pwc-archive/papers-with-abstracts huggingface dataset:
Communication-Efficient MARL for Platoon Stability and Energy-efficiency Co-optimization in Cooperative Adaptive Cruise Control of CAVs
Authors: Min Hua, Dong Chen, Kun Jiang, Fanggang Zhang, Jinhai Wang, Bo wang, Quan Zhou, Hongming Xu
Date: 2024-06-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Multi-agent Reinforcement Learning

Cooperative adaptive cruise control (CACC) has been recognized as a fundamental function of autonomous driving, in which platoon stability and energy efficiency are outstanding challenges that are difficult to accommodate in real-world operations. This paper studied the CACC of connected and autonomous vehicles (CAVs) based on the multi-agent reinforcement learning algorithm (MARL) to optimize platoon stability and energy efficiency simultaneously. The optimal use of communication bandwidth is the key to guaranteeing learning performance in real-world driving, and thus this paper proposes a communication-efficient MARL by incorporating the quantified stochastic gradient descent (QSGD) and a binary differential consensus (BDC) method into a fully-decentralized MARL framework. We benchmarked the performance of our proposed BDC-MARL algorithm against several several non-communicative andcommunicative MARL algorithms, e.g., IA2C, FPrint, and DIAL, through the evaluation of platoon stability, fuel economy, and driving comfort. Our results show that BDC-MARL achieved the highest energy savings, improving by up to 5.8%, with an average velocity of 15.26 m/s and an inter-vehicle spacing of 20.76 m. In addition, we conducted different information-sharing analyses to assess communication efficacy, along with sensitivity analyses and scalability tests with varying platoon sizes. The practical effectiveness of our approach is further demonstrated using real-world scenarios sourced from open-sourced OpenACC.
Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading
Authors: Maicol Ciani, Emanuele Parisi, Alberto Musa, Francesco Barchi, Andrea Bartolini, Ari Kulmala, Rafail Psiakis, Angelo Garofalo, Andrea Acquaviva, Davide Rossi
Date: 2024-06-17 00:00:00
Tasks: Autonomous Vehicles, Benchmarking

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-source RISC-V design with a comprehensive security toolkit as a standalone system-on-chip (SoC). OpenTitan includes Earl Grey, a fully implemented and silicon-proven SoC, and Darjeeling, announced but not yet fully implemented. Earl Grey targets standalone SoC implementations, while Darjeeling is for integrable implementations. The literature lacks a silicon-ready embedded implementation of an open-source Root of Trust, despite lowRISC's efforts on Darjeeling. We address the limitations of existing implementations by optimizing data transfer latency between memory and cryptographic accelerators to prevent under-utilization and ensure efficient task acceleration. Our contributions include a comprehensive methodology for integrating custom extensions and IPs into the Earl Grey architecture, architectural enhancements for system-level integration, support for varied boot modes, and improved data movement across the platform. These advancements facilitate deploying OpenTitan in broader SoCs, even without specific technology-dependent IPs, providing a deployment-ready research vehicle for the community. We integrated the extended Earl Grey architecture into a reference architecture in a 22nm FDX technology node, benchmarking the enhanced architecture's performance. The results show significant improvements in cryptographic processing speed, achieving up to 2.7x speedup for SHA-256/HMAC and 1.6x for AES accelerators compared to the baseline Earl Grey architecture.
Leveraging Cooperative Connected Automated Vehicles for Mixed Traffic Safety
Authors: Chenguang Zhao, Tamas G. Molnar, Huan Yu
Date: 2024-06-17 00:00:00

The introduction of connected and automated vehicles (CAV) is believed to reduce congestion, enhance safety, and improve traffic efficiency. Numerous research studies have focused on controlling pure CAV platoons in fully connected automated traffic, as well as single or multiple CAVs in mixed traffic with human-driven vehicles (HVs). CAV cruising control designs have been proposed to stabilize the car-following traffic dynamics, but few studies has considered their safety impact, particularly the trade-offs between stability and safety. In this paper, we study how cooperative control strategies for CAVs can be designed to enhance the safety and smoothness of mixed traffic under varying penetrations of connectivity and automation. Considering mixed traffic where a pair of CAVs travels amongst HVs, we design cooperative feedback controllers for the pair CAVs to stabilize traffic via cooperation and, possibly, by also leveraging connectivity with HVs. The real-time safety impact of the CAV controllers is investigated using control barrier functions (CBF). We construct CBF safety constraints, based on which we propose safety-critical control designs to guarantee CAV safety, HV safety and platoon safety. Both theoretical and numerical analyses have been conducted to explore the effect of CAV cooperation and HV connectivity on stability and safety. Our results show that the cooperation of CAVs helps to stabilize the mixed traffic while safety can be guaranteed with the safety filters. Moreover, connectivity between CAVs and HVs offers additional benefits: if an HV connects to an upstream CAV (i.e., the CAV looks ahead), it helps the CAV to stabilize the upstream traffic, while if an HV connects to a downstream CAV (i.e., the CAV looks behind), the safety of this connected HV can be enhanced.
A General Framework for Load Forecasting based on Pre-trained Large Language Model
Authors: Mingyang Gao, Suyang Zhou, Wei Gu, Zhi Wu, Haiquan Liu, Aihua Zhou
Date: 2024-06-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Load Forecasting

Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the advancement of data-driven methods, machine learning and deep learning models have become the predominant approaches for load forecasting tasks. In recent years, pre-trained large language models (LLMs) have achieved significant progress, demonstrating superior performance across various fields. This paper proposes a load forecasting method based on LLMs, offering not only precise predictive capabilities but also broad and flexible applicability. Additionally, a data modeling method is introduced to effectively transform load sequence data into natural language suitable for LLM training. Furthermore, a data enhancement strategy is designed to mitigate the impact of LLM hallucinations on forecasting results. The effectiveness of the proposed method is validated using two real-world datasets. Compared to existing methods, our approach demonstrates state-of-the-art performance across all validation metrics.
Energy efficiency analysis of ammonia-fueled power systems for vehicles considering residual heat recovery
Authors: Zexin Nie, Yi Huang, Guangyu Tian
Date: 2024-06-17 00:00:00

Ammonia, known as a good hydrogen carrier, shows great potential for use as a zero-carbon fuel for vehicles. However, both the internal combustion engine (ICE) and the proton exchange membrane fuel cell (PEMFC), the currently available engines used by the vehicle, require hydrogen decomposed from ammonia. On-board hydrogen production is an energy-intensive process that significantly reduces system efficiency. Therefore, energy recovery from the system's residual heat is essential to promote system efficiency. ICEs and FCs require different amounts of hydrogen, and they produce residual heat of different quality and quantity, so the system efficiency is not only determined by the engine operating point, but also by the measures and ratios of residual heat recovery. To thoroughly understand the relationships between system energy efficiency and system configuration as well as system parameters, this paper takes three typical power systems with different configurations as our objects. Models of three systems are set up for system energy efficiency analysis, and carry out simulations under different conditions to conduct system output power and energy efficiency. By analyzing the simulation results, the factors that most significantly impact the system efficiency are identified, the guidelines for system design and parameter optimization are proposed.
Newton and Secant Methods for Iterative Remnant Control of Preisach Hysteresis Operators
Authors: Jurrien Keulen, Bayu Jayawardhana
Date: 2024-06-17 00:00:00

We study the properties of remnant function, which is a function of output remnant versus amplitude of the input signal, of Preisach hysteresis operators. The remnant behavior (or the leftover memory when the input reaches zero) enables an energy-optimal application of piezoactuator systems where the applied electrical field can be removed when the desired strain/displacement has been attained. We show that when the underlying weight of Preisach operators is positive, the resulting remnant curve is monotonically increasing and accordingly a Newton and secant update laws for the iterative remnant control are proposed that allows faster convergence to the desired remnant value than the existing iterative remnant control algorithm in literature as validated by numerical simulation.
Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning
Authors: Yuanzhe Geng, Erwu Liu, Wei Ni, Rui Wang, Yan Liu, Hao Xu, Chen Cai, Abbas Jamalipour
Date: 2024-06-17 00:00:00
Tasks: Multi-agent Reinforcement Learning

This paper aims to balance performance and cost in a two-hop wireless cooperative communication network where the source and relays have contradictory optimization goals and make decisions in a distributed manner. This differs from most existing works that have typically assumed that source and relay nodes follow a schedule created implicitly by a central controller. We propose that the relays form an alliance in an attempt to maximize the benefit of relaying while the source aims to increase the channel capacity cost-effectively. To this end, we establish the trade problem as a Stackelberg game, and prove the existence of its equilibrium. Another important aspect is that we use multi-agent reinforcement learning (MARL) to approach the equilibrium in a situation where the instantaneous channel state information (CSI) is unavailable, and the source and relays do not have knowledge of each other's goal. A multi-agent deep deterministic policy gradient-based framework is designed, where the relay alliance and the source act as agents. Experiments demonstrate that the proposed method can obtain an acceptable performance that is close to the game-theoretic equilibrium for all players under time-invariant environments, which considerably outperforms its potential alternatives and is only about 2.9% away from the optimal solution.
Low-probability of Intercept/Detect (LPI/LPD) Secure Communications Using Antenna Arrays Employing Rapid Sidelobe Time Modulation
Authors: Jiahao Zhao, Shichen Qiao, John H. Booske, Nader Behdad
Date: 2024-06-17 00:00:00

We present an electronically-reconfigurable antenna array offering low probability of intercept/detect (LPI/LPD) and secure communications capabilities simultaneously at the physical layer. This antenna array is designed to provide rapidly time-varying sidelobes and a stationary main lobe. By performing rapid sidelobe time modulation (SLTM), the signal transmitted in the undesired directions (i.e., through sidelobes) undergoes spread-spectrum distortion making it more difficult to be detected, intercepted, and deciphered while the signal transmitted in the desired direction (i.e., through the main lobe) is unaffected. Therefore, the intended receiver would not need additional modifications (i.e. encryption keys) to detect and recover the signal. We describe the operating principles of this SLTM array and validate its spread-spectrum SLTM sequence generation in undesired directions through theory, simulations, and experiments. Using a fabricated SLTM prototype operating at X band, we conducted system-level measurements to demonstrate its LPI/LPD, secure communications, and jamming resilience capabilities. The presented method is a physical layer technique, which can bring LPI/LPD capabilities to existing communications systems by simply replacing their antennas with SLTM arrays. This technique can be used independently or in combination with additional coding and signal-processing techniques to achieve further enhancements in LPI/LPD and secure communications.
QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach
Authors: Long He, Geng Sun, Zemin Sun, Qingqing Wu, Jiawen Kang, Dusit Niyato, Zhu Han, Victor C. M. Leung
Date: 2024-06-17 00:00:00
Tasks: Edge-computing, Trajectory Planning

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches.
Dynamic Modeling and Control for an Offshore Semisubmersible Floating Wind Turbine
Authors: Yingjie Gong, Qinmin Yang, Hua Geng, Wenchao Meng, Lin Wang
Date: 2024-06-17 00:00:00
Tasks: continuous-control, Continuous Control

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies.
Parametric Resonance in Networked Oscillators
Authors: Karthik Chikmagalur, Bassam Bamieh
Date: 2024-06-17 00:00:00

We investigate parametric resonance in oscillator networks subjected to periodically time-varying oscillations in the edge strengths. Such models are inspired by the well-known parametric resonance phenomena for single oscillators, as well as the potential rich phenomenology when such parametric excitations are present in a variety of applications like deep brain stimulation, AC power transmission networks, as well as vehicular flocking formations. We consider cases where a single edge, a subgraph, or the entire network is subjected to forcing, and in each case, we characterize an interesting interplay between the parametric resonance modes and the eigenvalues/vectors of the graph Laplacian. Our analysis is based on a novel treatment of multiple-scale perturbation analysis that we develop for the underlying high-dimensional dynamic equations.
Design of Interacting Particle Systems for Fast Linear Quadratic RL
Authors: Anant A Joshi, Heng-Sheng Chang, Amirhossein Taghvaei, Prashant G Mehta, Sean P. Meyn
Date: 2024-06-16 00:00:00
Tasks: Reinforcement Learning (RL)

This paper is concerned with the design of algorithms based on systems of interacting particles to represent, approximate, and learn the optimal control law for reinforcement learning (RL). The primary contribution is that convergence rates are greatly accelerated by the interactions between particles. Theory focuses on the linear quadratic stochastic optimal control problem for which a complete and novel theory is presented. Apart from the new algorithm, sample complexity bounds are obtained, and it is shown that the mean square error scales as $1/N$ where $N$ is the number of particles. The theoretical results and algorithms are illustrated with numerical experiments and comparisons with other recent approaches, where the faster convergence of the proposed algorithm is numerically demonstrated.
Reachability Analysis for Linear Systems with Uncertain Parameters using Polynomial Zonotopes
Authors: Yushen Huang, Ertai Luo, Stanley Bak, Yifan Sun
Date: 2024-06-16 00:00:00

In real world applications, uncertain parameters are the rule rather than the exception. We present a reachability algorithm for linear systems with uncertain parameters and inputs using set propagation of polynomial zonotopes. In contrast to previous methods, our approach is able to tightly capture the non-convexity of the reachable set. Building up on our main result, we show how our reachability algorithm can be extended to handle linear time-varying systems as well as linear systems with time-varying parameters. Moreover, our approach opens up new possibilities for reachability analysis of linear time-invariant systems, nonlinear systems, and hybrid systems. We compare our approach to other state of the art methods, with superior tightness on two benchmarks including a 9-dimensional vehicle platooning system. Moreover, as part of the journal extension, we investigate through a polynomial zonotope with special structure named multi-affine zonotopes and its optimization problem. We provide the corresponding optimization algorithm and experiment over the examples obatined from two benchmark systems, showing the efficiency and scalability comparing to the state of the art method for handling such type of set representation.
Polynomial Chaos-based Stochastic Model Predictive Control: An Overview and Future Research Directions
Authors: Prabhat K. Mishra, Joel A. Paulson, Richard D. Braatz
Date: 2024-06-15 00:00:00
Tasks: Model Predictive Control

This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SMPC). In the past decade, PCT has been shown to provide a computationally tractable way to perform complete and accurate uncertainty propagation through (smooth) nonlinear dynamic systems. As such, it represents a very useful computational tool for accelerating the computations needed in SMPC with time invariant uncertainties. It turns out that it can also be used to reduce complexity of chance constraints, which are an important component of SMPC. In this paper, we provide an overview of PCT and discuss how it can be applied in such time invariant settings.
Intermittent Encryption Strategies for Anti-Eavesdropping Estimation
Authors: Zhongyao Hu, Bo Chen, Pindi Weng, Jianzheng Wang, Li Yu
Date: 2024-06-15 00:00:00

In this paper, an anti-eavesdropping estimation problem is investigated. A linear encryption scheme is utilized, which first linearly transforms innovation via an encryption matrix and then encrypts some components of the transformed innovation. To reduce the computation and energy resources consumed by the linear encryption scheme, both stochastic and deterministic intermittent strategies which perform the linear encryption scheme only at partial moments are developed. When the system is stable, it is shown that the mean squared error (MSE) of the eavesdropper converges under any stochastic or deterministic intermittent strategy. Also, an analytical encryption matrix that maximizes the steady-state of the MSE is designed. When the system is unstable, the eavesdropper's MSE can be unbounded with arbitrary positive encryption probabilities and decision functions if encryption matrices are chosen appropriately. Then, the relationship between the aforementioned encryption parameters and the eavesdropper's MSE is analyzed. Moreover, a single intermittent strategy which only encrypts one message is discussed. This strategy can be unavailable for stable systems, but can make the eavesdropper's MSE unbounded in unstable systems for the encrypted message satisfies a linear matrix inequality (LMI) condition. The effectiveness of the proposed methods is verified in the simulation.