摘要翻译:
人们通常希望有正式分析大规模系统的能力--然而,通常情况下,人们可以正式分析一个相当小的系统,也可以非正式地分析一个大规模系统。本工作试图进一步弥合线性系统可达性分析的这一性能差距。可达性分析可以捕捉动态系统的整套可能解,从而证明不安全状态永远不会达到;这需要充分考虑任意变化的不确定输入,因为传感器噪声或干扰通常不遵循任何模式。本文利用Krylov方法计算线性大系统的可达集。Krylov方法已经用于可达性分析,但是我们克服了以前输入必须是(分段)常数的限制。因此,对于有界但任意变化的输入,我们可以计算具有数千个状态变量的系统的可达集。
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英文标题:
《Reachability Analysis of Large Linear Systems with Uncertain Inputs in
the Krylov Subspace》
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作者:
Matthias Althoff
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最新提交年份:
2020
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分类信息:
一级分类:Mathematics 数学
二级分类:Numerical Analysis 数值分析
分类描述:Numerical algorithms for problems in analysis and algebra, scientific computation
分析和代数问题的数值算法,科学计算
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一级分类:Computer Science 计算机科学
二级分类:Numerical Analysis 数值分析
分类描述:cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
cs.na是Math.na的别名。大致包括ACM学科类G.1的材料。
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一级分类:Computer Science 计算机科学
二级分类:Systems and Control 系统与控制
分类描述:cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究,涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制,以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类:Electrical Engineering and Systems Science 电气工程与系统科学
二级分类:Systems and Control 系统与控制
分类描述:This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究,涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制,以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要:
One often wishes for the ability to formally analyze large-scale systems---typically, however, one can either formally analyze a rather small system or informally analyze a large-scale system. This work tries to further close this performance gap for reachability analysis of linear systems. Reachability analysis can capture the whole set of possible solutions of a dynamic system and is thus used to prove that unsafe states are never reached; this requires full consideration of arbitrarily varying uncertain inputs, since sensor noise or disturbances usually do not follow any patterns. We use Krylov methods in this work to compute reachable sets for large-scale linear systems. While Krylov methods have been used before in reachability analysis, we overcome the previous limitation that inputs must be (piecewise) constant. As a result, we can compute reachable sets of systems with several thousand state variables for bounded, but arbitrarily varying inputs.
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PDF链接:
https://arxiv.org/pdf/1712.00369