7期

最新文章

• 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...
• 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...
• 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...
• Model Predictive Control for Tracking Bounded References With Arbitrary Dynamics AI翻译
  作者：Han, Shibo; Hou, Bonan; Zhang, Yuhao; Shi, Xiaotong; Zhao, Xingwei
  作者单位：Hubei University; National University of Singapore; Singapore University of Technology & Design; Huazhong University of Science & Technology
  摘要：In this article, a model predictive control (MPC) method is proposed for constrained linear systems to track bounded references generated by linear exosystems with arbitrary Lyapunov stable dynamics. In the proposed method, sudden changes in reference are taken into account and are addressed by introducing an artificial reference as an additional decision variable. The cost function penalizes both the artificial state error and the reference error, while the terminal constraint is imposed on t...