IEEE Transactions on Automatic Control

最新文章

• Regret-Optimal Estimation and Control AI翻译
  作者：Goel, Gautam; Hassibi, Babak
  作者单位：University of California System; University of California Berkeley; California Institute of Technology
  摘要：In this article, we consider estimation and control in linear dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing causal state estimators and causal controllers, which compete against a clairvoyant noncausal policy, instead of the best policy selected in hindsight from some fixed parametric class. We show that regret-optimal filters and regret-optimal controllers can be derived in state space form using operat...
• Numerical Gaussian Process Kalman Filtering for Spatiotemporal Systems AI翻译
  作者：Kuper, Armin; Waldherr, Steffen
  作者单位：KU Leuven; University of Vienna
  摘要：We present a novel Kalman filter (KF) for spatiotemporal systems called the numerical Gaussian process Kalman filter (NGPKF). Numerical Gaussian processes have recently been introduced as a physics-informed machine-learning method for simulating time-dependent partial differential equations without the need for spatial discretization while also providing uncertainty quantification of the simulation resulting from noisy initial data. We formulate numerical Gaussian processes as linear Gaussian ...
• Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems AI翻译
  作者：Lopez, Victor G.; Alsalti, Mohammad; Mueller, Matthias A.
  作者单位：Leibniz University Hannover
  摘要：This article introduces and analyzes an improved Q-learning algorithm for discrete-time linear time-invariant systems. The proposed method does not require any knowledge of the system dynamics, and it enjoys significant efficiency advantages over other data-based optimal control methods in the literature. This algorithm can be fully executed offline, as it does not require to apply the current estimate of the optimal input to the system as in on-policy algorithms. It is shown that a PE input, ...
• Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control AI翻译
  作者：Tabuada, Paulo; Gharesifard, Bahman
  作者单位：University of California System; University of California Los Angeles
  摘要：In this article, we show that deep residual neural networks have the power of universal approximation by using, in an essential manner, the observation that these networks can be modeled as nonlinear control systems. We first study the problem of using a deep residual neural network to exactly memorize training data by formulating it as a controllability problem for an ensemble control system. Using techniques from geometric control theory, we identify a class of activation functions that allo...
• Consensus on Directed Matrix-Weighted Networks AI翻译
  作者：Su, Housheng; Miao, Suoxia
  作者单位：Huazhong University of Science & Technology; East China Jiaotong University; Jiangxi University of Water Resources & Electric Power
  摘要：Due to the interdependence of multidimensional states between agents in many practical scenarios, it is more accurate to model multiagent systems by matrix coupling networks. However, existing related results rely on undirected networks. In this article, we discuss the consensus problems for matrix-weighted continuous-time, discrete-time multiagent systems over fixed directed network topologies, and switched multiagent systems switching between continuous-time dynamics and discrete-time dynami...
• Distributed Linear Equations Over Random Networks AI翻译
  作者：Yi, Peng; Lei, Jinlong; Chen, Jie; Hong, Yiguang; Shi, Guodong
  作者单位：Tongji University; Tongji University; University of Sydney
  摘要：Distributed linear algebraic equation over networks, where nodes hold a part of problem data and cooperatively solve the equation via node-to-node communications, is a basic distributed computation task receiving an increasing research attention. Communications over a network have a stochastic nature, with both temporal and spatial dependence due to link failures, packet dropouts, or node recreation, etc. In this article, we study the convergence and convergence rate of distributed linear equa...
• Finite-Time Stabilization and Optimal Feedback Control for Nonlinear Discrete-Time Systems AI翻译
  作者：Haddad, Wassim M.; Lee, Junsoo
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Finite-time stability involves dynamical systems whose trajectories converge to an equilibrium state in finite time. Sufficient conditions for finite-time stability have recently been developed in the literature for discrete-time dynamical systems. In this article, we build on these results to develop a framework for addressing the problem of optimal nonlinear analysis and feedback control for finite-time stability and finite-time stabilization for nonlinear discrete-time controlled dynamical ...
• Hyperexponential and Fixed-Time Stability of Time-Delay Systems: Lyapunov-Razumikhin Method AI翻译
  作者：Nekhoroshikh, Artem N.; Efimov, Denis; Polyakov, Andrey; Perruquetti, Wilfrid; Furtat, Igor B.
  作者单位：ITMO University; Universite de Lille; Centrale Lille; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Information Sciences & Technologies (INS2I); Inria; Universite de Lille; Centrale Lille; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute for Information Sciences & Technologies (INS2I); Russian Academy of Sciences
  摘要：Razumikhin-like theorems on hyperexponential and fixed-time stability of time-delay systems are proposed for both explicitly and implicitly defined Lyapunov functions. While the former method is useful for stability analysis, the latter approach is more suitable for control synthesis. Examples of systems that can be stabilized hyperexponentially and in fixed time are given. The control parameters tuning algorithm is presented in the form of linear matrix inequalities. The numerical simulations...
• A Model Randomization Approach to Statistical Parameter Privacy AI翻译
  作者：Nekouei, Ehsan; Sandberg, Henrik; Skoglund, Mikael; Johansson, Karl Henrik
  作者单位：City University of Hong Kong; Royal Institute of Technology
  摘要：In this article, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation. The randomizer takes the private parameter as input and randomly generates a pseudo parameter. The nonlinear mapping transforms the measurements such that the joint p.d.f. of ...
• Excitation for Adaptive Optimal Control of Nonlinear Systems in Differential Games AI翻译
  作者：Karg, Philipp; Koepf, Florian; Braun, Christian A.; Hohmann, Soeren
  作者单位：Helmholtz Association; Karlsruhe Institute of Technology
  摘要：This article focuses on the fulfillment of the persistent excitation (PE) condition for signals which result from transformations by means of polynomials. This is essential, e.g., for the convergence of adaptive dynamic programming algorithms due to commonly used polynomial function approximators. As theoretical statements are scarce regarding the nonlinear transformation of PE signals, we propose conditions on the system state such that its transformation by polynomials is PE. To validate our...