pwc-archive/papers-with-abstracts huggingface dataset:
Robust Deep Reinforcement Learning for Volt-VAR Optimization in Active Distribution System under Uncertainty
Authors: Zhengrong Chen, Siyao Cai, A. P. Sakis Meliopoulos
Date: 2024-09-27 00:00:00
Tasks: Conformal Prediction, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Safe Exploration

The deep reinforcement learning (DRL) based Volt-VAR optimization (VVO) methods have been widely studied for active distribution networks (ADNs). However, most of them lack safety guarantees in terms of power injection uncertainties due to the increase in distributed energy resources (DERs) and load demand, such as electric vehicles. This article proposes a robust deep reinforcement learning (RDRL) framework for VVO via a robust deep deterministic policy gradient (DDPG) algorithm. This algorithm can effectively manage hybrid action spaces, considering control devices like capacitors, voltage regulators, and smart inverters. Additionally, it is designed to handle uncertainties by quantifying uncertainty sets with conformal prediction and modeling uncertainties as adversarial attacks to guarantee safe exploration across action spaces. Numerical results on three IEEE test cases demonstrate the sample efficiency and safety of the proposed robust DDPG against uncertainties compared to the benchmark algorithms.
Robust and efficient data-driven predictive control
Authors: Mohammad Alsalti, Manuel Barkey, Victor G. Lopez, Matthias A. Müller
Date: 2024-09-27 00:00:00

We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is also computationally efficient. This is done by leveraging an alternative data-based representation of the trajectories of linear time-invariant (LTI) systems. The proposed scheme relies only on using (short and potentially irregularly measured) noisy input-output data, the amount of which is independent of the prediction horizon. To account for measurement noise, we provide a novel result that quantifies the uncertainty between the true (unknown) restricted behavior of the system and the estimated one from noisy data. Furthermore, we show that the robust eDDPC scheme is recursively feasible and that the resulting closed-loop system is practically stable. Finally, we compare the performance of this scheme to existing ones on a case study of a four tank system.
Path Following Model Predictive Control of a Coupled Autonomous Underwater Vehicle
Authors: Isah A. Jimoh, Hong Yue
Date: 2024-09-27 00:00:00
Tasks: Model Predictive Control

The operation of an autonomous underwater vehicle (AUV) faces challenges in following predetermined waypoints due to coupled motions under environmental disturbances. To address this, a 3D path following guidance and control system is developed in this work based on the line-of-sight (LOS) guidance method. Conventionally, the 3D path following problem is transformed into heading and depth control problems, assuming that the motion of the vehicle is decoupled in horizontal and depth coordinates. The proposed control system design avoids this simplifying assumption by transforming the problem into a 3D position and orientation tracking problem. This design is achieved by computing a 2D horizontal coordinate based on the desired heading and then computing a corresponding LOS depth coordinate. A model predictive controller (MPC) is then implemented using the 3D LOS coordinate and the computed orientation vector. The MPC obtains a robust control by solving a minimax optimisation problem considering the effects of unknown ocean disturbances. The effectiveness of the proposed guidance and control system is demonstrated through the simulation of a prototype AUV system. Numerical results show that the AUV can follow predetermined waypoints in the presence of time-varying disturbances, and the system is steered at a constant surge speed that is proportional to the radius of the circle of acceptance used to implement the guidance system.
Dual Pricing to Prioritize Renewable Energy and Consumer Preferences in Electricity Markets
Authors: Emilie Jong, Samuel Chevalier, Spyros Chatzivasileiadis, Shie Mannor
Date: 2024-09-27 00:00:00

Electricity markets currently fail to incorporate preferences of buyers, treating polluting and renewable energy sources as having equal social benefit under a system of uniform clearing prices. Meanwhile, renewable energy is prone to curtailment due to transmission constraints, forcing grid operators to reduce or shut down renewable energy production despite its availability and need. This paper proposes a ``dual pricing mechanism" which allows buyers to bid both their willingness to pay for electricity, and additionally, their preference for green energy. Designed for use in deregulated electricity markets, this mechanism prioritizes the dispatch of more renewable energy sources according to consumer preferences. Traditional uniform clearing prices, which treat all energy sources equally, do not reflect the growing share of green energy in the power grid and the environmental values of consumers. By allowing load-serving entities to bid their willingness to pay for renewable energy directly into the clearing market, our proposed framework generates distinct pricing signals for green and ``black" electricity.
On Adaptive Frequency Sampling for Data-driven Model Order Reduction Applied to Antenna Responses
Authors: Lucas Åkerstedt, Darwin Blanco, B. L. G. Jonsson
Date: 2024-09-27 00:00:00

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with these adaptive error estimates, in an iterative algorithm, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. Both methods effectively provide frequency domain error estimates, which are essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework, where the proposed algorithms reach the smallest errors for the smallest number of frequency points chosen.
Impact of number of elements on the directivity of planar array of monopole antenna
Authors: S. E. Akpo, O. U. Omini, G. A. Tawo
Date: 2024-09-27 00:00:00

This research investigates how the number of elements affects the monopole antenna's planar array's directivity. This study also takes into account the antenna's effect on the whole field it radiates. The monopole antennas are arranged in a planar configuration with all the components in their proper locations using the Hadamard matrix approach. Each matrix's directivities and array factors were calculated, and a MATLAB tool was used to simulate the radiation pattern. A range of elements from 4 X 4 to 50 X 50 planar layouts were taken into consideration during the investigation. Increasing the number of elements improves the directivity. Increasing the number of elements in the planar array resulted in a great improvement in directivity, as seen by the computed and simulated results. Consequently, by increasing the antenna's directivity, a greater number of elements influences the overall field emitted.
Diffusion Models for Intelligent Transportation Systems: A Survey
Authors: Mingxing Peng, Kehua Chen, Xusen Guo, Qiming Zhang, Hui Zhong, Meixin Zhu, Hai Yang
Date: 2024-09-24 00:00:00
Tasks: Autonomous Driving, Survey, Trajectory Prediction

Intelligent Transportation Systems (ITS) are vital in modern traffic management and optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models have emerged as transformative tools for addressing complex challenges within ITS. In this paper, we present a comprehensive survey of diffusion models for ITS, covering both theoretical and practical aspects. First, we introduce the theoretical foundations of diffusion models and their key variants, including conditional diffusion models and latent diffusion models, highlighting their suitability for modeling complex, multi-modal traffic data and enabling controllable generation. Second, we outline the primary challenges in ITS and the corresponding advantages of diffusion models, providing readers with a deeper understanding of the intersection between ITS and diffusion models. Third, we offer a multi-perspective investigation of current applications of diffusion models in ITS domains, including autonomous driving, traffic simulation, trajectory prediction, and traffic safety. Finally, we discuss state-of-the-art diffusion model techniques and highlight key ITS research directions that warrant further investigation. Through this structured overview, we aim to provide researchers with a comprehensive understanding of diffusion models for ITS, thereby advancing their future applications in the transportation domain.
Survey of Moving Target Defense in Power Grids: Design Principles, Tradeoffs, and Future Directions
Authors: Subhash Lakshminarayana, Yexiang Chen, Charalambos Konstantinou, Daisuke Mashima, Anurag K. Srivastava
Date: 2024-09-26 00:00:00

Moving target defense (MTD) in power grids is an emerging defense technique that has gained prominence in the recent past. It aims to solve the long-standing problem of securing the power grid against stealthy attacks. The key idea behind MTD is to introduce periodic/event-triggered controlled changes to the power grid's SCADA network/physical plant, thereby invalidating the knowledge attackers use for crafting stealthy attacks. In this paper, we provide a comprehensive overview of this topic and classify the different ways in which MTD is implemented in power grids. We further introduce the guiding principles behind the design of MTD, key performance metrics, and the associated trade-offs in MTD and identify the future development of MTD for power grid security.
Analysis of a platooned car-following model with different inter-platoon communication levels
Authors: Shouwei Hui, Michael Zhang
Date: 2024-09-26 00:00:00
Tasks: Autonomous Vehicles, Management

Despite growing interest in vehicle platooning research, the effect of communication capability between platoons is not investigated to a depth of depth. In this paper, we extend a single-platoon car-following (CF) model to multi-platoon CF models for connected and autonomous vehicles (CAVs) with different inter-platoon communication capabilities. Specifically, we consider forward and backward connection availabilities with delays between platoons. Using linear stability analysis, we discovered that for identical platoons, stability increases with platoon size and connection availabilities and decreases exponentially with large delay. With maximum acceleration and emergency braking constraints integrated into the models, we performed simulations for various cases of CAV platoons and mixed autonomy with human-driven vehicles (HDVs). The simulation results for CAV platoons are consistent with theoretical analysis. The mixed autonomy experiments demonstrate that in the ring road scenario, CAV platoons can stabilize HDVs without adaptions, and the effect of distribution is marginal. Overall, this paper provides valuable insights for designing vehicle-to-vehicle (V2V) communications and managing mixed traffic scenarios.
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach
Authors: Ali Amhaz, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine
Date: 2024-09-26 00:00:00
Tasks: Reinforcement Learning (RL)

This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be able to transmit the signal to the other user providing an extra transmission resource and enhancing performance. Both users are equipped with a receiving MA each and a transmitting MA for the relaying user. In this regard, we formulate an optimization problem with the objective of maximizing the achievable sum rate by jointly determining the beamforming vector at the BS, the transmit power at the device and the positions of the MAs while meeting the quality of service (QoS) constraints. Due to the non-convex structure of the formulated problem and the randomness in the channels we adopt a deep deterministic policy gradient (DDPG) approach, a reinforcement learning (RL) algorithm capable of dealing with continuous state and action spaces. Numerical results demonstrate the superiority of the presented model compared to the other benchmark schemes showing gains reaching 45% compared to the NOMA enabled MA scheme and 60% compared to C-NOMA model with fixed antennas. The solution approach showed 93% accuracy compared to the optimal solution.
Discontinuous Reception with Adjustable Inactivity Timer for IIoT
Authors: David E. Ruíz-Guirola, Carlos A. Rodríguez-López, Onel L. A. López, Samuel Montejo-Sánchez, Vitalio Alfonso Reguera, Matti Latva-aho
Date: 2024-09-26 00:00:00

Discontinuous reception (DRX) is a key technology for reducing the energy consumption of industrial Internet of Things (IIoT) devices. Specifically, DRX allows the devices to operate in a low-power mode when no data reception is scheduled, and its effectiveness depends on the proper configuration of the DRX parameters. In this paper, we characterize the DRX process departing from a semi-Markov chain modeling. We detail two ways to set DRX parameters to minimize the device power consumption while meeting a mean delay constraint. The first method exhaustively searches for the optimal configuration. In contrast, the second method uses a low-complexity metaheuristic to find a sub-optimal configuration, thus considering ideal and practical DRX configurations. Notably, within the DRX parameters, the inactivity timer (IT) is a caution time that specifies how long a device remains active after the last information exchange. Traditionally, a device implementing DRX will restart the IT after each data reception as a precedent to a low-power mode. The usual approach lies in restarting the IT whenever new data is received during this cautious period, which might sometimes needlessly extend the active time. Herein, we propose a more efficient method in which the transmit base station (BS) explicitly indicates restarting the timer through the control channel only when appropriate. The decision is taken based on the BS's knowledge about its buffer status. We consider Poisson and bursty traffic models, which are typical in IIoT setups, and verify the suitability of our proposal for reducing the energy consumption of the devices without significantly compromising the communication latency through extensive numerical simulations. Specifically, energy-saving gains of up to 30% can be obtained regardless of the arrival rate and delay constraints.
On the Output Redundancy of LTI Systems: A Geometric Approach with Application to Privacy
Authors: Guitao Yang, Alexander J. Gallo, Angelo Barboni, Riccardo M. G. Ferrari, Andrea Serrani, Thomas Parisini
Date: 2024-09-26 00:00:00

This paper examines the properties of output-redundant systems, that is, systems possessing a larger number of outputs than inputs, through the lenses of the geometric approach of Wonham et al. We begin by formulating a simple output allocation synthesis problem, which involves ``concealing" input information from a malicious eavesdropper having access to the system output, while still allowing for a legitimate user to reconstruct it. It is shown that the solvability of this problem requires the availability of a redundant set of outputs. This very problem is instrumental to unveiling the fundamental geometric properties of output-redundant systems, which form the basis for our subsequent constructions and results. As a direct application, we demonstrate how output allocation can be employed to effectively protect the information of input information from certain output eavesdroppers with guaranteed results.
Stereographic Projection of Probabilistic Frequency-Domain Uncertainty
Authors: Anton Nystrom, Venkatraman Renganathan, Michael Cantoni
Date: 2024-09-26 00:00:00

This paper investigates the stereographic projection of points along the Nyquist plots of single input single output (SISO) linear time invariant (LTI) systems subject to probabilistic uncertainty. At each frequency, there corresponds a complex-valued random variable with given probability distribution in the complex plane. The chordal distance between the stereographic projections of this complex value and the corresponding value for a nominal model, as per the well-known Nu-Gap metric of Vinnicombe, is also a random quantity. The main result provides the cumulative density function (CDF) of the chordal distance at a given frequency. Such a stochastic distance framework opens up a fresh and a fertile research direction on probabilistic robust control theory.
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method
Authors: Ugur Yildiran
Date: 2023-09-30 00:00:00
Tasks: Benchmarking, Reinforcement Learning (RL)

Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning (RL) based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.
Safe Stabilization using Nonsmooth Control Lyapunov Barrier Function
Authors: Jianglin Lan, Eldert van Henten, Peter Groot Koerkamp, Congcong Sun
Date: 2024-09-20 00:00:00

This paper addresses the challenge of safe stabilization, ensuring the system state reach the origin while avoiding unsafe regions. Existing approaches relying on smooth Lyapunov barrier functions often fail to guarantee a feasible controller. To overcome this limitation, we introduce the nonsmooth Control Lyapunov Barrier Function (NCLBF), which ensures the existence of a safe and stabilizing controller. We provide a systematic framework for designing NCLBF and feedback control strategies to achieve safe stabilization in the presence of multiple bounded unsafe regions. Theoretical analysis and simulations of both linear and nonlinear systems demonstrate the effectiveness and superiority of our approach compared to the existing smooth functions method.
Optimal control of stochastic reaction networks with entropic control cost and emergence of mode-switching strategies
Authors: Shuhei A. Horiguchi, Tetsuya J. Kobayashi
Date: 2024-09-26 00:00:00

Controlling the stochastic dynamics of biological populations is a challenge that arises across various biological contexts. However, these dynamics are inherently nonlinear and involve a discrete state space, i.e., the number of molecules, cells, or organisms. Additionally, the possibility of extinction has a significant impact on both the dynamics and control strategies, particularly when the population size is small. These factors hamper the direct application of conventional control theories to biological systems. To address these challenges, we formulate the optimal control problem for stochastic population dynamics by utilizing a control cost function based on the Kullback-Leibler divergence. This approach naturally accounts for population-specific factors and simplifies the complex nonlinear Hamilton-Jacobi-Bellman equation into a linear form, facilitating efficient computation of optimal solutions. We demonstrate the effectiveness of our approach by applying it to the control of interacting random walkers, Moran processes, and SIR models, and observe the mode-switching phenomena in the control strategies. Our approach provides new opportunities for applying control theory to a wide range of biological problems.
The Power-Oriented Graphs Modeling Technique: From the Fundamental Principles to the Systematic, Step-by-Step Modeling of Complex Physical Systems
Authors: Davide Tebaldi, Roberto Zanasi
Date: 2024-09-25 00:00:00

Modeling physical systems is an essential skill for a control engineer, since it enables to achieve a deep understanding of their dynamic behavior and, consequently, the development of effective control strategies. The first part of this article provides a tutorial description of the fundamental principles and properties of the Power-Oriented Graphs (POG) modeling technique. Various case studies in different energetic domains are then presented to consolidate the fundamental principles, each highlighting different features of the POG modeling technique. The latter is then compared with the other two main graphical modeling techniques available in the literature, namely Bond Graph (BG) and Energetic Macroscopic Representation (EMR). The second part of this article assumes once again a tutorial nature, in order to introduce the new Fast Modeling POG (FMPOG) procedure. The FMPOG, which operates in the POG framework, is a methodical step-by-step procedure that enables the readers to quickly derive the power-oriented graphical model of physical systems starting from their schematics. From the power-oriented graphical model, the state-space model can then be directly determined. To ensure the FMPOG procedure is easily usable by the entire community, we apply it to three examples in different energetic domains in this article, guiding the reader step-by-step through the derivation of the physical systems models. A freely available Matlab/Simulink program is provided in a repository, allowing the users to automatically apply the FMPOG procedure to various classes of physical systems. This program allows to convert the physical systems schematics into the corresponding POG block schemes and, ultimately, into the state-space mathematical models.