IEEE Transactions on Automatic Control

最新文章

• Stabilization of Triangular Nonlinear Systems With Multiplicative Stochastic State Sensing Noise AI翻译
  作者：Li, Wuquan; Krstic, Miroslav
  作者单位：Ludong University; University of California System; University of California San Diego
  摘要：We present new state feedback control designs for lower/upper triangular nonlinear systems with multiplicative stochastic sensor uncertainty. For lower triangular nonlinear systems with small sensor noise, we develop a novel control design where the control gains are suitably constructed to simultaneously dominate the nonlinear functions and sensor noise of sufficiently small multiplicative gain. For upper triangular nonlinear systems, we propose a new low-gain domination design, the advantage...
• A Warm-Start Strategy in Interior Point Methods for Shrinking Horizon Model Predictive Control With Variable Discretization Step AI翻译
  作者：Zhang, Ziyou; Zhao, Qianchuan; Dai, Fa-An
  作者单位：Tsinghua University
  摘要：In this article, we present a warm-start point algorithm in interior point methods for shrinking horizon model predictive control with a variable discretization step. In the algorithm, a convex combination of components of the earlier optimal solution is used to construct an interpolation point, and we use the convex combination of the modified interpolation point and the cold-start point to obtain a warm-start point. We prove that the worst-case iteration complexity of our strategy is better ...
• Representer Theorem for Learning Koopman Operators AI翻译
  作者：Khosravi, Mohammad
  作者单位：Delft University of Technology
  摘要：In this work, we consider the problem of learning the Koopman operator for discrete-time autonomous systems. The learning problem is formulated as a generic constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that a representer theorem holds for the introduced learning problem under certain but general conditions, which allows convex reformulation of the problem in a specific finite-dimensional space without any approximation and l...
• Data-Efficient Active Weighting Algorithm for Composite Adaptive Control Systems AI翻译
  作者：Kim, Seong-hun; Lee, Hanna; Cho, Namhoon; Kim, Youdan
  作者单位：Seoul National University (SNU); Cranfield University
  摘要：We propose an active weighting algorithm for composite adaptive control to reduce the state and estimate errors while maintaining the estimation quality. Unlike previous studies that construct the composite term by simply stacking, removing, and pausing observed data, the proposed method efficiently utilizes the data by providing a theoretical set of weights for observations that can actively manipulate the composite term to have desired characteristics. As an example, a convex optimization fo...
• On-the-Fly Symbolic Synthesis With Memory Reduction Guarantees AI翻译
  作者：Macoveiciuc, Elisei; Reissig, Gunther
  作者单位：Bundeswehr University Munich
  摘要：We present novel on-the-fly abstraction-based controller synthesis algorithms for two fundamental specifications-invariance and optimal reachability. In contrast to existing methods, the algorithms we propose do not require precomputation or storage of any large abstraction parts. This translates to orders of magnitude of reduction in memory consumption compared to standard methods. Moreover, our algorithms fully apply to general nonlinear systems and provide controllers that do not posses any...
• Solving Problems With Inconsistent Constraints With a Modified Augmented Lagrangian Method AI翻译
  作者：Neuenhofen, Martin P.; Kerrigan, Eric C.
  作者单位：Imperial College London; Imperial College London
  摘要：We present a numerical method for the minimization of constrained optimization problems where the objective is augmented with large quadratic penalties of inconsistent equality constraints. Such objectives arise from quadratic integral penalty methods for the direct transcription of optimal control problems. The augmented Lagrangian method (ALM) has a number of advantages over the quadratic penalty method (QPM). However, if the equality constraints are inconsistent, then ALM might not converge...
• Incentivizing Collaboration in Heterogeneous Teams via Common-Pool Resource Games AI翻译
  作者：Gupta, Piyush; Bopardikar, Shaunak D.; Srivastava, Vaibhav
  作者单位：Michigan State University
  摘要：We consider a team of heterogeneous agents, which is collectively responsible for servicing, and subsequently reviewing a stream of homogeneous tasks. Each agent has an associated mean service time and a mean review time for servicing and reviewing the tasks, respectively. Agents receive a reward based on their service and review admission rates. The team objective is to collaboratively maximize the number of serviced and reviewed tasks. We formulate a common-pool resource game, and design uti...
• State Distribution of Markovian Jump Boolean Networks and Its Applications AI翻译
  作者：Meng, Min; Xiao, Gaoxi
  作者单位：Tongji University; Tongji University; Nanyang Technological University
  摘要：This article investigates the state distribution of Markovian jump Boolean networks subject to stochastic disturbances based on the measured outputs. The considered disturbances are modeled as independent and identically distributed processes with known probability distributions. An iterative algorithm is proposed to compute conditional probability distributions of the current state and one-step predicted state based on the knowledge of the output measurements. The obtained conditional probabi...
• Safe Policies for Reinforcement Learning via Primal-Dual Methods AI翻译
  作者：Paternain, Santiago; Calvo-Fullana, Miguel; Chamon, Luiz F. O.; Ribeiro, Alejandro
  作者单位：Massachusetts Institute of Technology (MIT); University of California System; University of California Berkeley; University of Pennsylvania
  摘要：In this article, we study the design of controllers in the context of stochastic optimal control under the assumption that the model of the system is not available. This is, we aim to control a Markov decision process of which we do not know the transition probabilities, but we have access to sample trajectories through experience. We define safety as the agent remaining in a desired safe set with high probability during the operation time. The drawbacks of this formulation are twofold. The pr...
• Functional Detectability and Asymptotic Functional Observer Design AI翻译
  作者：Darouach, Mohamed; Fernando, Tyrone
  作者单位：Universite de Lorraine; University of Western Australia
  摘要：The objective of this article is to present a new solution to the functional observer design problem. First, a new definition for functional detectability is given; then, some algebraic conditions for linear multivariable systems to be functional detectable are presented. They generalize and coincide with the existing detectability condition required in the design of reduced and full-order Luenberger observers. Then, necessary and sufficient conditions for existence of an asymptotic functional...