2022

最新文章

• Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Method AI翻译
  作者：Li, Na; Li, Xun; Peng, Jing; Xu, Zuo Quan
  作者单位：Shandong University of Finance & Economics; Hong Kong Polytechnic University
  摘要：This article adopts a reinforcement learning (RL) method to solve infinite horizon continuous-time stochastic linear quadratic problems, where the drift and diffusion terms in the dynamics may depend on both the state and control. Based on the Bellman's dynamic programming principle, we presented an online RL algorithm to attain optimal control with partial system information. This algorithm computes the optimal control, rather than estimates the system coefficients, and solves the related Ric...
• Inference With Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach AI翻译
  作者：Singh, Rahul; Haasler, Isabel; Zhang, Qinsheng; Karlsson, Johan; Chen, Yongxin
  作者单位：University System of Georgia; Georgia Institute of Technology; Royal Institute of Technology
  摘要：We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods that either exhibit prohibitive complexity as the population grows or do not guarantee convergence. Our method is based on optimal transport, or more...
• Data-Driven Identification of Dissipative Linear Models for Nonlinear Systems AI翻译
  作者：Sivaranjani, S.; Agarwal, Etika; Gupta, Vijay
  作者单位：Purdue University System; Purdue University; University of Notre Dame; Purdue University System; Purdue University
  摘要：We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time-domain input-output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of the linear model to enforce dissipativity, while closely approximating the dynamical behavior of the nonlinear system. Further, we provide an analytical relationship between the...
• Appointed-Time and Attack-Free Bipartite Synchronization of Generic Linear Multiagent Systems Over Directed Switching Networks AI翻译
  作者：Zhao, Yu; Zhou, Yuan; Huang, Panfeng; Chen, Guanrong
  作者单位：Northwestern Polytechnical University; City University of Hong Kong
  摘要：This article derives a solution to the appointed-time and attack-free bipartite synchronization problem for generic linear multiagent systems over directed switching networks accommodating cooperative and antagonistic interactions. Herein, appointed-time means that the settling time is independent of any parameters and is preappointed in advance. Attack-free requires that the communication channel is protected, which means that no state and observer information exchange is allowed, and only me...
• On the Analysis of Inexact Augmented Lagrangian Schemes for Misspecified Conic Convex Programs AI翻译
  作者：Aybat, Necdet Serhat; Ahmadi, Hesam; Shanbhag, Uday V.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：In this article, we consider the misspecified optimization problem of minimizing a convex function f(x; theta*) in x over a conic constraint set represented by h(x; theta*) is an element of K, where theta* is an unknown (or misspecified) vector of parameters, K is a closed convex cone, and h is affine in x. Suppose that theta* is unavailable but may be learnt by a separate process that generates a sequence of estimators theta(k), each of which is an increasingly accurate approximation of theta...
• An Explicit Parametrization of Closed Loops for Spatially Distributed Controllers With Sparsity Constraints AI翻译
  作者：Jensen, Emily; Bamieh, Bassam
  作者单位：University of California System; University of California Santa Barbara; University of California System; University of California Santa Barbara
  摘要：In this article, we study the linear timeinvariant state-feedback controller design problem for distributed systems. We follow the recently developed system level synthesis (SLS) approach and impose locality structure on the resulting closed-loop mappings; the corresponding controller implementation inherits this prescribed structure. In contrast to existing SLS results, we derive an explicit (rather than implicit) parameterization of all achievable stabilized closed-loops. This admits more ef...
• Event-Triggered Control for Switched Systems With Denial-of-Service Attack AI翻译
  作者：Zhao, Rui; Zuo, Zhiqiang; Wang, Yijing
  作者单位：Tianjin University
  摘要：This article studies the stabilization problem for switched systems in the presence of denial-of-service (DoS) attack using the event-triggered scheme. Unlike the traditional switching signal design with DoS attack, some challenges arise in the joint influence on the constraints of DoS duration/frequency, and the asynchronous behavior caused by the controller and the subsystem mode. To this end, a novel multiple Lyapunov function involving the joint effects of DoS attack and controller mode is...
• LMI Framework for Set Reachability Inclusion in Discrete-Time LTI Systems AI翻译
  作者：Hadizadeh Kafash, Sahand; Ruths, Jusin
  作者单位：University of Texas System; University of Texas Dallas; University of Texas System; University of Texas Dallas
  摘要：In this article, we present a convex optimization framework to verify the reachability of a desired set for discrete-time linear time-invariant systems. Given elliptically bounded inputs, the set of reachable states in N time steps is the Minkowski sum of a finite number of ellipsoids. We formulate the inclusion verification problem as a chain of constraints in the form of linear matrix inequalities. As the time horizon grows, the number of constraints becomes unwieldy, and we present a techni...
• Trajectory Tracking of Underactuated VTOL Aerial Vehicles With Unknown System Parameters Via IRL AI翻译
  作者：Li, Shaobao; Durdevic, Petar; Yang, Zhenyu
  作者单位：Aalborg University
  摘要：This article studies the optimal control policy learning for underactuated vertical take-off and landing (VTOL) aerial vehicles subject to the unknown mass and inertia matrix. A novel off-policy integral reinforcement learning (IRL) scheme is presented for simultaneously unknown parameter identification and optimal trajectory tracking. In the outer loop of the VTOL vehicles, a novel off-policy IRL scheme is proposed, where the fixed control policy for data generation is chosen to be different ...
• Resilient Model Predictive Control for Constrained Cyber-Physical Systems Subject to Severe Attacks on the Communication Channels AI翻译
  作者：Franze, Giuseppe; Lucia, Walter; Tedesco, Francesco
  作者单位：University of Calabria; Concordia University - Canada
  摘要：In this article, a robust model predictive control strategy is developed for networked cyber-physical systems under false data injections. The main feature of the proposed scheme relies on the capability to mitigate undesired system behaviors due to external malicious actions. This is achieved by showing that set-membership conditions allow us to quickly detect data integrity anomalies and, as a consequence, to implement adequate countermeasures. The core of the resulting solution exploits rec...