pwc-archive/papers-with-abstracts huggingface dataset:
Privacy-aware Fully Model-Free Event-triggered Cloud-based HVAC Control
Authors: Zhenan Feng, Ehsan Nekouei
Date: 2024-10-08 00:00:00
Tasks: Cloud Computing, Model Predictive Control

Privacy is a major concern when computing-as-a-service (CaaS) platforms, e.g., cloud-computing platforms, are utilized for building automation, as CaaS platforms can infer sensitive information, such as occupancy, using the sensor measurements of a building. Although the existing encrypted model-based control algorithms can ensure the security and privacy of sensor measurements, they are highly complex to implement and require high computational resources, which result in a high cost of using CaaS platforms. To address these issues, in this paper, we propose an encrypted fully model-free event-triggered cloud-based HVAC control framework that ensures the privacy of occupancy information and minimizes the communication and computation overhead associated with encrypted HVAC control. To this end, we first develop a model-free controller for regulating indoor temperature and CO2 levels. We then design a model-free event-triggering unit which reduces the communication and computation costs of encrypted HVAC control using an optimal triggering policy. Finally, we evaluate the performance of the proposed encrypted fully model-free event-triggered cloud-based HVAC control framework using the TRNSYS simulator, comparing it to an encrypted model-based event-triggered control framework, which uses model predictive control to regulate the indoor climate. Our numerical results demonstrate that, compared to the encrypted model-based method, the proposed fully model-free framework improves the control performance while reducing the communication and computation costs. More specifically, it reduces the communication between the system and the CaaS platform by 64% amount, and its computation time is 75% less than that of the model-based control.
Distributed Coordination for Multi-Vehicle Systems in the Presence of Misbehaving Vehicles
Authors: Dongkun Han, Yijun Huang, Hejun Huang, Tianrui Fang
Date: 2024-10-08 00:00:00
Tasks: Autonomous Driving, Collision Avoidance

The coordination problem of multi-vehicle systems is of great interests in the area of autonomous driving and multi-vehicle control. This work mainly focuses on multi-task coordination problem of a group of vehicles with a bicycle model and some specific control objectives, including collision avoidance, connectivity maintenance and convergence to desired destinations. The basic idea is to develop a proper Lyapunov-like barrier function for all tasks and a distributed controller could be built in the presence of misbehaving vehicles. Control protocols are provided for both leader vehicle and follower vehicles. The simulation results demonstrate the effectiveness of proposed method.
Mobile IoT device for BPM monitoring people with heart problems
Authors: Luis Chuquimarca, Dahyana Roca, Washington Torres, Luis Amaya, Jaime Orozco, David Sánchez
Date: 2024-10-08 00:00:00

The developed system using a mobile electronic device for monitoring and warnings of heart problems, when the heart rate is outside the nominal range, which ranges from 60 to 100 beats per minute. Also, a system has been developed to save and monitor in real time changes of the cardiac pulsations, through a sensor connected to a control system. The connection of the communication module for Arduino GSM/GPRS/GPS, using the GPS network to locate the user. In addition, this device connects with GSM / GPRS technology that allows text messages to be sent to the contact number configured in the device, when warnings of heart problems are issued, moreover connects to the internet to store data in the cloud.
Linear Convergence of Data-Enabled Policy Optimization for Linear Quadratic Tracking
Authors: Shubo Kang, Feiran Zhao, Keyou You
Date: 2024-10-08 00:00:00

Data-enabled policy optimization (DeePO) is a newly proposed method to attack the open problem of direct adaptive LQR. In this work, we extend the DeePO framework to the linear quadratic tracking (LQT) with offline data. By introducing a covariance parameterization of the LQT policy, we derive a direct data-driven formulation of the LQT problem. Then, we use gradient descent method to iteratively update the parameterized policy to find an optimal LQT policy. Moreover, by revealing the connection between DeePO and model-based policy optimization, we prove the linear convergence of the DeePO iteration. Finally, a numerical experiment is given to validate the convergence results. We hope our work paves the way to direct adaptive LQT with online closed-loop data.
Structural Constraints for Physics-augmented Learning
Authors: Simon Kuang, Xinfan Lin
Date: 2024-10-07 00:00:00
Tasks: Physics-informed machine learning

When the physics is wrong, physics-informed machine learning becomes physics-misinformed machine learning. A powerful black-box model should not be able to conceal misconceived physics. We propose two criteria that can be used to assert integrity that a hybrid (physics plus black-box) model: 0) the black-box model should be unable to replicate the physical model, and 1) any best-fit hybrid model has the same physical parameter as a best-fit standalone physics model. We demonstrate them for a sample nonlinear mechanical system approximated by its small-signal linearization.
A New Type of Nonlinear Disturbance Rejection
Authors: Simon Kuang, Xinfan Lin
Date: 2024-10-07 00:00:00

Asymptotic disturbance rejection (equivalently tracking) for nonlinear systems has been studied only in qualitative terms (the state is asymptotically stable under bounded disturbances). We show how to prove quantitative performance guarantees for the nonlinear servomechanism problem. Our technique originates by applying a gain inequalities point of view to an ad fontes reexamination of the linear problem: what is the nonlinear equivalent of a sensitivity transfer function with a zero at the origin? We answer: a nonlinear input-output system is high-pass if its output is stable with respect to the \emph{derivative} of the input. We first show that definition generalizes high-pass resistor-capacitor circuit analysis to accommodate nonlinear resistors. We then show that this definition generalizes the steady-state disturbance rejection property of integral feedback controllers for linear systems. The theoretical payoff is that low-frequency disturbance rejection is captured by a quantitative, non-asymptotic output cost bound. Finally, we raise theoretical questions about compositionality of nonlinear operators.
Modeling Buffer Occupancy in bittide Systems
Authors: Sanjay Lall, Tammo Spalink
Date: 2024-10-07 00:00:00

The bittide mechanism enables logically synchronous computation across distributed systems by leveraging the continuous frame transmission inherent to wired networks such as Ethernet. Instead of relying on a global clock, bittide uses a decentralized control system to adjust local clock frequencies, ensuring all nodes operate with a consistent notion of time by utilizing elastic buffers at each node to absorb frequency variations. This paper presents an analysis of the steady-state occupancy of these elastic buffers, a critical factor influencing system latency. Using a fluid model of the bittide system, we prove that buffer occupancy converges and derive an explicit formula for the steady-state value in terms of system parameters, including network topology, physical latencies, and controller gains. This analysis provides valuable insights for optimizing buffer sizes and minimizing latency in bittide-based distributed systems.
Upgrading SPHERE with the second stage AO system SAXO+: frequency-based data-driven controller for adaptive optics
Authors: Isaac Dinis, François Wildi, Damien Ségransan, Vaibhav Gupta, Alireza Karimi, Michel Tallon, Isabelle Bosc, Maud Langlois, Magali Loupias, Clémentine Bechet, Eric Thiébaut, Charles Goulas, Florian Ferreira, Anthony Boccaletti, Fabrice Vidal, Caroline Kulcsar, Henri-François Raynaud, Nicolas Galland, Markus Kasper, Julien Milli, David Mouillet, Laura Schreiber, Emiliano Diolaiti, Raffaele Gratton, Gael Chauvin
Date: 2024-10-07 00:00:00

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer functions through convex optimization to obtain an optimal control in an infinite input response filter form. Evaluated within the SAXO+ project, it demonstrates efficacy under diverse atmospheric conditions and operational scenarios. The proposed controller is tested in both standard and disentangled adaptive optics schemes, showcasing its adaptability and performance. Experimental validation is conducted using the COMPASS simulation tool, affirming the controller's promise for enhancing adaptive optics systems in real-world applications.
Active Inference for Closed-loop transmit beamsteering in Fetal Doppler Ultrasound
Authors: Beatrice Federici, Ruud JG van Sloun, Massimo Mischi
Date: 2024-10-07 00:00:00
Tasks: Heart rate estimation, Position

Doppler ultrasound is widely used to monitor fetal heart rate during labor and pregnancy. Unfortunately, it is highly sensitive to fetal and maternal movements, which can cause the displacement of the fetal heart with respect to the ultrasound beam, in turn reducing the Doppler signal-to-noise ratio and leading to erratic, noisy, or missing heart rate readings. To tackle this issue, we augment the conventional Doppler ultrasound system with a rational agent that autonomously steers the ultrasound beam to track the position of the fetal heart. The proposed cognitive ultrasound system leverages a sequential Monte Carlo method to infer the fetal heart position from the power Doppler signal, and employs a greedy information-seeking criterion to select the steering angle that minimizes the positional uncertainty for future timesteps. The fetal heart rate is then calculated using the Doppler signal at the estimated fetal heart position. Our results show that the system can accurately track the fetal heart position across challenging signal-to-noise ratio scenarios, mainly thanks to its dynamic transmit beam steering capability. Additionally, we find that optimizing the transmit beamsteering to minimize positional uncertainty also optimizes downstream heart rate estimation performance. In conclusion, this work showcases the power of closed-loop cognitive ultrasound in boosting the capabilities of traditional systems.
State Observer for the Fourth-order Model of a Salient Pole Synchronous Generator with Stator Losses: Known and Partially Unknown Input Cases
Authors: Alexey Bobtsov, Romeo Ortega, Nicolai Lorenz-Meyer, Johannes Schiffer
Date: 2024-10-07 00:00:00
Tasks: parameter estimation, State Estimation

In this paper we study the question of how to reconstruct the state of a power system using Phasor Measurement Units (PMUs). In our previous research we proved that this question has an affirmative answer imposing some rather strict structural assumptions: namely, neglecting the generator rotors saliency and assuming that the stator resistance of the synchronous generator is zero. It was shown in simulations that the performance of the proposed observer was sensitive to these assumptions, observing a transient quality degradation for realistic simulations not imposing these assumptions. Moreover, it was assumed in our previous work that the mechanical power and the field voltage are available for measurement, a scenario that it is not always realistic. In this paper we accomplish two ambitious objectives. First, we propose a new observer that does not impose the simplifying assumptions on the generator model. Secondly, we consider the more realistic scenario where only mechanical power is available for measurement. That is, we solve a problem of state reconstruction of a nonlinear system with partially known input measurements -- that is well-known to be a very challenging task. The design of the first observer relies on two recent developments proposed by the authors, a parameter estimation based approach to the problem of state estimation and the use of the Dynamic Regressor Extension and Mixing (DREM) technique to estimate these parameters. The use of DREM allows us to overcome the problem of lack of persistent excitation that stymies the application of standard parameter estimation designs. On the other hand, the observer for the partial input measurement scenario relies on the clever exploitation of the systems model. Simulation results illustrates the good performance of the proposed observers.
An Optimized H5 Hysteresis Current Control with Clamped Diodes in Transformer-less Grid-PV Inverter
Authors: Sushil Phuyal, Shashwot Shrestha, Swodesh Sharma, Rachana Subedi, Anil Kumar Panjiyar, Mukesh Gautam
Date: 2024-10-07 00:00:00

With the rise of renewable energy penetration in the grid, photovoltaic (PV) panels are connected to the grid via inverters to supply solar energy. Transformer-less grid-tied PV inverters are gaining popularity because of their improved efficiency, reduced size, and lower costs. However, they can induce a path for leakage currents between the PV and the grid part due to the absence of galvanic isolation between them. This leads to serious electromagnetic interference, loss in efficiency and safety concerns. The leakage current is primarily influenced by the nature of the common mode voltage (CMV), which is determined by the switching techniques of the inverter. In this paper, a novel inverter topology of Hysteresis Controlled H5 with Two Clamping Diodes (HCH5-D2) has been derived. The HCH5-D2 topology helps to decouple the AC part (Grid) and DC part (PV) during the freewheeling to make the CMV constant and in turn, reduces the leakage current. Also, the additional diodes help to reduce the voltage spikes generated during the freewheeling period and maintain the CMV at a constant value. Finally, a 2.2kW grid-connected single-phase HCH5-D2 PV inverter system's MATLAB simulation has been presented with better results when compared with a traditional H4 inverter.
Transient-Safe and Attack-Resilient Secondary Control in AC Microgrids Under Polynomially Unbounded FDI Attacks
Authors: Mohamadamin Rajabinezhad, Nesa Shams, Yichao Wang, Shan Zuo
Date: 2024-10-07 00:00:00

This letter proposes a novel, fully distributed, transient-safe resilient secondary control strategies for AC microgrids, addressing unbounded false data injection (FDI) attacks on control input channels. Unlike existing methods that focus primarily on steady-state convergence, our approach guarantees transient safety, ensuring that system states remain within predefined safety bounds even during attack initiation a critical aspect overlooked in prior research. Given the reduction of network inertia by increasing the penetration of inverted-based renewables, large overshooting and intense fluctuations are more likely to occur during transients caused by disturbances and cyber-attacks. To mitigate these risks, the proposed control method enhances defense capabilities against polynomially unbounded FDI attacks, maintaining safe system trajectories for both frequency and voltage throughout the transient response. Through rigorous Lyapunov-based stability analysis, we formally certify the strategies to achieve uniformly ultimately bounded (UUB) convergence in frequency and voltage regulation, and active power sharing across multi-inverter-based AC microgrids. Numerical simulation studies verify the effectiveness of the proposed control protocols, demonstrating improved system reliability, safety and resilience under adverse conditions.
Path Planning and Robust Path Tracking Control of an Automated Parallel Parking Maneuver
Authors: Xincheng Cao, Levent Guvenc
Date: 2024-10-07 00:00:00

Self driving vehicles should be able to perform parallel parking or a similar maneuver successfully. With this motivation, the S shaped maneuverability test of the Ohio driver license examination is chosen here for automatic execution by a self driving vehicle with drive by wire capability and longitudinal and lateral controls. The Ohio maneuverability test requires the driver to start within an area enclosed by four pylons and the driver is asked to go to the left of the fifth pylon directly in front of the vehicle in a smooth and continuous manner while ending in a parallel direction to the initial one. The driver is then asked to go backwards to the starting location of the vehicle without stopping the vehicle or hitting the pylons. As a self driving vehicle should do a much better job repeatably than a driver, a high order polynomial path model is built along with speed profiling to start and stop smoothly at the ends of the path without large longitudinal and lateral accelerations. In contrast to the long horizon, higher speed path planning and path tracking control applications in the literature, this paper treats low speed and very short horizon path planning and path tracking control with stopping and direction reversal. The path is constructed using a segmented polynomial fit optimization routine that guarantees path curvature smoothness. A linear path tracking model is utilized as the basis of the designed control system consisting of a disturbance observer based curvature rejection filter and a speed scheduled, parameter space robust PID controller. Simulation studies indicate that it has better performance compared to other common control systems such as standalone PID controller and combined PID and feedforward control. indicate that it has better performance compared to other common control systems such as standalone PID controller and combined PID and feedforward control.
Distributed ADMM Approach for the Power Distribution Network Reconfiguration
Authors: Yacine Mokhtari, Patrick Coirault, Emmanuel Moulay, Jérôme Le Ny, Didier Larraillet
Date: 2024-10-06 00:00:00

The electrical network reconfiguration problem aims to minimize losses in a distribution system by adjusting switches while ensuring radial topology. The growing use of renewable energy and the complexity of managing modern power grids make solving the reconfiguration problem crucial. Distributed algorithms help optimize grid configurations, ensuring efficient adaptation to changing conditions and better utilization of renewable energy sources. This paper introduces a distributed algorithm designed to tackle the problem of power distribution network reconfiguration with a radiality constraint. This algorithm relies on ADMM (Alternating Direction Method of Multipliers), where each agent progressively updates its estimation based on the information exchanged with neighboring agents. We show that every agent is required to solve a linearly constrained convex quadratic programming problem and a Minimum Weight Rooted Arborescence Problem (MWRAP) with local weights during each iteration. Through numerical experiments, we demonstrate the performance of the proposed algorithm in various scenarios, including its application to a 33-bus test system and a real-world network.
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
Authors: Bart van Laatum, Eldert J. van Henten, Sjoerd Boersma
Date: 2024-10-06 00:00:00
Tasks: Numerical Integration, Reinforcement Learning (RL)

This study presents GreenLight-Gym, a new, fast, open-source benchmark environment for developing reinforcement learning (RL) methods in greenhouse crop production control. Built on the state-of-the-art GreenLight model, it features a differentiable C++ implementation leveraging the CasADi framework for efficient numerical integration. GreenLight-Gym improves simulation speed by a factor of 17 over the original GreenLight implementation. A modular Python environment wrapper enables flexible configuration of control tasks and RL-based controllers. This flexibility is demonstrated by learning controllers under parametric uncertainty using two well-known RL algorithms. GreenLight-Gym provides a standardized benchmark for advancing RL methodologies and evaluating greenhouse control solutions under diverse conditions. The greenhouse control community is encouraged to use and extend this benchmark to accelerate innovation in greenhouse crop production.
Data-driven Under Frequency Load Shedding Using Reinforcement Learning
Authors: Glory Justin, Santiago Paternain
Date: 2024-10-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Underfrequency load shedding (UFLS) is a critical control strategy in power systems aimed at maintaining system stability and preventing blackouts during severe frequency drops. Traditional UFLS schemes often rely on predefined rules and thresholds, which may not adapt effectively to the dynamic and complex nature of modern power grids. Reinforcement learning (RL) methods have been proposed to effectively handle the UFLS problem. However, training these RL agents is computationally burdensome due to solving multiple differential equations at each step of training. This computational burden also limits the effectiveness of the RL agents for use in real-time. To reduce the computational burden, a machine learning (ML) classifier is trained to capture the frequency response of the system to various disturbances. The RL agent is then trained using the classifier, thus avoiding multiple computations during each step of agent training. Key features of this approach include reduced training time, as well as faster real-time application compared to other RL agents, and its potential to improve system resilience by minimizing the amount of load shed while effectively stabilizing the frequency. Comparative studies with conventional UFLS schemes demonstrate that the RL-based strategy achieves superior performance while significantly reducing the time required. Simulation results on the IEEE 68-bus system validate the performance of the proposed RL method.
Decentralized Equitable Energy Access in Energy Communities
Authors: Siying Li, Timothy Douglas Mount, Lang Tong
Date: 2024-10-05 00:00:00
Tasks: Scheduling

We address the issue of equitable energy access within an energy community consisting of members with diverse socioeconomic backgrounds, including varying income levels and differing capacities to access distributed energy resources such as solar power and storage systems. While optimal energy consumption scheduling is well-studied, integrating equity into decentralized real-time energy access remains under-explored. This paper formulates Equity-regarding Welfare Maximization (EqWM)--a welfare optimization energy scheduling subject to equity constraints. We further develop a decentralized implementation (D-EqWM) as a bi-level optimization, where a non-profit operator designs a community pricing policy aimed at maximizing overall welfare, subject to constraints that ensure equitable access. Community members, in turn, optimize their individual consumption based on these prices. We present the optimal pricing policy along with its key properties.