IEEE Transactions on Automatic Control

最新文章

• Adaptive Output Regulation of a Class of 1-D Hyperbolic PDEs With Unknown Boundary Scaled Parameters AI翻译
  作者：Xu, Xiaodong; Yuan, Yuan; Dubljevic, Stevan; Yin, Shen
  作者单位：Central South University; Changsha University of Science & Technology; University of Alberta
  摘要：This article considers the problem of adaptive output regulation of a class of 1-D anticollocated hyperbolic partial differential equations (PDEs). The model is subject to unknown scaled parameters in both the boundary condition and boundary measurement. An adaptive boundary observer, providing online estimates of the system state and parameters, is designed. Particularly, to realize that the pure boundary state at left side tracks a reference signal, a challenging problem with the estimate of...
• The Domain of Attraction of the Desired Path in Vector-Field-Guided Path Following AI翻译
  作者：Yao, Weijia; Lin, Bohuan; Anderson, Brian D. O.; Cao, Ming
  作者单位：Hunan University; University of Groningen; University of Groningen; Australian National University
  摘要：In the vector-field-guided path-following problem, a sufficiently smooth vector field is designed such that its integral curves converge to and move along a 1-D geometric desired path. The existence of singular points where the vector field vanishes creates a topological obstruction to global convergence to the desired path and some associated topological analysis has been conducted in our previous work. In this article, we further show that the domain of attraction of the desired path, which ...
• Feedback Control of Quantum Systems to Stabilize Superposition States AI翻译
  作者：Daeichian, Abolghasem
  作者单位：Arak University
  摘要：As a specific trait of quantum mechanics, quantum superposition is exploited in many applications, such as quantum computation. This article proposes a feedback scheme to stabilize the desired quantum superposition states. To this aim, a new factorization is derived for the stochastic master equation of a quantum system which is driven by the field in the superposition of coherent states. Then, a feedback scheme is proposed, which consists of a quantum system driven by the field in the superpo...
• Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance AI翻译
  作者：Doan, Thinh T. T.
  作者单位：Virginia Polytechnic Institute & State University
  摘要：Two-time-scale stochastic approximation, a generalized version of the popular stochastic approximation, has found broad applications in many areas including stochastic control, optimization, and machine learning. Despite its popularity, theoretical guarantees of this method, especially its finite-time performance, are mostly achieved for the linear case while the results for the nonlinear counterpart are very sparse. Motivated by the classic control theory for singularly perturbed systems, we ...
• Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models AI翻译
  作者：Chen, Guang-Yong; Gan, Min; Chen, Jing; Chen, Long
  作者单位：Fuzhou University; Qingdao University; Jiangnan University; University of Macau
  摘要：This article presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the special structure of nonlinear regression models, in which the parameters can be partitioned into a linear part and a nonlinear part. In this article, we develop an efficient recursive algorithm for nonlinear regression models based on analyzing the equivalent f...
• Filippov's Solution and Finite-Time Stability of Stochastic Systems for Discontinuous Control AI翻译
  作者：Chen, Sheng; Li, Tao; Zang, Qiang; Liu, Yunping
  作者单位：Nanjing University of Information Science & Technology
  摘要：this article, a suite of theoretic tools is provided for discontinuous control design and finite-time stability analysis of a class of stochastic differential systems. The notion of Filippov's solutions for stochastic differential systems is proposed, and the corresponding solution existence problem is explored. The classical Ito differentiation formula is generalized for quasi-C?(2)(0) (R-n, R)-class functions along Filippov's solutions of stochastic differential systems, and two involved set...
• Inaccuracy Matters: Accounting for Solution Accuracy in Event-Triggered Nonlinear Model Predictive Control AI翻译
  作者：Faqir, Omar J.; Kerrigan, Eric C.
  作者单位：Imperial College London; Imperial College London
  摘要：We consider the effect of using approximate system predictions in event-triggered control schemes. These approximations often result from using numerical transcription methods for solving continuous-time optimal control problems. Mesh refinement can guarantee upper bounds on the error in the differential equations that model the system dynamics. We employ the accuracy guarantees of a mesh refinement scheme to show that the proposed event-triggering scheme, which compares the measured system wi...
• Designing Distributed Impulsive Controller for Networked Singularly Perturbed Systems AI翻译
  作者：He, Wangli; Liang, Kun; Qian, Feng; Chen, Guanrong
  作者单位：East China University of Science & Technology; City University of Hong Kong
  摘要：This article develops a novel synthesis approach for the synchronization of a network of singularly perturbed systems (SPSs) with a small singular perturbation parameter e via distributed impulsive control. First, a decoupling method in the setting of directed networks is employed to decompose networked SPSs related to complex eigenvalues of the Laplacian matrix. Then, based on an improved piecewise continuous Lyapunov function, an e-dependent synchronization criterion is established. The rela...
• Deep Filtering With Adaptive Learning Rates AI翻译
  作者：Qian, Hongjiang; Yin, George; Zhang, Qing
  作者单位：University of Connecticut; University System of Georgia; University of Georgia
  摘要：This article develops a new deep learning framework for general nonlinear filtering. Our main contribution is to present a computationally feasible procedure. The proposed algorithms have the capability of dealing with challenging (infinitely dimensional) filtering problems involving diffusions with randomly-varying switching. First, we convert it to a problem in a finite-dimensional setting by approximating the optimal weights of a neural network. Then, we construct a stochastic gradient-type...
• Hamiltonian Deep Neural Networks Guaranteeing Nonvanishing Gradients by Design AI翻译
  作者：Galimberti, Clara Lucia; Furieri, Luca; Xu, Liang; Ferrari-Trecate, Giancarlo
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Shanghai University
  摘要：Deep neural networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretization of continuous-time Hamiltonian systems and include several existing DNN architectures based on ordinary differential equations. Our main result is that a broad set of H-DNNs ensures nonvanishing gradients by design for an arbitrary netw...