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control, Robotics and Informatics (ICCRI), International Conference on

作者： Duc Sang Cao Van Hien Ngo Tran Thang Le Van He Ngo Department of Electric Actuators & Automation Control Automation in Production and Improvement of Technology Institute Ha Noi Vietnam Department of Mechanical Engineering Hanoi University of Science and Technology Ha Noi Vietnam Department of Integrated System Control Automation in Production and Improvement of Technology Institute Ha Noi Vietnam

ISBN: (数字)9798350380309

ISBN: (纸本)9798350380316

Ocean engineering demands systemic solutions for mission-critical demands and technological challenges. From an industrial control perspective, development engineers need to factor in both costs and established standards to ensure the efficient implementation of the control system development lifecycle within a reasonable budget. Customization and reusability play key roles in driving down costs, resources, and development time when creating new applications. In the paper, the Unified Architecture Framework (UAF) [5], Model Based Systems engineering (MBSE), combined with real time Unified Modeling Language/Systems Modeling Language (UML/SysML) are merged and tailored to produce an object-oriented (OO) control model. This model is then extensively applied to develop a controller for Autonomous Underwater Vehicles (AUVs). To reach this objective, the dynamic model of an Autonomous Underwater Vehicle (AUV) are linked with specific specializations of UAF/MBSE. In this setup, the model of analysis is precisely outlined through the detailed description of a use case model, aimed at closely capturing the intricate control demands. The designed model is then specialized by using the real-time UML/SysML to retain key blocks, which are used to describe in detail structures for control and behaviors of AUV. Next, the designed model is transformed into the model of implementation via round-trip engineering, facilitating the quick realization of an AUV controller. Building upon this object-oriented model, a controller for course-tracking applied to a miniature AUV was designed. The results from the simulation demonstrate the good feasibility of the proposed model.

关键词： Autonomous underwater vehicles Analytical models Costs Object oriented modeling Unified modeling language Real-time systems Modeling Vehicle dynamics Standards Robots

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Reward Drops in Learning-based control with an Experimental Validation on Microdrones

Reward Drops in Learning-based Control with an Experimental ...

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IEEE Conference on Decision and control

作者： Nejat Tukenmez Filippos Fotiadis José M. Magalhães Júnior Kyriakos G. Vamvoudakis Seta O. Bogosyan Mechatronics Engineering Department Istanbul Technical University Istanbul Türkiye School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Control and Automation Engineering Department Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider a computationally efficient learning-based control mechanism dealing with dense reward processing for a zero-sum game. The problem has been formulated as the online learning of the Nash equilibrium without requiring any information on the system dynamics. It has firstly been constructed as an infinite horizon optimal control problem, then as an online model-free Q-learning framework, which is composed of critic and actor networks (i.e., for the control and disturbance input). The closed-loop system is also proved to have a stable equilibrium point even in the presence of reward drops. The efficacy of the learning-based controller has been validated through simulations and experiments on micro drones.

关键词： Q-learning System dynamics Heuristic algorithms Process control Nash equilibrium Real-time systems Nonlinear dynamical systems Infinite horizon Closed loop systems Quadrotors

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Reinforcement Learning of Threshold Policy for Dynamic Wireless Charging with Distributed Energy Resources

Reinforcement Learning of Threshold Policy for Dynamic Wirel...

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IEEE General Meeting Power& Energy Society

作者： Liangliang Hao Yunjian Xu Lang Tong Department of Control Science and Engineering Shandong University Jinan China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong SAR School of Electrical and Computer Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

We study dynamic wireless charging for Electric Vehicles (EVs) on electrified highways where EVs are charged while in motion. Our focus is on the cost-minimization scheduling of EV charging based on EV mobility states, charging demands, travel plans, available distributed renewables, and power constraints of the distribution systems. As a Markov decision process with uncountable action space that couples stochasticities in charging demands, vehicle mobilities, and renewable resources, standard dynamic programming and approximation approaches face exponentially growing complexity and modeling uncertainties. By exploiting the linear network topology when EVs are on a highway, this work reveals a threshold structure of the optimal charging policy and develops a reinforcement learning approach for uncertainty models, resulting in a scalable EV charging solution. The proposed EV charging algorithm outperforms multiple charging benchmarks by 4.7-52.4% cost reduction.

关键词： Road transportation Renewable energy sources Uncertainty Costs Markov decision processes Inductive charging Dynamic scheduling Electric vehicle charging Distributed power generation Vehicle dynamics

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A Smart Grid Traffic Anomaly Detector Based on Deep Learning

A Smart Grid Traffic Anomaly Detector Based on Deep Learning

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2022 International Conference on Frontiers of Communications, Information System and Data Science, CISDS 2022

作者： Xiong, Wei Lei, Yu Zhou, Yuqing Zhang, Yachao Wei, Su Liu, Zhe State Grid Chongqing Electric Power Company Automation Department of Control Center Chongqing China Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9781665464468

The information network of the power system is an important part of the long- term continuous and effective operation in power industry. The complex network structure between the power grid and the information network in the smart grid brings a great challenge to the abnormal detection of network flow in the security of information communication network. However, traditional machine learning algorithms often have shortcomings such as low detection accuracy and poor real-time performance in solving the problem of network flow anomaly detection, and they are not possible to provide a high-precision early warning function for network security events in advance. In this paper, we introduce the principle of traffic anomaly detection technology based on intrusion detection, and propose a deep learning predictor for abnormal traffic in grid system. By learning the temporal and spatial features of traffic data, deep learning predictor can predict traffic features in the next period and classify traffic security events for the predicted traffic. To a certain extent, it can meet the demand of abnormal traffic detection of smart grid servers, so as to achieve the purpose of improving power grid information security. © 2022 IEEE.

关键词： Smart power grids

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Hybrid Oscillation Damping and Inertia Management for Distributed Energy Resources

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IEEE Transactions on Power Systems 2025年

作者： Feng, Cheng Huang, Linbin He, Xiuqiang Wang, Yi Dorfler, Florian Kang, Chongqing Tsinghua University Department of Electrical Engineering Beijing100084 China Zhejiang University College of Electrical Engineering Hangzhou310027 China Tsinghua University Department of Automation Beijing100084 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Automatic Control Laboratory ETH Zurich8092 Switzerland

Power systems dominated by converter-interfaced distributed energy resources (DERs) typically exhibit weaker damping capabilities and lower inertia, compromising system stability. Although individual DER controllers are evolving to provide superior oscillation damping capabilities and inertia supports, there is a lack of network-wide coordinated management measures for multiple DERs, potentially leading to unexpected instability and cost-effectiveness problems. To address this gap, this paper introduces a hybrid oscillation damping and inertia management strategy for multiple DERs, considering network coupling effects, and seeks to encourage DERs to provide enhanced damping and inertia with appropriate economic incentives. We first formulate an optimization problem to tune and allocate damping and inertia coefficients for DERs, minimizing associated power and energy costs while ensuring hard constraints for system frequency stability and small-signal stability. The problem is built upon a novel convex parametric formulation that integrates oscillation mode location and frequency trajectory requirements, equipped with a theoretical guarantee, and eliminating the need for iterative tuning and computation burdens. Furthermore, to increase the willingness of DERs to cooperate, we further design appropriate economic incentives to compensate for DERs' costs based on the proposed cost minimization problem, and assess its impact on system cost-efficiency. Numerical tests highlight the effectiveness of the proposed method in promoting system stability and offer insights into potential economic benefits. © 1969-2012 IEEE.

关键词： Damping

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Distributed Least-Squares Optimization Solvers with Differential Privacy

arXiv

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arXiv 2024年

作者： Liu, Weijia Wang, Lei Guo, Fanghong Wu, Zhengguang Su, Hongye Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Zhejiang University of Technology Hangzhou China

This paper studies the distributed least-squares optimization problem with differential privacy requirement of local cost functions, for which two differentially private distributed solvers are proposed. The first is established on the distributed gradient tracking algorithm, by appropriately perturbing the initial values and parameters that contain the privacy-sensitive data with Gaussian and truncated Laplacian noises, respectively. Rigorous proofs are established to show the achievable tradeoff between the (ǫ, δ)-differential privacy and the computation accuracy. The second solver is established on the combination of the distributed shuffling mechanism and the average consensus algorithm, which enables each agent to obtain a noisy version of parameters characterizing the global gradient. As a result, the least-squares optimization problem can be eventually solved by each agent locally in such a way that any given (ǫ, δ)-differential privacy requirement can be preserved while the solution may be computed with the accuracy independent of the network size, which makes the latter more suitable for large-scale distributed least-squares problems. Numerical simulations are presented to show the effectiveness of both solvers. © 2024, CC BY-NC-SA.

关键词： Parameter estimation

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From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models

arXiv

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arXiv 2024年

作者： Pulli, Tessa Thalhammer, Stefan Schwaiger, Simon Vincze, Markus Vision for Robotics Laboratory Automation and Control Institute TU Wien Austria Industrial Engineering Department UAS Technikum Vienna Austria

Robots are increasingly envisioned to interact in real-world scenarios, where they must continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose estimators determine poses without prior knowledge. Recently, vision language models (VLMs) have shown considerable advances in robotics applications by establishing an understanding between language input and image input. In our work, we take advantage of VLMs zero-shot capabilities and translate this ability to 6D object pose estimation. We propose a novel framework for promptable zero-shot 6D object pose estimation using language embeddings. The idea is to derive a coarse location of an object based on the relevancy map of a language-embedded NeRF reconstruction and to compute the pose estimate with a point cloud registration method. Additionally, we provide an analysis of LERF’s suitability for open-set object pose estimation. We examine hyperparameters, such as activation thresholds for relevancy maps and investigate the zero-shot capabilities on an instance- and category-level. Furthermore, we plan to conduct robotic grasping experiments in a real-world setting. © 2024, CC BY.

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Reinforcement Learning-Based Fault-Tolerant control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation Industrial control

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Feedback-Based Quantum Algorithm for Excited States Calculation

arXiv

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arXiv 2024年

作者： Rahman, Salahuddin Abdul Karabacak, Özkan Wisniewski, Rafal Automation and Control section Department of electronic systems Aalborg University Aalborg Denmark Department of Mechatronics Engineering Kadir Has University Istanbul Turkey

Recently, feedback-based quantum algorithms have been introduced to calculate the ground states of Hamiltonians, inspired by quantum Lyapunov control theory. This paper aims to generalize these algorithms to the problem of calculating an eigenstate of a given Hamiltonian, assuming that the lower energy eigenstates are known. To this aim, we propose a new design methodology that combines the layer-wise construction of the quantum circuit in feedback-based quantum algorithms with a new feedback law based on a new Lyapunov function to assign the quantum circuit parameters. We present two approaches for evaluating the circuit parameters: one based on the expectation and overlap estimation of the terms in the feedback law and another based on the gradient of the Lyapunov function. We demonstrate the algorithm through an illustrative example and through an application in quantum chemistry. To assess its performance, we conduct numerical simulations and execution on IBM’s superconducting quantum computer. © 2024, CC BY.

关键词： Lyapunov functions

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A Micromesh Multi-Scaled Features Extraction Network for Li-ion Batteries SOH Estimation

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Min Chen, Yitian Guo, Dongxu Xu, Zhiwei University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Tsinghua University School of Vehicle and Mobility Beijing100084 China Shandong University Department of Control Science and Engineering Jinan250061 China

The great progress and wide application of electronic products and electric vehicles have entailed more stringent requirements for the reliability of lithium-ion batteries. The State of Health (SOH) serves as a significant indicator for evaluating the condition of such batteries. However existing methods are lack of refined modeling for sequences and unable to make precise estimation for SOH. In this paper, a micromesh multi-scaled features extraction network (MMFEN) is proposed for accurately estimating SOH. A refined representation block is developed for heterogeneous elaborate feature extraction. Then, a multi-head convolution attention block is constructed to capture multi-scaled efficient state information. To demonstrate the superiority of MMFEN, experiments are conducted on the NASA and CALCE data published online and a real-world electric vehicles (EVs) data set which is collected from existing battery management systems. Comparing with traditional methods, MMFEN achieves remarkable performance with average root mean square error of 1.21% and mean absolute percentage error of 0.99% on NASA samples, 2.78% and 2.71% on CALCE dataset, while 2.39% and 2.08% on EVs data set, respectively. © 1967-2012 IEEE.

关键词： Lithium-ion batteries

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