pwc-archive/papers-with-abstracts huggingface dataset:
Two-Phase Switched Reluctance Motors: Optimal Magnet Placement and Drive System for Torque Density
Authors: Gholamreza Davarpanah, Sajjad Mohammadi, James L. Kirtley
Date: 2024-10-31 00:00:00

This paper focuses on designing new motors with high torque density, which is crucial for applications ranging from electric vehicles to robotics. We propose a double-teeth C-core switched reluctance motor with hybrid excitation, integrating permanent magnets and a novel drive technique to enhance motor torque density. We explore three magnet placement configurations to maximize torque. A common challenge with most self-starting methods used in two-phase SRMs is the generation of negative torque, which reduces the motor's torque density. Our adopted self-starting method minimizes negative torque, and we introduce a new drive strategy to control the switching on and off, effectively eliminating negative torque. Additionally, magnetic equivalent circuits are developed for the analytical design and theoretical analysis of all configurations. The SRMs under study are prototyped and tested, and their performances are evaluated in terms of torque-angle characteristics, current, and voltage. Both experimental and simulation results validate the effectiveness of the PM-assisted SRMs in enhancing torque density and efficiency.
Satellite Safe Margin: Fast solutions for Conjunction Analysis
Authors: Ricardo N. Ferreira, Marta Guimarães, Cláudia Soares
Date: 2024-10-31 00:00:00

The amount of debris in orbit has increased significantly over the years. With the recent growth of interest in space exploration, conjunction assessment has become a central issue. One important metric to evaluate conjunction risk is the miss distance. However, this metric does not intrinsically take into account uncertainty distributions. Some work has been developed to consider the uncertainty associated with the position of the orbiting objects, in particular, to know if these uncertainty distributions overlap (e.g., ellipsoids when considering Gaussian distributions). With this work, we present fast solutions to not only check if the ellipsoids overlap but to compute the distance between them, which we call margin. We present two fast solution methods for two different paradigms: when the best-known data from both objects can be centralized (e.g., debris-satellite conjunctions) and when the most precise covariances cannot be shared (conjunctions of satellites owned by different operators). Our methods are both accurate and fast, being able to process 15,000 conjunctions per minute with the centralized solution and approximately 490 conjunctions per minute with the distributed solution.
Topology Optimization in Three-Phase C-Core SRMs
Authors: Gholamreza Davarpanah, Sajjad Mohammadi, James L. Kirtley
Date: 2024-10-31 00:00:00

This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical loading capability, leading to a higher torque density. The general formulation is discussed. Additionally, the shorter flux path within the design leads to a reduced core loss. The finite element method is employed in the design and field analysis. A comparison with a conventional motor shows the superiority of the proposed configuration. The optimized design is prototyped and tested. Both static and dynamic torques are extracted. A good correlation between simulation and experimental results is observed.
A Novel Radial-Flux IPM Eddy-Current Coupler for Wind Generator Applications
Authors: Sajjad Mohammadi, Gholamreza Davarpanah, James L. Kirtley
Date: 2024-10-31 00:00:00

A novel radial-flux eddy-current coupler with interior permanent magnets (IPM) is proposed, providing higher demagnetization tolerance, making it well-suited for applications with limited accessibility, such as offshore wind generation. Finite element analysis is employed in the design and derivation of coupler quantities. Finally, the coupler is prototyped to experimentally validate the design and the simulation results.
Carbon Neutral Greenhouse: Economic Model Predictive Control Framework for Education
Authors: Marek Wadinger, Rastislav Fáber, Erika Pavlovičová, Radoslav Paulen
Date: 2024-10-31 00:00:00
Tasks: Model Predictive Control

This paper presents a comprehensive framework aimed at enhancing education in modeling, optimal control, and nonlinear Model Predictive Control~(MPC) through a practical greenhouse climate control model. The framework includes a detailed mathematical model of lettuce growth and greenhouse, which are influenced by real-time external weather conditions obtained via an application programming interface~(API). Using this data, the MPC-based approach dynamically adjusts greenhouse conditions, optimizing plant growth and energy consumption and minimizing the social cost of CO\textsubscript{2}. The presented results demonstrate the effectiveness of this approach in balancing energy use with crop yield and reducing CO\textsubscript{2} emissions, contributing to economic efficiency and environmental sustainability. Besides optimizing lettuce production, the framework also provides a valuable resource for making control systems education more engaging and effective. The main aim is to provide students with a hands-on platform to understand the principles of modeling, the complexity of MPC and the trade-offs between profitability and sustainability in agricultural systems. This framework provides students with hands-on experience, helping them to understand the control theory better, connecting it to the practical implementation, and developing their problem-solving skills. The framework can be accessed at \url{ecompc4greenhouse.streamlit.app}.
LiFePO4 Battery SOC Estimation under OCV-SOC Curve Error Based onAdaptive Multi-Model Kalman Filter
Authors: Daniyaer Paizulamu, Lin Cheng, Yingrui Zhuang, Helin Xu, Ning Qi, Song Ci
Date: 2024-10-31 00:00:00

LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the OCV-SOC curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose an SOC estimation method that accounts for error in OSC. First, we establish battery equivalent circuit model (ECM) and introduce a parameters identification algorithm based on adaptive recursive least squares. Next, we derive the relationship between the innovation's cross-correlation matrix (CCM)/ auto-correlation matrix (ACM) of the Kalman filter and the OSC error. We then develop an adaptive multi-model Kalman filter (AMMKF), which dynamically adjusts the measurement model parameters of each filter based on the sign of the OSC error. By assigning a probability to each filter according to its predicted voltage distribution function, the optimal filter is selected. The proposed method is tested under various OSC error types and operating conditions. Results demonstrate that the proposed method achieves high accuracy and robustness, with an RMSE of less than 3\%, which is more than 10\% lower than the estimation error of traditional method.
Adaptive Distributed Observer-based Model Predictive Control for Multi-agent Formation with Resilience to Communication Link Faults
Authors: Binyan Xu, Yufan Dai, Afzal Suleman, Yang Shi
Date: 2024-10-31 00:00:00
Tasks: Model Predictive Control

In order to address the nonlinear multi-agent formation tracking control problem with input constraints and unknown communication faults, a novel adaptive distributed observer-based distributed model predictive control method is developed in this paper. This design employs adaptive distributed observers in local control systems to estimate the leader's state, dynamics, and relative positioning with respect to the leader. Utilizing the estimated data as local references, the original formation tracking control problem can be decomposed into several fully localized tracking control problems, which can be efficiently solved by the local predictive controller. Through the incorporation of adaptive distributed observers, this proposed design not only enhances the resilience of distributed formation tracking against communication faults but also simplifies the distributed model predictive control formulation.
Accurate Solutions to Optimal Control Problems via a Flexible Mesh and Integrated Residual Transcription
Authors: Lucian Nita, Eric C. Kerrigan
Date: 2024-10-30 00:00:00

We propose joining a flexible mesh design with an integrated residual transcription in order to improve the accuracy of numerical solutions to optimal control problems. This approach is particularly useful when state or input trajectories are non-smooth, but it may also be beneficial when dynamics constraints are stiff. Additionally, we implement an initial phase that will ensure a feasible solution is found and can be implemented immediately in real-time controllers. Subsequent iterations with warm-starting will improve the solution until optimality is achieved. Optimizing over the mesh node locations allows for discontinuities to be captured exactly, while integrated residuals account for the approximation error in-between the nodal points. First, we numerically show the improved convergence order for the flexible mesh. We then present the feasibility-driven approach to solve control problems and show how flexible meshing and integrated residual methods can be used in practice. The presented numerical examples demonstrate for the first time the numerical implementation of a flexible mesh for an integrated residual transcription. The results show that our proposed method can be more than two times more accurate than conventional fixed mesh collocation for the same computational time and more than three times more accurate for the same problem size.
PC-Gym: Benchmark Environments For Process Control Problems
Authors: Maximilian Bloor, José Torraca, Ilya Orson Sandoval, Akhil Ahmed, Martha White, Mehmet Mercangöz, Calvin Tsay, Ehecatl Antonio del Rio Chanona, Max Mowbray
Date: 2024-10-29 00:00:00
Tasks: Benchmarking, Chemical Process, Model Predictive Control, Reinforcement Learning (RL)

PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemical processes, incorporating nonlinear dynamics, disturbances, and constraints. The tool includes customizable constraint handling, disturbance generation, reward function design, and enables comparison of RL algorithms against Nonlinear Model Predictive Control (NMPC) across different scenarios. Case studies demonstrate the framework's effectiveness in evaluating RL approaches for systems like continuously stirred tank reactors, multistage extraction processes, and crystallization reactors. The results reveal performance gaps between RL algorithms and NMPC oracles, highlighting areas for improvement and enabling benchmarking. By providing a standardized platform, PC-Gym aims to accelerate research at the intersection of machine learning, control, and process systems engineering. By connecting theoretical RL advances with practical industrial process control applications, offering researchers a tool for exploring data-driven control solutions.
Constrained Trajectory Optimization for Hybrid Dynamical Systems
Authors: Pietro Noah Crestaz, Gokhan Alcan, Ville Kyrki
Date: 2024-10-30 00:00:00
Tasks: Computational Efficiency

Hybrid dynamical systems pose significant challenges for effective planning and control, especially when additional constraints such as obstacle avoidance, state boundaries, and actuation limits are present. In this letter, we extend the recently proposed Hybrid iLQR method [1] to handle state and input constraints within an indirect optimization framework, aiming to preserve computational efficiency and ensure dynamic feasibility. Specifically, we incorporate two constraint handling mechanisms into the Hybrid iLQR: Discrete Barrier State and Augmented Lagrangian methods. Comprehensive simulations across various operational situations are conducted to evaluate and compare the performance of these extended methods in terms of convergence and their ability to handle infeasible starting trajectories. Results indicate that while the Discrete Barrier State approach is more computationally efficient, the Augmented Lagrangian method outperforms it in complex and real-world scenarios with infeasible initial trajectories.
Signal Processing via Cross-Dimensional Projection
Authors: Daizhan Cheng
Date: 2024-10-30 00:00:00
Tasks: compressed sensing

Using projection between Euclidian spaces of different dimensions, the signal compression and decompression become straightforward. This encoding/decoding technique requires no preassigned measuring matrix as in compressed sensing. Moreover, in application there is no dimension or size restrictions. General formulas for encoding/decoding of any finite dimensional signals are provided. Their main properties are revealed. Particularly, it is shown that under the equivalence assumption the technique provides the best approximation with least square error.
Controlling the Wireless Power Transfer Mechanism of the Both-Sides Retrodirective System
Authors: Charleston Dale M. Ambatali, Shinichi Nakasuka
Date: 2024-10-30 00:00:00

To achieve efficient long-range wireless power transfer (WPT), large antenna systems are necessary spanning lengths of tens to thousands of meters in one dimension. This creates an array in the order of at least hundreds of thousands to billions of elements. This makes the implementation of beamforming control a challenge. Various works focus on iterative optimization or channel estimation to maintain high efficiency in a time-varying environment requiring complex processing capabilities. A simpler alternative is the both-sides retrodirective antenna array (BS-RDAA) system where iterative optimization or channel estimation is not required. In a previous study, it was observed that this system achieves maximum WPT efficiency if the system is marginally stable. Thus, there is a need to regulate the system to maintain marginal stability regardless of the transmission channel conditions. In this paper, we present a plant model for the system and design a control system to drive it to marginal stability. The result is a WPT implementation that is not dependent on complex processing capabilities present in other established high efficiency methods. We also confirmed the ability of the proposed design to maintain maximum efficiency in a dynamic environment through the results of an electromagnetic simulator and a time domain simulator.
Lyapunov Characterization for ISS of Impulsive Switched Systems
Authors: Saeed Ahmed, Patrick Bachmann, Stephan Trenn
Date: 2024-10-29 00:00:00

In this study, we investigate the ISS of impulsive switched systems that have modes with both stable and unstable flows. We assume that the switching signal satisfies mode-dependent average dwell and leave time conditions. To establish ISS conditions, we propose two types of time-varying ISS-Lyapunov functions: one that is non-decreasing and another one that is decreasing. Our research proves that the existence of either of these ISS-Lyapunov functions is a necessary and sufficient condition for ISS. We also present a technique for constructing a decreasing ISS-Lyapunov function from a non-decreasing one, which is useful for its own sake. Our findings also have added value to previous research that only studied sufficient conditions for ISS, as our results apply to a broader class of systems. This is because we impose less restrictive dwell and leave time constraints on the switching signal and our ISS-Lyapunov functions are time-varying with general nonlinear conditions imposed on them. Moreover, we provide a method to guarantee the ISS of a particular class of impulsive switched systems when the switching signal is unknown.
Extending Jacobian matrix in proving stability for nonlinear systems with one equilibrium point such as compressor
Authors: Seyed Mohammad Hosseindokht, SamanehAlsadat Saeedinia
Date: 2024-10-29 00:00:00

Global stability of the systems has always been vital of importance; however, this concept has not yet been sufficiently developed for the nonlinear systems. This paper extends the Jacobian matrix so that this method be able to seek the criteria to ensure global stability for a special class of nonlinear systems. In this regard, we propose a new analysis method that utilizes the Jacobian matrix concept, integrating with the characteristics of the negative eigenvalues to analyze the global stability of the nonlinear systems with only one equilibrium point. Also, the positive eigenvalue to analyze the global instability of the nonlinear systems with only one equilibrium point. Some theorems such as Hartman-Grobman and Popov criteria can prove this claim. To this end, several examples and a benchmark systems have been intended to evaluate the efficiency of the proposed method. Results indicate the high potential of the proposed approach in order to develop the global stability analysis. The nonlinear compressor model, categorized in this extensive class, is also investigated as a well-known industrial system besides other several examples. The outcomes demonstrate that extended Jacobian stability analysis can ensure global stability for this class of nonlinear systems under some spatial conditions, discussed in this paper.
Bayesian Quantum Neural Network for Renewable-Rich Power Flow with Training Efficiency and Generalization Capability Improvements
Authors: Ziqing Zhu, Shuyang Zhu, Siqi Bu
Date: 2024-10-29 00:00:00
Tasks: Computational Efficiency

This paper addresses the challenges of power flow calculation in large scale power systems with high renewable penetration, focusing on computational efficiency and generalization. Traditional methods, while accurate, struggle with scalability for large power systems. Existing data driven deep learning approaches, despite their speed, require extensive training data and lacks generalization capability in face of unseen scenarios, such as uncertainties of power flow caused by renewables. To overcome these limitations, we propose a novel power flow calculation model based on Bayesian Quantum Neural Networks (BQNNs). This model leverages quantum computing's ability to improve the training efficiency. The BQNN is trained using Bayesian methods, enabling it to update its understanding of renewable energy uncertainties dynamically, improving generalization to unseen data. Additionally, we introduce two evaluation metrics: effective dimension for model complexity and generalization error bound to assess the model's performance in unseen scenarios. Our approach demonstrates improved training efficiency and better generalization capability, making it as an effective tool for future steady-state power system analysis.
Average Predictor-Feedback Control Design for Switched Linear Systems
Authors: Andreas Katsanikakis, Nikolaos Bekiaris-Liberis, Delphine Bresch-Pietri
Date: 2024-10-29 00:00:00

We develop an input delay-compensating feedback law for linear switched systems with time-dependent switching. Because the future values of the switching signal, which are needed for constructing an exact predictor-feedback law, may be unavailable at current time, the key design challenge is how to construct a proper predictor state. We resolve this challenge constructing an average predictor-based feedback law, which may be viewed as an exact predictor-feedback law for a particular average system without switching. We establish that, under the predictor-based control law introduced, the closed-loop system is exponentially stable, provided that the plant's parameters are sufficiently close to the corresponding parameters of the average system. In particular, the allowable difference is inversely proportional to the size of delay and proportional to the dwell time of the switching signal. Since no restriction is imposed on the size of delay or dwell time themselves, such a limitation on the parameters of each mode is inherent to the problem considered (in which no a priori information on the switching signal is available), and thus, it cannot be removed. The stability proof relies on two main ingredients-a Lyapunov functional constructed via backstepping and derivation of solutions' estimates for the difference between the average and the exact predictor states. We present consistent, numerical simulation results, which illustrate the necessity of employing the average predictor-based law for achieving stabilization and desired performance of the closed-loop system.
Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection and Mitigation
Authors: Sajjad Maleki, Shijie Pan, Subhash Lakshminarayana, Charalambos Konstantinou
Date: 2024-10-29 00:00:00

The growing penetration of IoT devices in power grids despite its benefits, raises cybersecurity concerns. In particular, load-altering attacks (LAAs) targeting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review of LAAs, highlighting the threat model, analyzing their impact on transmission and distribution networks, and the electricity market dynamics. We also review the detection and localization schemes for LAAs that employ either model-based or data-driven approaches, with some hybrid methods combining the strengths of both. Additionally, mitigation techniques are examined, focusing on both preventive measures, designed to thwart attack execution, and reactive methods, which aim to optimize responses to ongoing attacks. We look into the application of each study and highlight potential streams for future research.
Addressing Imbalance Risk with Reserves and Flexibility Options: An ERCOT-like Case Study
Authors: Elina Spyrou, Robin Hytowitz, Benjamin F. Hobbs, Ibrahim Krad, Liping Li, Mengmeng Cai, Michael Blonsky
Date: 2024-10-29 00:00:00
Tasks: Management

As the role of variable renewables in electricity markets expands, new market products help system operators manage imbalances caused by uncertainty and variability. Whereas work in the last decade has focused on constructing demand curves for central procurement of those products, little attention has been paid to designing their settlement scheme and understanding the connections between the economic value of these products, the schedule of variable resources, and the cost of flexibility. In this article, we compare a new product called Flexibility Options, which addresses these gaps, with a traditional reserve product using a case study similar to the 2019 Texas (ERCOT) system. Our findings suggest that both products are equally effective in managing imbalances, but Flexibility Options have superior risk management properties and keep the system operator revenue adequate.