检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Safe Reinforcement Learning via Confidence-Based Filters

arXiv

引用

arXiv 2022年

作者： Curi, Sebastian Lederer, Armin Hirche, Sandra Krause, Andreas Learning & Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Germany

— Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach for certifying state safety constraints for nominal policies learnt via standard RL techniques, based on probabilistic dynamics models. Our approach is based on a reformulation of state constraints in terms of cost functions, reducing safety verification to a standard RL task. By exploiting the concept of hallucinating inputs, we extend this formulation to determine a "backup" policy which is safe for the unknown system with high probability. Finally, the nominal policy is minimally adjusted at every time step during a roll-out towards the backup policy, such that safe recovery can be guaranteed afterwards. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach. © 2022, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Learning Unknown Lagrange Dynamical systems with Guaranteed Persistency of Excitation

Learning Unknown Lagrange Dynamical Systems with Guaranteed ...

引用

International Conference on systems and control (ICSC)

作者： A. Samanis P. S. Trakas X. Papageorgiou K. J. Kyriakopoulos C. P. Bechlioulis Control Systems Lab School of Mechanical Engineering National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (数字)9781665465076

ISBN: (纸本)9781665465083

In this paper, we present a methodology that ensures a priori that all possible unknown dynamics of the system within a compact set of operation will be excited. A controller is used to make sure that the system with unknown dynamics will follow the reference trajectory and Radial Basis Function (RBF) neural networks are employed to estimate the unknown nonlinearities. The persistency of excitation condition is guaranteed as a prerequisite to achieve accurate estimation of the unknown nonlinear terms and efficient learning. A simulation example clarifies the proposed approach and verifies the aforementioned assertions.

关键词： Neural networks Estimation control systems Trajectory Nonlinear dynamical systems

来源：评论

学校读者我要写书评

暂无评论

Exploring the Economic Feasibility of Advanced Air Mobility in the Early Stages

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4826-4830页

作者： Jingqiu Guo Long Chen Lingxi Li Xiaoxiang Na Ljubo Vlacic Fei-Yue Wang Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University–Purdue University Indianapolis (IUPUI) Indianapolis IN USA Department of Engineering University of Cambridge Cambridge U.K. Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith University Nathan QLD Australia State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Engineering Macau University of Science and Technology Macau China

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

关键词： Active appearance model Costs Economics Transportation Helicopters Aircraft propulsion Public transportation

来源：评论

学校读者我要写书评

暂无评论

A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

PID controller Machine Learning Algorithm Applied to the Mathematical Model of Quadrotor Lateral Motion 6

PID Controller Machine Learning Algorithm Applied to the Mat...

引用

6th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2021

作者： Kucherov, Dmytro Kozub, Andrei Tkachenko, Valerii Rosinska, Galina Poshyvailo, Olexii Faculty of Cybersecurity Computer and Software Engineering National Aviation University Department of Computerized Control Systems Kyiv Ukraine National Space Facilities Control and Test Center State Space Agency of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665438223

The paper discusses the problem of automatic tuning of the PID controller. The auto-tuning algorithm of the PID controller based on one machine learning method, which is equivalent to the steepest descent, is proposed. A feature of the proposed approach is the vector adjustment of the PID controller parameters, where the angular direction of descent is taken into account at first, and the step sizes are made if taking into account the increased accuracy of the approximation near the optimum point. Modeling performed in the Matlab environment confirms the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Three term control systems

来源：评论

学校读者我要写书评

暂无评论

Robust Prescribed Performance control and Adaptive Learning for the Longitudinal Dynamics of Fixed-Wing UAVs

Robust Prescribed Performance Control and Adaptive Learning ...

引用

International Conference on systems and control (ICSC)

作者： S. Tzeranis P. S. Trakas X. Papageorgiou K. J. Kyriakopoulos C. P. Bechlioulis Control Systems Lab School of Mechanical Engineering National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (数字)9781665465076

ISBN: (纸本)9781665465083

The objective of this work is to simultaneously control and identify the nonlinear longitudinal dynamics of small-scale fixed-wing Unmanned Aerial Vehicles (UAVs). The main difficulty in this endeavor lies in the satisfaction of the Persistence of Excitation (PE) condition, which eventually ensures accurate learning. Towards this direction, our key components comprise Radial Basis Function - Neural Networks (RBF-NNs), which are suitable mathematical models for universal function approximation, alongside with: i) the recently developed Dynamic Regression Extension and Mixing (DREM) technique; a new procedure for designing parameter estimators with enhanced performance, as well as ii) a novel control design for the longitudinal UAV dynamics utilizing the Prescribed Performance control (PPC) methodology, which enables robust trajectory tracking with predetermined transient and steady state quality, even in the presence of model uncertainties.

关键词： Uncertainty Trajectory tracking Neural networks Autonomous aerial vehicles Mathematical models Steady-state Object recognition

来源：评论

学校读者我要写书评

暂无评论

control of Reaction-Diffusion Processes Under Communication Delays

Control of Reaction-Diffusion Processes Under Communication ...

引用

European control Conference (ECC)

作者： Luca Ballotta Juncal Arbelaiz Vijay Gupta Luca Schenato Mihailo R. Jovanović Delft Center for Systems and Control Delft University of Technology Delft The Netherlands Department of Mechanical and Aerospace Engineering Center for Statistics and Machine Learning Princeton University Princeton NJ USA Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Information Engineering University of Padova Padova Italy Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper we investigate the design of optimal spatially distributed controllers for a linear and spatially invariant reaction-diffusion process over the real line. The controller receives state measurements from different spatial locations with non-negligible delays. In this set-up and for the class of proportional spatially invariant state feedback controllers, the optimal control synthesis problem is equivalent to a feedback gain optimization for a spatially distributed delay system. We show that the spatial locality of optimal feedback gains is affected not only by diffusion and reaction coefficients, but also by the parameter representing communication time-delay that causes a sharp flattening of the control gains. In the expensive control regime, the optimal controller is solved analytically, yielding some practical design guidelines.

关键词： State feedback Delay systems Design methodology Process control Optimal control Europe Delays

来源：评论

学校读者我要写书评

暂无评论

Towards reliable data-based optimal and predictive control using extended DMD

引用

IFAC-PapersOnLine 2023年第1期56卷 169-174页

作者： Manuel Schaller Karl Worthmann Friedrich Philipp Sebastian Peitz Feliks Nüske Technische Universität Ilmemau Institute of Mathematics Optimization-based Control group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.

关键词： Approximation error data-based dictionary size eDMD estimation error finite data Koopman predicted control projection error optimal control

来源：评论

学校读者我要写书评

暂无评论

Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

arXiv

引用

arXiv 2025年

作者： de Heij, Vincent Niazi, M.B. Umar Johansson, Karl H. Ahmed, Saeed Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper proposes a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified. Copyright © 2025, The Authors. All rights reserved.

关键词： Digital arithmetic

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：