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Robust Voltage control in DC Networks with Uncertain Nonlinear Load Dynamics

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Robust Voltage Control in DC Networks with Uncertain Nonline...

IEEE Conference on Decision and control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

Efficient Self-Learning Evolutionary Neural Architecture Search

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SSRN

SSRN 2023年

作者： Qiu, Zhengzhong Bi, Wei Xu, Dong Guo, Hua Ge, Hongwei Liang, Yanchun Lee, Heow Pueh Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun130012 China YGSOFT INC Guangdong Zhuhai519085 China Department of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States College of Computer Science and Technology Dalian University of Technology Liaoning Dalian116081 China School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution adopted by the traditional evolutionary algorithm makes the algorithm unable to control the increase or decrease of the individual architecture size and thus cannot guarantee the lightweight and inference efficiency of the candidate architecture. In addition, the existing approaches cannot learn the optimal sampling probability distribution relevant to the specific problem from the empirical information accumulated through the search process. What’s more, the algorithm needs to evaluate the performance of all the individual architectures, which requires huge computing resources and time overhead. To overcome these problems and challenges, an Efficient Self-learning Evolutionary Neural Architecture Search method, called ESE-NAS, is presented in this paper. Firstly, we propose a Model Size control module, which generates the probability distribution for sampling the type of mutation operators according to the current model size, so as to control both the node number of network architecture and the sparsity of links, therefore ensuring that neural architectures can remain compact and efficient as they evolve. Thereafter, a Mutation Candidate Credit Assignment method is proposed, which enables the algorithm to dynamically adjust the probability distribution for available types of node operations and directed links according to the empirical information from individuals’ performance evaluation, which can guide the evolutionary direction of neural architecture and shorten the first hitting time of the optimal architecture. Finally, a neural architecture performance predictor is formulated, and the accuracy of each individual architecture is predicted by using the simplified input features, to further improve the efficiency of neural architecture search. Experiments show that the ESE-NAS method proposed in this paper can learn a highly i

关键词： Evolutionary algorithms

Homodyne measurement with a Schrödinger cat state as a local oscillator

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Physical Review A 2022年第6期106卷 063706-063706页

作者： Joshua Combes Austin P. Lund Department of Electrical Computer and Energy Engineering University of Colorado Boulder Colorado 80309 USA Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany Centre for Quantum Computation and Communications Technology School of Mathematics and Physics The University of Queensland St Lucia QLD Australia

Homodyne measurements are a widely used quantum measurement. Using a coherent state of large amplitude as the local oscillator, it can be shown that the quantum homodyne measurement limits to a field quadrature measurement. In this work, we give an example of a general idea: injecting nonclassical states as a local oscillator can lead to nonclassical measurements. Specifically, we consider injecting a superposition of coherent states, a Schrödinger cat state, as a local oscillator. We derive the Kraus operators and the positive operator-valued measure in this situation.

关键词： Optics & lasers Quantum engineering Quantum optics Quantum sensing

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTIMATION 9

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COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTI...

9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

Minimum structural sensor placement for switched linear time-invariant systems and unknown inputs

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arXiv 2021年

作者： Reed, Emily A. Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Electrical and Computer Engineering Department University of Southern California United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal University of Southern California Ming Hsieh Electrical and Computer Engineering Department Delft University of Technology Delft Center for Systems and Control

In this paper, we study the structural state and input observability of continuous-time switched linear time- invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in O((m(n + p))2), where n is the number of state variables, p is the number of unknown inputs, and m is the number of modes. Moreover, we address the minimum sensor placement problem for these systems by adopting a feed-forward analysis and by providing an algorithm with a computational complexity of O((m(n + p) + α)2.373), where α is the number of target strongly connected components of the system's digraph representation. Lastly, we explore different assumptions on both the system and unknown inputs (latent space) dynamics that add more structure to the problem, and thereby, enable us to render algorithms with lower computational complexity, which are suitable for implementation in large-scale systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Time varying control systems

Fault-tolerant quantum computation using large spin cat-codes

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arXiv 2024年

作者： Omanakuttan, Sivaprasad Buchemmavari, Vikas Gross, Jonathan A. Deutsch, Ivan H. Marvian, Milad Center for Quantum Information and Control University of New Mexico AlbuquerqueNM United States Department of Physics and Astronomy University of New Mexico AlbuquerqueNM United States Google Quantum AI VeniceCA90291 United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM87131 United States

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the CNOT gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical CNOT gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving CNOT gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing. © 2024, CC0.

关键词： Qubits

A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks

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arXiv 2021年

作者： Reed, Emily A. Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Electrical and Computer Engineering Department University of Southern California United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft University of Technology Delft Center for Systems and Control

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learning and control theory problems. On the one hand, SCCs are used in solving the 2-satisfiability problem, which has applications in clustering, scheduling, and visualization. On the other hand, the diameter has applications in network learning and discovery problems enabling efficient internet routing and searches, as well as identifying faults in the power grid. In this paper, we leverage consensus-based principles to find the SCCs in a scalable and distributed fashion with a computational complexity of O (Ddmax in-degree ) , where D is the (finite) diameter of the network and dmax in-degree is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in D + 1 iterations, which allows us to retrieve the diameter of the network. We illustrate the performance of our algorithm on several random networks, including Erdos-Rényi, Barabási- Albert, and Watts-Strogatz networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

Super Twisting Sliding Mode control Strategy for a Three-Level T-Type Dual MPPT Inverter

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Super Twisting Sliding Mode Control Strategy for a Three-Lev...

Compatibility in Power Electronics (CPE)

作者： Farzaneh Bagheri Naki Guler Marco Rivera Hasan Komurcugil Department of Electrical and Electronic Engineering Antalya Bilim University Antalya Turkey Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Power Electronics Machines and Control (PEMC) Research Institute University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Académica Universidad de Talca Curicó Chile Department of Computer Engineering Eastern Mediterranean University (EMU) Famagusta Turkey

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Nowadays Residential and small commercial power systems are taking advantage of the dual maximum power point tracking (MPPT) inverters, optimizing energy harvesting from two individual solar panel arrays to increase performance and energy output. Three-level T-type inverters are also famous due to their lower total harmonic distortion, high efficacy, and lower voltage stress on the switch which are a good option to be integrated into the dual MPPT solar plants. So, this article incorporates the dual MPPT solar plants with a T-Type grid-connected inverter which uses a double super twisting sliding mode control (STSMC) to control the injected currents to the grid in d and q reference frames. The STSMC mitigates chattering while showing robustness under model variations and dynamic response. The efficiency of the proposed control method under different irradiation values is verified through simulation results.

关键词： Maximum power point trackers Total harmonic distortion Switches Inverters Robustness Real-time systems Solar panels Time factors Voltage control Sliding mode control

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality