2026

最新文章

• 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...
• Analysis of Higher Order Lotka-Volterra Models: Application of S-Tensors and the Polynomial Complementarity Problem AI翻译
  作者：Cui, Shaoxuan; Zhao, Qi; Zhang, Guofeng; Jardon-Kojakhmetov, Hildeberto; Cao, Ming
  作者单位：University of Groningen; Qingdao University of Science & Technology; Hong Kong Polytechnic University; University of Groningen
  摘要：It is known that the effect of species' density on its growth is nonadditive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce higher order interactions, which are able to capture, for example, the indirect effect of one species on a second one correlating to yet a third. Towards this end, we study a general higher order Lotka-Volterra model. We pr...
• Unobservable Systems: No Problem for Noise Identification AI翻译
  作者：Kost, Oliver; Dunik, Jindrich; Puncochar, Ivo; Straka, Ondrej
  作者单位：University of West Bohemia Pilsen
  摘要：This article deals with the noise identification of a linear time-varying stochastic dynamic system described by the state-space model. In particular, the stress is laid on the design of the correlation measurement difference method for estimation of the state and measurement noise covariance matrices for both observable and unobservable systems with possibly unknown input sequence. The method provides unbiased and consistent estimates and is implemented in a publicly available Matlab toolbox ...
• Backward Reachability Analysis of Perturbed Continuous-Time Linear Systems Using Set Propagation AI翻译
  作者：Wetzlinger, Mark; Althoff, Matthias
  作者单位：Technical University of Munich
  摘要：Backward reachability analysis computes the set of states that reach a target set under the competing influence of control inputs and disturbances. Depending on their interplay, the backward reachable set either represents all states that can be steered into the target set or all states that cannot avoid entering it-the corresponding solutions can be used for controller synthesis and safety verification, respectively. A popular technique for backward reachable set computation solves Hamilton-J...
• Partitioning and Observability in Linear Systems via Submodular Optimization AI翻译
  作者：Kazma, Mohamad H.; Taha, Ahmad F.
  作者单位：Vanderbilt University
  摘要：Network partitioning has gained recent attention as a pathway to enable decentralized operation and control in large-scale systems. This article addresses the interplay between partitioning, observability, and sensor placement (SP) in dynamic networks. The problem, being computationally intractable at scale, is a largely unexplored, open problem in the literature. To that end, this article's objective is designing scalable partitioning of linear systems while maximizing observability metrics o...
• Tannenbaum's Gain-Margin Optimization Meets Polyak's Heavy-Ball Algorithm AI翻译
  作者：Wu, Wuwei; Chen, Jie; Jovanovic, Mihailo R.; Georgiou, Tryphon T.
  作者单位：City University of Hong Kong; University of Southern California; University of California System; University of California Irvine
  摘要：This article highlights an apparent, yet relatively unknown link between algorithm design in optimization theory and controller synthesis in robust control. Specifically, quadratic optimization can be recast as a regulation problem within the framework of H-infinity control. From this vantage point, the optimality of Polyak's fastest heavy-ball algorithm can be ascertained as a solution to a gain-margin optimization problem. The approach is independent of Polyak's original and brilliant argume...
• Maximizing Markov Trajectory Entropy Under Kemeny Constraints for Robotic Surveillance AI翻译
  作者：Duan, Xiaoming; Wang, Weizhen; Yan, Rui
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Beihang University
  摘要：We study trajectory-entropy maximization for Markov chains (MCs) under a Kemeny-constant constraint, given a fixed graph topology and stationary distribution, where the trajectory entropy is the weighted average of the entropy of trajectories between every pair of states, with the weights equal to the product of the stationary probabilities of the initial and final states. This problem is motivated by the application of MCs in the stochastic robotic surveillance, where unpredictability is a de...
• Algebraic Criterion for Nonlinear Unknown Input Observability in Systems Without Control Inputs AI翻译
  作者：Martinelli, Agostino
  摘要：This article investigates nonlinear systems driven solely by unknown inputs. A straightforward definition of observability is introduced for such systems, and based on this, this article derives the algebraic criterion that characterizes state observability. The criterion is easy to apply and well-suited for complex scenarios, as demonstrated by its ability to consistently yield new insights across various domains. Specifically, this article applies this criterion to revisit the structure-from...
• Unifying Direct and Indirect Learning for Safe Control of Linear Systems AI翻译
  作者：Modares, Amir; Ghiasi, Niyousha; Kiumarsi, Bahare; Modares, Hamidreza
  作者单位：University of Cyprus; Michigan State University
  摘要：This article develops learning-enabled safe controllers for linear systems subject to system uncertainties and bounded disturbances. Given the disturbance zonotope, the data-based closed-loop dynamics (CLDs) are first characterized using a matrix zonotope (MZ), and refined through several steps to yield a constrained matrix zonotope (CMZ). This refinement is achieved by introducing conformal equality constraints that eliminate incompatible disturbance realizations. More precisely, prior knowle...