3期

最新文章

• A Linear Differential Inclusion for Contraction Analysis to Known Trajectories AI翻译
  作者：Harapanahalli, Akash; Coogan, Samuel
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of the system. In this note, we use contraction tools to study the exponential stability of a system to a particular known trajectory, deriving a new LDI characterizing the error betw...
• Sampling-Based Stochastic Data-Driven Predictive Control Under Data Uncertainty AI翻译
  作者：Teutsch, Johannes; Kerz, Sebastian; Wollherr, Dirk; Leibold, Marion
  作者单位：Technical University of Munich
  摘要：We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single PE input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance...
• Invariant and Attracting Sets of Stochastic Partial Functional Differential Systems AI翻译
  作者：Xu, Liguang; Li, Dingshi
  作者单位：University of Shanghai for Science & Technology; Southwest Jiaotong University
  摘要：This article is dedicated to analyzing invariant and attracting sets of stochastic partial functional differential systems. By introducing an approximate system with strong solutions and employing a limit argument along with a proof by reductio ad absurdum, several new sufficient conditions for the existence of invariant and attracting sets of the mild solutions are established. Finally, examples are provided alongside comparisons to existing results to demonstrate the superiority of our obtai...
• A Linear Differential Inclusion for Contraction Analysis to Known Trajectories AI翻译
  作者：Harapanahalli, Akash; Coogan, Samuel
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of the system. In this note, we use contraction tools to study the exponential stability of a system to a particular known trajectory, deriving a new LDI characterizing the error betw...
• Sampling-Based Stochastic Data-Driven Predictive Control Under Data Uncertainty AI翻译
  作者：Teutsch, Johannes; Kerz, Sebastian; Wollherr, Dirk; Leibold, Marion
  作者单位：Technical University of Munich
  摘要：We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single PE input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance...
• Invariant and Attracting Sets of Stochastic Partial Functional Differential Systems AI翻译
  作者：Xu, Liguang; Li, Dingshi
  作者单位：University of Shanghai for Science & Technology; Southwest Jiaotong University
  摘要：This article is dedicated to analyzing invariant and attracting sets of stochastic partial functional differential systems. By introducing an approximate system with strong solutions and employing a limit argument along with a proof by reductio ad absurdum, several new sufficient conditions for the existence of invariant and attracting sets of the mild solutions are established. Finally, examples are provided alongside comparisons to existing results to demonstrate the superiority of our obtai...
• A Linear Differential Inclusion for Contraction Analysis to Known Trajectories AI翻译
  作者：Harapanahalli, Akash; Coogan, Samuel
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Infinitesimal contraction analysis provides exponential convergence rates between arbitrary pairs of trajectories of a system by studying the system's linearization. An essentially equivalent viewpoint arises through stability analysis of a linear differential inclusion (LDI) encompassing the incremental behavior of the system. In this note, we use contraction tools to study the exponential stability of a system to a particular known trajectory, deriving a new LDI characterizing the error betw...
• Sampling-Based Stochastic Data-Driven Predictive Control Under Data Uncertainty AI翻译
  作者：Teutsch, Johannes; Kerz, Sebastian; Wollherr, Dirk; Leibold, Marion
  作者单位：Technical University of Munich
  摘要：We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single PE input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance...
• Invariant and Attracting Sets of Stochastic Partial Functional Differential Systems AI翻译
  作者：Xu, Liguang; Li, Dingshi
  作者单位：University of Shanghai for Science & Technology; Southwest Jiaotong University
  摘要：This article is dedicated to analyzing invariant and attracting sets of stochastic partial functional differential systems. By introducing an approximate system with strong solutions and employing a limit argument along with a proof by reductio ad absurdum, several new sufficient conditions for the existence of invariant and attracting sets of the mild solutions are established. Finally, examples are provided alongside comparisons to existing results to demonstrate the superiority of our obtai...
• Fast and Robust State Estimation and Tracking via Hierarchical Learning AI翻译
  作者：Mclaughlin, Connor; Ding, Matthew; Erdogmus, Deniz; Su, Lili
  作者单位：Northeastern University; University of California System; University of California Berkeley
  摘要：Fast and reliable state estimation and tracking are essential for real-time situation awareness in cyber-physical systems operating in tactical environments or complicated civilian environments. Traditional centralized solutions do not scale well whereas existing fully distributed solutions over large networks suffer slow convergence, and are vulnerable to a wide spectrum of communication failures. In this article, we aim to speed up the convergence and enhance the resilience of state estimati...