IEEE Transactions on Automatic Control

最新文章

• New Versions of Gradient Temporal-Difference Learning AI翻译
  作者：Lee, Donghwan; Lim, Han-Dong; Park, Jihoon; Choi, Okyong
  作者单位：Korea Advanced Institute of Science & Technology (KAIST)
  摘要：Sutton, Szepesvari and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training. The goal of this article is 1) to propose some variants of GTDs with extensive comparative analysis and 2) to establish new theoretical analysis frameworks for the GTDs. These variants are based on convex-concave saddle-point interpretations of GTDs, which effectively unify all the GTDs into a single framework, and p...
• Distributed Event-Triggered Control of Networked Strict-Feedback Systems via Intermittent State Feedback AI翻译
  作者：Sun, Libei; Huang, Xiucai; Song, Yongduan
  作者单位：Chongqing University
  摘要：It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed adaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered st...
• A Toolkit for Globally Robust Observer-Based Feedback With Relaxed Characterization of iISS/ISS AI翻译
  作者：Ito, Hiroshi; Shim, Hyungbo
  作者单位：Kyushu Institute of Technology; Seoul National University (SNU)
  摘要：This article elaborates on flexibility in dealing with the interconnection of integral input-to-state stable (iISS) and input-to-state stable (ISS) systems. The undecoupled characterizations introduced separately for iISS and ISS in the literature are linked to build a framework enabling global analysis without settling for local and semiglobal properties. Feedback control design in the presence of measurement noise can benefit from the framework immediately if plants are nonlinear, irrespecti...
• Periodic Averaging of Discrete-Time Systems: A Time-Delay Approach AI翻译
  作者：Yang, Xuefei; Zhang, Jin; Fridman, Emilia
  作者单位：Tel Aviv University; Shanghai University
  摘要：This article is concerned with the stability of discrete-time systems with fast-varying coefficients that may be uncertain. Recently, a constructive time-delay approach to averaging was proposed for continuous-time systems. In this article, we develop, for the first time, this approach to discrete-time case. We first transform the system to a time-delay system with the delay being the period of averaging, which can be regarded as a perturbation of the classical averaged system. The stability o...
• Maximally Permissive Supervisors for Nonblocking Similarity Control of Nondeterministic Discrete-Event Systems AI翻译
  作者：Li, Jinglun; Takai, Shigemasa
  作者单位：University of Osaka
  摘要：This article investigates a nonblocking similarity control problem for nondeterministic discrete-event systems, which is a problem of synthesizing a nonblocking supervisor such that the supervised system is simulated by the given specification. In this article, the state of the system is not required to be observable, and the event occurrence is allowed to be partially observed. We propose an algorithm that computes a nonblocking supervisor from a possibly blocking one by iteratively removing ...
• Sufficient Conditions for Convergent Recursive Extrapolation of qLPV Scheduling Parameters Along a Prediction Horizon AI翻译
  作者：Morato, Marcelo Menezes; Normey-Rico, Julio Elias; Sename, Olivier
  作者单位：Universidade Federal de Santa Catarina (UFSC); Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
  摘要：predictive control (MPC) algorithms have long been applied to nonlinear processes. In a quasi-linear parameter varying (qLPV) setting, nonlinearities are included into bounded scheduling parameters, which are given as a function of endogenous variables; these scheduling parameters are a priori unknown along a future prediction horizon, which complicates MPC design. To address this problem, the literature points out two options: robust MPC approaches, considering the scheduling to be uncertain;...
• Disturbance Observer-Based Boundary Control for an Antistable Stochastic Heat Equation With Unknown Disturbance AI翻译
  作者：Wu, Ze-Hao; Zhou, Hua-Cheng; Deng, Feiqi; Guo, Bao-Zhu
  作者单位：Foshan University; Central South University; South China University of Technology; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; North China Electric Power University
  摘要：In this article, a novel control strategy namely disturbance observer-based control is first applied to stabilization and disturbance rejection for an antistable stochastic heat equation with Neumann boundary actuation and unknown boundary external disturbance generated by an exogenous system. A disturbance observer-based boundary control is designed based on the backstepping approach and estimation/cancellation strategy, where the unknown disturbance is estimated in real time by a disturbance...
• Recursive Identification of Time-Varying Hammerstein Systems With Matrix Forgetting AI翻译
  作者：Dokoupil, Jakub; Vaclavek, Pavel
  作者单位：Brno University of Technology
  摘要：The real-time estimation of the time-varying Hammerstein system by using a noniterative learning schema is considered and extended to incorporate a matrix forgetting factor. The estimation is cast in a variational-Bayes framework to best emulate the original posterior distribution of the parameters within the set of distributions with feasible moments. The recursive concept we propose approximates the exact posterior comprising undistorted information about the estimated parameters. In many pr...
• Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees AI翻译
  作者：Huang, Linbin; Zhen, Jianzhe; Lygeros, John; Dorfler, Florian
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：We introduce a general framework for robust data-enabled predictive control (DeePC) for linear time-invariant systems, which enables us to obtain robust and optimal control in a receding-horizon fashion based on inexact input and output data. Robust DeePC solves a min-max optimization problem to compute the optimal control sequence that is resilient to all possible realizations of the uncertainties in data within a prescribed uncertainty set. We present computationally tractable reformulations...
• On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraints AI翻译
  作者：Sun, Youbang; Fazlyab, Mahyar; Shahrampour, Shahin
  作者单位：Northeastern University; Johns Hopkins University
  摘要：Mirror descent (MD) is a powerful first-order optimization technique that subsumes several optimization algorithms including gradient descent (GD). In this work, we leverage quadratic constraints and Lyapunov functions to analyze the stability and characterize the convergence rate of the MD algorithm as well as its distributed variant using semidefinite programming (SDP). For both algorithms, we consider both strongly convex and nonstrongly convex assumptions. For centralized MD and strongly c...