9期

最新文章

• Distributed Dynamic Clustering and Consensus in Multiagent Systems AI翻译
  作者：Lian, Bosen; Koru, Ahmet Taha; Xue, Wenqian; Lewis, Frank L.; Davoudi, Ali
  作者单位：Auburn University System; Auburn University; State University System of Florida; University of Florida
  摘要：This article proposes a unified framework of a distributed dynamic clustering method and a distributed control law to solve the cluster consensus problem for networked multiagent systems (MASs) with multiple leaders. Given any initial unclustered directed graph and any initial-state followers and leaders, the distributed dynamic clustering method aims to cluster each leader with at least one follower without interconnection among the clusters. This cuts some initially available edges online by...
• Stability and Convergence of a Randomized Model Predictive Control Strategy AI翻译
  作者：Veldman, Daniel W. M.; Borkowski, Alexandra; Zuazua, Enrique
  作者单位：University of Erlangen Nuremberg; University of London; King's College London; University of Deusto; Autonomous University of Madrid
  摘要：RBM-MPC is a computationally efficient variant of model predictive control (MPC) in which the random batch method (RBM) is used to speed up the finite-horizon optimal control problems at each iteration. In this article, stability and convergence estimates are derived for RBM-MPC of unconstrained linear systems. The obtained estimates are validated in a numerical example that also shows a clear computational advantage of RBM-MPC.
• A Multivariate Non-Gaussian Bayesian Filter Using Power Moments AI翻译
  作者：Wu, Guangyu; Lindquist, Anders
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University
  摘要：In this article, we extend our results on the univariate non-Gaussian Bayesian filter using power moments (Wu and Lindquist, 2023) to the multivariate systems, which can be either linear or nonlinear. Doing this introduces several challenging problems, for example a positive parametrization of the density surrogate, which is not only a problem of filter design but also one of the multidimensional Hamburger moment problem. We propose a parametrization of the density surrogate with the proofs to...
• Global Consensus of Double-Integrator Multiagent Systems With Input Saturation by Fully Distributed Event-Triggered and Self-Triggered Controls AI翻译
  作者：Zhang, Kai; Zhou, Bin; Duan, Guang-Ren
  作者单位：Harbin Institute of Technology
  摘要：This article considers the global leader-following event-triggered consensus problem for the input-constrained double-integrator multiagent systems. By utilizing a Lyapunov-like function consisting of a positive semidefinite term and an integral term, a novel bounded static linear event-triggered protocol is proposed to ensure that the global leader-following consensus problem is solved in a fully distributed manner for all undirected communication graphs. In order to avoid continuously monito...
• Error Corrected References for Accelerated Convergence of Low Gain Norm Optimal Iterative Learning Control AI翻译
  作者：Owens, David H.; Chu, Bing
  作者单位：Zhengzhou University; University of Sheffield; University of Southampton
  摘要：To reduce the need for high gains (reduced control weighting) for fast convergence in norm optimal iterative learning control (NOILC), this article presents a simple data-driven mechanism for accelerating the convergence of low gain feedback NOILC controllers. The method uses a modification to the reference signal on each NOILC iteration using the measured tracking error from the previous iteration. The basic algorithm is equivalent to a gradient iteration combined with an NOILC iteration. The...
• Reinforcement Learning for Partially Observable Linear Gaussian Systems Using Batch Dynamics of Noisy Observations AI翻译
  作者：Yaghmaie, Farnaz Adib; Modares, Hamidreza; Gustafsson, Fredrik
  作者单位：Linkoping University; Michigan State University
  摘要：Reinforcement learning algorithms are commonly used to control dynamical systems with measurable state variables. If the dynamical system is partially observable, reinforcement learning algorithms are modified to compensate for the effect of partial observability. One common approach is to feed a finite history of input-output data instead of the state variable. In this article, we study and quantify the effect of this approach in linear Gaussian systems with quadratic costs. We coin the conce...
• A Computationally Efficient Trajectory Prediction in MPC for Piecewise Affine Systems AI翻译
  作者：Adelirad, Masoud; Afzalian, Ali A.
  作者单位：Shahid Beheshti University; Shahid Beheshti University
  摘要：A novel model predictive control strategy is proposed to determine an optimal control signal, which steers the states of a constraint piecewise affine system to a desired target point through suboptimal state trajectories, based on quadratic performance criteria. In piecewise affine systems, the number of possible switching sequences grows exponentially with the length of the prediction horizon that leads to multiple trajectories in the receding horizon strategy; and should be considered in ca...
• Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems AI翻译
  作者：Hakobyan, Astghik; Yang, Insoon
  作者单位：Seoul National University (SNU); Seoul National University (SNU)
  摘要：Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in stochastic systems. While most existing works address inaccurate distributional information in fully observable settings, we consider a partially observable DRC problem for discrete-time linear systems using the Wasserstein metric. For a tractable solution, we propose a novel approximation method exploiting the Gelbrich bound of the Wasserstein distance. Using techniques from modern distributionally ro...
• Deadlock Resolution and Recursive Feasibility in MPC-Based Multirobot Trajectory Generation AI翻译
  作者：Chen, Yuda; Guo, Meng; Li, Zhongkui
  作者单位：Peking University
  摘要：Online collision-free trajectory generation within a shared workspace is fundamental for most multirobot applications. However, many widely-used methods based on model predictive control (MPC) lack theoretical guarantees on the feasibility of underlying optimization. Furthermore, when applied in a distributed manner without a central coordinator, deadlocks often occur where several robots block each other indefinitely. Whereas heuristic methods such as introducing random perturbations exist, n...
• A Restless Bandit Model for Energy-Efficient Job Assignments in Server Farms AI翻译
  作者：Fu, Jing; Wang, Xinyu; Wang, Zengfu; Zukerman, Moshe
  作者单位：Royal Melbourne Institute of Technology (RMIT); City University of Hong Kong; Northwestern Polytechnical University
  摘要：We aim to maximize the energy efficiency, gauged as average energy cost per job, in a large-scale server farm with various storage or/and computing components modeled as parallel abstracted servers. Each server operates in multiple power modes characterized by potentially different service and energy consumption rates. The heterogeneity of servers and multiple power modes complicate the maximization problem, where optimal solutions are generally intractable. Relying on the Whittle relaxation t...