fiedler, Michael Herty, Sebastian Trimpe
Date: 2023-12-11 00:00:00

Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continuous-time theory is well-developed, few works have considered mean field limits for deterministic discrete-time systems, which are relevant for the analysis and control of large-scale discrete-time multiagent system. We prove existence results for the mean field limit of very general discrete-time control systems, for which we utilize kernel mean embeddings. These results are then applied in a typical optimal control setup, where we establish the mean field limit of the relaxed dynamic programming principle. Our results can serve as a rigorous foundation for many applications of mean field approaches for discrete-time dynamical systems.
Output contraction analysis of nonlinear systems
Authors: Hao Yin, Bayu Jayawardhana, Stephan Trenn
Date: 2023-12-11 00:00:00

This paper introduce the notion of output contraction that expands the contraction notion to the time-varying nonlinear systems with output. It pertains to the systems' property that any pair of outputs from the system converge to each other exponentially. This concept exhibits a more expansive nature when contrasted with another generalized contraction framework known as partial contraction. The first result establishes a connection between the output contraction of a time-varying system and the output exponential stability of its variational system. Subsequently, we derive a sufficient condition for achieving output contraction in time-varying systems by applying the output contraction Lyapunov criterion. Finally, we apply the results to analyze the output exponential stability of nonlinear time-invariant systems.
Stability Analysis of LQR-ANFIS Control Schemes on 2-degree-of-freedom Inverted Pendulum Systems
Authors: Shamanth S, Aditya Kumar Chari, Harshitha S
Date: 2023-12-11 00:00:00
Tasks: Position

The concepts of stability and balance represent many critical problems faced by engineering today. The inverted pendulum on a cart is one such non-linear, unstable, multivariate system whose goal is to determine a suitable control action given to the cart such that it stabilizes the pendulum in an upright vertical position. This paper therefore, aims to design and study a highly robust MISO control structure using Linear Quadratic Regulation, Fuzzy logic and Neural Networks called Two-Stage LQR-based-ANFIS (referred to as TS-LA) for the stabilization of Inverted Pendulums. The proposed controller is implemented on a Simulink model of the Inverted Pendulum constructed through relevant mathematical and state space modelling using Newtonian and Lagrangian mechanics. Applying external disturbances, transient parameters are obtained and are benchmarked against standard conventional controllers to perform comparative analysis and showcase its disturbance rejection capabilities.
Overcoming Cognitive Distraction and Measurement Noise: Strategies for Humans and Engineering Systems
Authors: Mehdi Delrobaei
Date: 2023-12-11 00:00:00

Cognitive distraction and measurement noise are two distinct factors that significantly impact the performance of humans and engineering systems. Cognitive distraction occurs when an individual's attention is diverted from a task, while measurement noise refers to the random variation that can occur in system measurements. Although humans and engineering systems employ different methods to overcome these obstacles, the ultimate goal is to achieve optimal performance. An intriguing question arises: what are the similarities and differences between using the term "noise" in engineering and cognitive psychology? Additionally, it is worthwhile to explore whether the human brain and engineering control systems use similar or different approaches to attenuate noise. While this article does not provide a definitive answer, it emphasizes the importance of addressing this question and encourages further investigation.
Robust and Decentralized Reinforcement Learning for UAV Path Planning in IoT Networks
Authors: Xueyuan Wang, M. Cenk Gursoy
Date: 2023-12-11 00:00:00

Unmanned aerial vehicle (UAV)-based networks and Internet of Things (IoT) are being considered as integral components of current and next-generation wireless networks. In particular, UAVs can provide IoT devices with seamless connectivity and high coverage and this can be accomplished with effective UAV path planning. In this article, we study robust and decentralized UAV path planning for data collection in IoT networks in the presence of other noncooperative UAVs and adversarial jamming attacks. We address three different practical scenarios, including single UAV path planning, UAV swarm path planning, and single UAV path planning in the presence of an intelligent mobile UAV jammer. We advocate a reinforcement learning framework for UAV path planning in these three scenarios under practical constraints. The simulation results demonstrate that with learning-based path planning, the UAVs can complete their missions with high success rates and data collection rates. In addition, the UAVs can adapt and execute different trajectories as a defensive measure against the intelligent jammer.
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
Authors: Patrick J. W. Koelewijn, Rajiv Sing, Peter Seiler, Roland Tóth
Date: 2023-12-11 00:00:00

In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a projection to a lower dimensional state-space. In step two, an LPV model is learned on the reduced-order state-space using a novel, efficient parameterization in terms of neural networks. The improved modeling accuracy of the method compared to an existing method is demonstrated by simulation examples.
Contraction analysis of time-varying DAE systems via auxiliary ODE systems
Authors: Hao Yin, Bayu Jayawardhana, Stephan Trenn
Date: 2023-12-11 00:00:00

This paper studies the contraction property of time-varying differential-algebraic equation (DAE) systems by embedding them to higher-dimension ordinary differential equation (ODE) systems. The first result pertains to the equivalence of the contraction of a DAE system and the uniform global exponential stability (UGES) of its variational DAE system. Such equivalence inherits the well-known property of contracting ODE systems on a specific manifold. Subsequently, we construct an auxiliary ODE system from a DAE system whose trajectories encapsulate those of the corresponding variational DAE system. Using the auxiliary ODE system, a sufficient condition for contraction of the time-varying DAE system is established by using matrix measure which allows us to estimate an lower bound on the parameters of the auxiliary system. Finally, we apply the results to analyze the stability of time-invariant DAE systems, and to design observers for time-varying ODE systems.
Adaptive Event-triggered Control For Strict-feedback Systems With Time-varying Parameters
Authors: Yan Tan, Liucang Wu, Wenqi Liu
Date: 2023-12-11 00:00:00
Tasks: parameter estimation

In this article, we develop a new adaptive event-triggered asymptotic control scheme for strict-feedback systems with fast time-varying parameters. To deal with time-varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the feedback path and one for the uncertain parameters in the input path. In particular, two sets of tuning functions are introduced to avoid over-parametrization. Additionally, an event-triggering mechanism is embedded in this adaptive control framework to reduce the data transmission from the controller to the actuator. We also introduce a soft sign function to handle the perturbations caused by sampling errors to achieve asymptotic stability and avoid the so-called parameter drift. The stability analysis shows that the closed-loop system is globally uniformly asymptotically stable and the Zeno behavior can be excluded. Simulation results verify the effectiveness and performance of the proposed adaptive scheme.
Predictive Reliability Assessment of Distribution Grids with Residential Distributed Energy Resources
Authors: Arun Kumar Karngala, Chanan Singh, Le Xie
Date: 2023-12-11 00:00:00

Distribution system end users are transforming from passive to active participants, marked by the push towards widespread adoption of edge-level Distributed Energy Resources (DERs). This paper addresses the challenges in distribution system planning arising from these dynamic changes. We introduce a bottom-up probabilistic approach that integrates these edge-level DERs into the reliability evaluation process. Our methodology leverages joint probability distributions to characterize and model the penetration of rooftop photovoltaic (PV) systems and energy storage across a distribution network at the individual residential level. Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level. To validate our approach, we applied this methodology to the RBTS test system across various adoption scenarios, effectively showcasing the capability of our proposed method in quantifying the variation in end-user reliability indices for each scenario within the distribution system.
Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach
Authors: Xuan He, Danny H. K. Tsang, Yize Chen
Date: 2023-12-11 00:00:00

Global climate challenge is demanding urgent actions for decarbonization, while electric power systems take the major roles in clean energy transition. Due to the existence of spatially and temporally dispersed renewable energy resources and the uneven distribution of carbon emission intensity throughout the grid, it is worth investigating future load planning and demand management to offset those generations with higher carbon emission rates. Such techniques include inter-region utilization of geographically shiftable resources and stochastic renewable energy. For instance, data center is considered to be a major carbon emission producer in the future due to increasing information load, while it holds the capability of geographical load balancing. In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources. This model decides the optimal locations for shiftable resources expansion along with power dispatch schedule. To accommodate future system operation patterns and a wide range of operating conditions, we incorporate 20-year fine-grained load and renewables scenarios for grid simulations of realistic sizes (e.g., up to 1888 buses). To tackle the computational challenges coming from the combinatorial nature of such large-scale planning problem, we develop a customized Monte Carlo Tree Search (MCTS) method, which can find reasonable solutions satisfying solution time limits. Besides, MCTS enables flexible time window settings and offline solution adjustments. Extensive simulations validate that our planning model can reduce more than 10\% carbon emission across all setups. Compared to off-the-shelf optimization solvers such as Gurobi, our method achieves up to 8.1X acceleration while the solution gaps are less than 1.5\% in large-scale cases.