Grid-forming converters are widely envisioned to be the cornerstone of future converter-dominated power systems. However, standard grid forming (GFM) control strategies assume a fully controllable source with enough h...
Grid-forming converters are widely envisioned to be the cornerstone of future converter-dominated power systems. However, standard grid forming (GFM) control strategies assume a fully controllable source with enough headroom behind the converter and only implicitly address renewable generation limits through the converter limits. This can lead to instabilities on time scales of both primary and secondary frequency control and jeopardize the safe and reliable operation of electric power systems. In this work, we leverage the recently proposed dual-port GFM control that maps power imbalances in the grid to the power generation interfaced by the power converter. We show that this mechanism allows for considering and transparently addressing limits of renewable generation (e.g., solar photovoltaics and wind) in primary and secondary frequency control. We illustrate that renewable generation using dual-port GFM control can directly integrate into prevailing secondary control methods such as automatic generation control (AGC). Moreover, we discuss the limitations of standard AGC when one or more areas of a power system are dominated by renewable generation and propose an anti-windup strategy to address the power generation limits of renewables. Finally, we verify our findings in a time-domain, electromagnetic transient (EMT) simulation.
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge co...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.
This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, ena...
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An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore,...
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The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge co...
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For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during...
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ISBN:
(数字)9798331528096
ISBN:
(纸本)9798331528102
For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application.
This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, ...
This paper presents a learning-based methodology for developing an optimal lane-changing control policy for a Remote controlled (RC) car using real-time sensor data. The RC car is equipped with sensors including GPS, IMU devices, and a camera integrated in an Nvidia Jetson AGX Xavier board. By a novel Adaptive Dynamic Programming (ADP) algorithm, our RC car is capable of learning the optimal lane-changing strategies based on the real-time processed measurement from the sensors. The experimental outcomes show that our learning-based control algorithm can be effectively implemented, adapt to parameter changes, and complete the lane changing tasks in a short learning time with satisfactory performance.
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interp...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyzing intricate sensor data, and making decisions within split seconds to ensure safety through numerous computer vision tasks. In this paper, we present a new real-time multi-task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer — the prevalent issue in multi-task learning — we introduce a task-adaptive attention generator. This generator is designed to automatically discern interrelations across the three tasks and arrange the task-sharing pattern, all while leveraging the efficiency of the hard-parameter sharing approach. To the best of our knowledge, the proposed model is pioneering in its capability to concurrently handle multiple tasks, notably 3D object detection, while maintaining real-time processing speeds. Our rigorously optimized network, when tested on the Cityscapes-3D datasets, consistently outperforms various base-line models. Moreover, an in-depth ablation study substantiates the efficacy of the methodologies integrated into our framework.
The Hilbert–space Gaussian Process (hgp) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (gp) inference by projecting the gp onto M basis functions. These pr...
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Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to add...
Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to address the time delay between the driver and the vehicle. Collision-free path generation has emerged as a vital technique facilitating both teleoperation and autonomous driving, particularly in high-level path planning for vehicles. In the context of real-time teleoperation, a generated collision-free path serves as a valuable guide for the teleoperator, effectively mitigating the impact of time delay. In this research, we present a framework dubbed dual transformer network (DTNet), designed to cater to the needs of teleoperation by addressing road scene understanding. The proposed DTNet employs two transformer-based networks to effectively segment the road free space and detect road objects. Additionally, we introduce an innovative fusion mechanism that leverages the combined information from both networks to predict a collision-free path. The efficacy of the DTNet is extensively evaluated using a large-scale BDD100k dataset, substantiating its superior performance in road free space segmentation and road object detection tasks. Remarkably, DTNet achieves a mean intersection over union score of 83.89% for road free space segmentation and an impressive mean average precision score of 34.20% for road object detection. The experimental findings affirm the effectiveness of the DT-Net framework in addressing the challenges of road scene understanding, making it a promising solution to provide a robust and efficient approach for collision-free path generation, with broader implications for the advancement of autonomous driving technologies.
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