pwc-archive/papers-with-abstracts huggingface dataset:
Constraint Removal for MPC with Performance Preservation and a Hyperthermia Cancer Treatment Case Study
Authors: S. A. N. Nouwens, B. de Jager, M. M. Paulides, W. P. M. H. Heemels
Date: 2024-10-23 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) is an optimization-based control strategy with broad industrial adoption. Unfortunately, the required computation time to solve the receding-horizon MPC optimization problem can become prohibitively large for many applications with a large number of state constraints. This large number of state constraints can, for instance, originate from spatially discretizing a partial differential equation of which the solution has to satisfy constraints over the full spatial domain. This is particularly the case in MPC for RF-based hyperthermia cancer treatments, which forms a strong motivation for this study. To address this problem, we propose a novel constraint-adaptive MPC framework for linear discrete-time systems. In this framework, we select at each time-step a subset of the state constraints that are included in the optimization problem, thereby reducing the online computational burden. Critically, our framework guarantees the same closed-loop performance, recursive feasibility, and constraint satisfaction properties as the original (non-reduced) MPC scheme. We achieve this result by efficiently exploiting reachable set computations and the MPC cost function. We will demonstrate our novel method using a hyperthermia cancer treatment case study showing a two-orders of magnitude improvement in computation time, with identical closed-loop performance as the original (non-reduced) MPC scheme.
Exploiting Data Centres and Local Energy Communities Synergies for Market Participation
Authors: Ángel Paredes, Yihong Zhou, Chaimaa Essayeh, José A. Aguado, Thomas Morstyn
Date: 2024-10-23 00:00:00
Tasks: energy management, Management, Scheduling

The evolving energy landscape has propelled energy communities to the forefront of modern energy management. However, existing research has yet to explore the potential synergies between data centres and energy communities, necessitating an assessment on their collective capabilities for cost efficiency, waste heat optimisation, and market participation. This paper presents a mixed integer linear programming model to assess the collaborative performance of energy communities, data centres and energy markets. The evaluation focuses on the efficient use of waste heat and the flexibility of job scheduling while minimising system energy costs and maintaining quality of service requirements for data centres. Our results, based on realistic profiles of an energy community and a data centre, showcase significant benefits of these synergies, with a 38% reduction in operating costs and an 87% decrease in heat demand.
Risk-sensitive Affine Control Synthesis for Stationary LTI Systems
Authors: Yang Hu, Shahriar Talebi, Na Li
Date: 2024-10-23 00:00:00

To address deviations from expected performance in stochastic systems, we propose a risk-sensitive control synthesis method to minimize certain risk measures over the limiting stationary distribution. Specifically, we extend Worst-case Conditional Value-at-Risk (W-CVaR) optimization for Linear Time-invariant (LTI) systems to handle nonzero-mean noise and affine controllers, using only the first and second moments of noise, which enhances robustness against model uncertainty. Highlighting the strong coupling between the linear and bias terms of the controller, we reformulate the synthesis problem as a Bilinear Matrix Inequality (BMI), and propose an alternating optimization algorithm with guaranteed convergence. Finally, we demonstrate the numerical performance of our approach in two representative settings, which shows that the proposed algorithm successfully synthesizes risk-sensitive controllers that outperform the na\"ive LQR baseline.
Electric Grid Topology and Admittance Estimation: Quantifying Phasor-based Measurement Requirements
Authors: Norak Rin, Iman Shames, Ian R. Petersen, Elizabeth L. Ratnam
Date: 2024-10-23 00:00:00

In this paper, we quantify voltage and current phasor-based measurement requirements for the unique estimation of the electric grid topology and admittance parameters. Our approach is underpinned by the concept of a rigidity matrix that has been extensively studied in graph rigidity theory. Specifically, we show that the rank of the rigidity matrix is the same as that of a voltage coefficient matrix in a corresponding electric power system. Accordingly, we show that there is a minimum number of measurements required to uniquely estimate the admittance matrix and corresponding grid topology. By means of a numerical example on the IEEE 4-node radial network, we demonstrate that our approach is suitable for applications in electric power grids.
Invisible Manipulation Deep Reinforcement Learning Enhanced Stealthy Attacks on Battery Energy Management Systems
Authors: Qi Xiao, Lidong Song, Jongha Woo, Rongxing Hu, Bei Xu, Kai Ye, Ning Lu
Date: 2024-10-22 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, State Estimation, valid

This paper introduces "invisible manipulation," an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system toward a predetermined operational state without detection. Using the battery energy management system (BEMS) as a case study, we employ deep reinforcement learning (DRL) to generate synthetic measurements, such as battery voltage and current, that align closely with actual measurements. These synthetic measurements, falling within the acceptable error margin of residual-based bad data detection algorithm provided by state estimation, can evade detection and mislead Extended Kalman-filter-based State of Charge estimation. Subsequently, considering the deceptive data as valid inputs, the BEMS will operate the BESS towards the attacker desired operational states when the targeted time period come. The use of the DRL-based scheme allows us to covert an online optimization problem into an offline training process, thereby alleviating the computational burden for real-time implementation. Comprehensive testing on a high-fidelity microgrid real-time simulation testbed validates the effectiveness and adaptability of the proposed methods in achieving different attack objectives.
Preserving Privacy in Cloud-based Data-Driven Stabilization
Authors: Teimour Hosseinalizadeh, Nima Monshizadeh
Date: 2024-10-22 00:00:00
Tasks: Privacy Preserving

In the recent years, we have observed three significant trends in control systems: a renewed interest in data-driven control design, the abundance of cloud computational services and the importance of preserving privacy for the system under control. Motivated by these factors, this work investigates privacy-preserving outsourcing for the design of a stabilizing controller for unknown linear time-invariant systems.The main objective of this research is to preserve the privacy for the system dynamics by designing an outsourcing mechanism. To achieve this goal, we propose a scheme that combines transformation-based techniques and robust data-driven control design methods. The scheme preserves the privacy of both the open-loop and closed-loop system matrices while stabilizing the system under control.The scheme is applicable to both data with and without disturbance and is lightweight in terms of computational overhead. Numerical investigations for a case study demonstrate the impacts of our mechanism and its role in hindering malicious adversaries from achieving their goals.
Empowering the Grid: Decentralized Autonomous Control for Effective Utilization and Resilience
Authors: Sai Pushpak Nandanoori, Alok Kumar Bharati, Subhrajit Sinha, Soumya Kundu, Veronica Adetola, Kevin Schneider
Date: 2024-10-22 00:00:00

With the emergence of low-inertia microgrids powered by inverter-based generation, there remains a concern about the operational resilience of these systems. Grid-forming inverters (GFMs), enabled by various device-level (primary) and system-level (secondary) control methods, are poised to play a significant role in achieving certain operational objectives, such as the effective utilization of clean energy resources while maintaining stability. However, despite the recent advances in GFMs, there is a lack of suitable controls that can ascertain resilience-constrained operations, like maintaining critical operational safety limits during transients under various cyber-physical disruptions. In this work, we develop decentralized autonomous controllers (DACs) that enforce resilience-constrained operation via local, minimally invasive adjustments (e.g., changes in set-points) while co-existing within the hierarchy of existing (primary and secondary) controls. The DACs work autonomously by sensing only local GFM measurements and act only when operational resilience constraints are violated. The proposed DAC scheme is computationally efficient (only algebraic computations), which enables fast, real-time execution and demonstrates the efficacy of the proposed control framework on GridLAB-D-HELICS-based control-grid co-simulations on the IEEE 123-node networked microgrid. Finally, we show how the developed DACs empower the grid by utilizing the available resources entirely to ensure resilience (maintain frequency safe limits).
A Hybrid Simulation of DNN-based Gray Box Models
Authors: Aayushya Agarwal, Yihan Ruan, Larry Pileggi
Date: 2024-10-22 00:00:00

Simulation is vital for engineering disciplines, as it enables the prediction and design of physical systems. However, the computational challenges inherent to large-scale simulations often arise from complex device models featuring high degrees of nonlinearities or hidden physical behaviors not captured by first principles. Gray-box models combine deep neural networks (DNNs) with physics-based models to address the computational challenges in modeling physical systems. A well-crafted gray box model capitalizes on the interpretability and accuracy of a physical model while incorporating DNNs to capture hidden physical behaviors and mitigate computational load associated with highly nonlinear components. Previously, gray box models have been constructed by defining an explicit combination of physics-based and DNN models to represent the behavior of sub-systems; however this cannot represent the coupled interactions within physical systems. We explore an implicit gray box model, where both DNNs and physical equations share a common set of state-variables. While this approach captures coupled interactions at the boundary of DNN and physics-based models, simulating the implicit gray box model remains an open-ended problem. In this work, we introduce a new hybrid simulation that integrates DNNs into the numerical solvers of simulation engines to fully simulate implicit gray box models of large physical systems. This is accomplished by backpropagating through the DNN to calculate Jacobian values during each iteration of the numerical method. The hybrid simulation improves the accuracy and runtime compared to physics-based simulation and enables reusable DNN models with lower data requirements. We explore the advantages of this approach as compared to physics-based, black box, and other gray box methods for simulating the steady-state and transient behavior of power systems.
Optimal gait design for nonlinear soft robotic crawlers
Authors: Yenan Shen, Naomi Ehrich Leonard, Bassam Bamieh, Juncal Arbelaiz
Date: 2024-10-22 00:00:00
Tasks: Friction

Soft robots offer a frontier in robotics with enormous potential for safe human-robot interaction and agility in uncertain environments. A stepping stone towards unlocking their potential is a control theory tailored to soft robotics, including a principled framework for gait design. We analyze the problem of optimal gait design for a soft crawling body - the crawler. The crawler is an elastic body with the control signal defined as actuation forces between segments of the body. We consider the simplest such crawler: a two-segmented body with a passive mechanical connection modeling the viscoelastic body dynamics and a symmetric control force modeling actuation between the two body segments. The model accounts for the nonlinear asymmetric friction with the ground, which together with the symmetric actuation forces enable the crawler's locomotion. Using a describing-function analysis, we show that when the body is forced sinusoidally, the optimal actuator contraction frequency corresponds to the body's natural frequency when operating with only passive dynamics. We then use the framework of Optimal Periodic Control (OPC) to design optimal force cycles of arbitrary waveform and the corresponding crawling gaits. We provide a hill-climbing algorithm to solve the OPC problem numerically. Our proposed methods and results inform the design of optimal forcing and gaits for more complex and multi-segmented crawling soft bodies.
On Optimal Battery Sizing for Electric Vehicles
Authors: Felix Wieberneit, Emanuele Crisostomi, Anthony Quinn, Homayoun Hamedmoghadam, Pietro Ferraro, Robert Shorten
Date: 2024-10-22 00:00:00

In this paper, we introduce a quantitative framework to optimize electric vehicle (EV) battery capacities, considering two criteria: upfront vehicle cost and charging inconvenience cost. For this purpose, we (1) develop a comprehensive model for charging inconvenience costs, incorporating both charging time and detours, improving on existing studies, (2) show, through extensive simulations and analytical models, how charging inconvenience cost is affected by different battery capacity and charging infrastructure configurations, (3) introduce an optimisation framework to determine optimal battery capacities based on charging inconvenience and vehicle cost, and (4) show that optimal battery capacities can be influenced by strategic investments in charging infrastructure and tax/incentive policies. The proposed framework provides actionable insights into the sustainable design of EV systems, supporting the development of cost-effective and convenient electric mobility solutions.
Electrode SOC and SOH estimation with electrode-level ECMs
Authors: Iker Lopetegi, Sergio Fernandez, Gregory L. Plett, M. Scott Trimboli, Unai Iraola
Date: 2024-10-22 00:00:00
Tasks: Management, Prognosis, State Estimation

Being able to predict battery internal states that are related to battery degradation is a key aspect to improve battery lifetime and performance, enhancing cleaner electric transportation and energy generation. However, most present battery management systems (BMSs) use equivalent-circuit models (ECMs) for state of charge (SOC) and state of health (SOH) estimation. These models are not able to predict these aging-related variables, and therefore, they cannot be used to limit battery degradation. In this paper, we propose a method for electrode-level SOC (eSOC) and electrode-level SOH (eSOH) estimation using an electrode-level ECM (eECM). The method can produce estimates of the states of lithiation (SOL) of both electrodes and update the eSOH parameters to maintain estimation accuracy through the lifetime of the battery. Furthermore, the eSOH parameter estimates are used to obtain degradation mode information, which could be used to improve state estimation, health diagnosis and prognosis. The method was validated in simulation and experimentally.
Iterative Cut-Based PWA Approximation of Multi-Dimensional Nonlinear Systems
Authors: Leila Gharavi, Bart De Schutter, Simone Baldi
Date: 2024-10-22 00:00:00

PieceWise Affine (PWA) approximations for nonlinear functions have been extensively used for tractable, computationally efficient control of nonlinear systems. However, reaching a desired approximation accuracy without prior information about the behavior of the nonlinear systems remains a challenge in the function approximation and control literature. As the name suggests, PWA approximation aims at approximating a nonlinear function or system by dividing the domain into multiple subregions where the nonlinear function or dynamics is approximated locally by an affine function also called local mode. Without prior knowledge of the form of the nonlinearity, the required number of modes, the locations of the subregions, and the local approximations need to be optimized simultaneously, which becomes highly complex for large-scale systems with multi-dimensional nonlinear functions. This paper introduces a novel approach for PWA approximation of multi-dimensional nonlinear systems, utilizing a hinging hyperplane formalism for cut-based partitioning of the domain. The complexity of the PWA approximation is iteratively increased until reaching the desired accuracy level. Further, the tractable cut definitions allow for different forms of subregions, as well as the ability to impose continuity constraints on the PWA approximation. The methodology is explained via multiple examples and its performance is compared to two existing approaches through case studies, showcasing its efficacy.
Cooperative Trajectory Planning: Principles for Human-Machine System Design on Trajectory Level
Authors: Julian Schneider, Balint Varga, Sören Hohmann
Date: 2024-10-22 00:00:00
Tasks: Trajectory Planning, valid

This paper explores cooperative trajectory planning approaches within the context of human-machine shared control. In shared control research, it is typically assumed that the human and the automation use the same reference trajectory to stabilize the coupled system. However, this assumption is often incorrect, as they usually follow different trajectories, causing control conflicts at the action level that have not been widely researched. To address this, it is logical to extend shared control concepts to include human-machine interaction at the trajectory-level before action execution, resulting in a unified reference trajectory for both human and automation. This paper begins with a literature overview on approaches of cooperative trajectory planning. It then presents an approach of finding a joint trajectory by modelling cooperative trajectory planning as an agreement process. A generally valid system structure is proposed for this purpose. Finally, it proposes concepts to implement cooperative trajectory planning as an agreement process.
Nature-inspired dynamic control for pursuit-evasion of robots
Authors: Panpan Zhou, Sirui Li, Benyun Zhao, Bo Wahlberg, Xiaoming Hu
Date: 2024-10-22 00:00:00

The pursuit-evasion problem is widespread in nature, engineering, and societal applications. It is commonly observed in nature that predators often exhibit faster speeds than their prey but have less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient strategies for both pursuit and evasion. In this paper, we provide a dynamic framework for the pursuit-evasion problem of unicycle systems, drawing inspiration from nature. First, we address the scenario involving one pursuer and one evader by proposing an Alert-Turn control strategy, which consists of two efficient ingredients: a sudden turning maneuver and an alert condition for starting and maintaining the maneuver. We present and analyze the escape and capture results at two levels: a lower level of a single run and a higher level with respect to parameters' changes. In addition, we provide a theorem with sufficient conditions for capture. The Alert-Turn strategy is then extended to more complex scenarios involving multiple pursuers and evaders by integrating aggregation control laws and a target-changing mechanism. By adjusting a 'selfish parameter', the aggregation control commands produce various escape patterns of evaders: cooperative mode, selfish mode, as well as their combinations. The influence of the selfish parameter is quantified, and the effects of the number of pursuers and the target-changing mechanism are explored from a statistical perspective. Our findings align closely with observations in nature. Finally, the proposed control strategies are validated through numerical simulations that replicate some chasing behaviors of animals in nature.
PEtra: A Flexible and Open-Source PE Loop Tracer for Polymer Thin-Film Transducers
Authors: Marc-Andre Wessner, Federico Villani, Sofia Papa, Kirill Keller, Laura Ferrari, Francesco Greco, Luca Benini, Christoph Leitner
Date: 2024-10-21 00:00:00

Accurate characterization of ferroelectric properties in polymer piezoelectrics is critical for optimizing the performance of flexible and wearable ultrasound transducers, such as screen-printed PVDF devices. Standard charge measurement techniques, like the Sawyer-Tower circuit, often fall short when applied to ferroelectric polymers due to low-frequency leakage. In this work, we present PEtra, an open-source and versatile piezoelectric loop tracer. PEtra employs a transimpedance amplifier (LMP7721, TI) to convert picoampere-level currents into measurable voltages, covering a frequency range of 0.1 Hz to 5 Hz for a gain setting of 10^7 V/A, and 0.1 Hz to 200 Hz for gain settings between 10^3 V/A to 10^6 V/A (10-fold increments). We demonstrate through simulations and experimental validations that PEtra achieves a sensitivity down to 2 pA, effectively addressing the limitations of traditional charge measurement methods. Compared to the Sawyer-Tower circuit, PEtra directly amplifies currents without the need for a reference capacitor. As a result, it is less susceptible to leakage and can operate at lower frequencies, improving measurement accuracy and reliability. PEtra's design is fully open source, offering researchers and engineers a versatile tool to drive advancements in flexible PVDF transducer technology.
Residues in Partial Fraction Decomposition Applied to Pole Sensitivity Analysis and Root Locus Construction
Authors: Davide Tebaldi, Roberto Zanasi
Date: 2024-10-21 00:00:00
Tasks: Sensitivity

The applications of the partial fraction decomposition in control and systems engineering are several. In this letter, we propose a new interpretation of residues in the partial fraction decomposition, which is employed for the following purposes: to address the pole sensitivity problem, namely to study the speed of variation of the system poles when the control parameter changes and when the system is subject to parameters variations, as well as to propose a new algorithm for the construction of the root locus. The new algorithm is proven to be more efficient in terms of execution time than the dedicated MATLAB function, while providing the same output results.