-
作者：Stoorvogel, Anton A.; Saberi, Ali; Liu, Zhenwei
作者单位：University of Twente; Washington State University; Northeastern University - China
摘要：Originally, protocols were designed for multiagent systems using information about the network which might not be available. Recently, there has been a focus on scale-free synchronization where the protocol is designed without any prior information about the network. As long as the network contains a directed spanning tree, a scale-free protocol guarantees that the network achieves synchronization. If there is no directed spanning tree then synchronization cannot be achieved. But what happens ...
-
作者：Ito, Kaito; Kashima, Kenji
作者单位：University of Tokyo; Kyoto University
摘要：This article addresses the problem of steering the distribution of the state of a discrete-time linear system to a given target distribution while minimizing an entropy-regularized cost functional. This problem is called a maximum entropy density control problem. Specifically, the running cost is given by quadratic forms of the state and the control input, and the initial and target distributions are Gaussian. We first reveal that our problem boils down to solving two Riccati difference equati...
-
作者：McAllister, Robert D.; Esfahani, Peyman Mohajerin
作者单位：Delft University of Technology
摘要：We establish a collection of closed-loop guarantees and propose a scalable optimization algorithm for distributionally robust model predictive control (DRMPC) applied to linear systems, convex constraints, and quadratic costs. Via standard assumptions for the terminal cost and constraint, we establish distributionally robust long-term and stagewise performance guarantees for the closed-loop system. We further demonstrate that a common choice of the terminal cost, i.e., via the discrete-algebra...
-
作者：Zhang, Yu-Qing; Liu, Da-Yan; Boutat, Driss; Wu, Ze-Hao
作者单位：Universite de Orleans; Foshan University
摘要：This article investigates the nonasymptotic and robust distributed algebraic state estimation for linear time-varying systems using a network of sensors in noisy environments. First, at each sensor node, the system state is transformed into a linear combination of a group of nodes' local observable states, allowing for distributed estimation by estimating a reduced-order state for each node. Second, without requiring initial conditions, the estimation scheme based on generalized modulating fun...
-
作者：Jiang, Xia; Zeng, Xianlin; Xie, Lihua; Sun, Jian; Chen, Jie
作者单位：Chinese University of Hong Kong; Beijing Institute of Technology; Nanyang Technological University
摘要：This article proposes a distributed stochastic projection-free algorithm for large-scale constrained finite-sum optimization whose constraint set is complicated such that the projection onto the constraint set can be expensive. The global cost function is allocated to multiple agents, each of which computes its local stochastic gradients and communicates with its neighbors to solve the global problem. Stochastic gradient methods enable low computational complexity, while they are hard and slow...
-
作者：Shi, Ji; Jiao, Xiaopei; Yau, Stephen S. -T.
作者单位：Capital Normal University; University of Twente; Tsinghua University
摘要：The optimal filtering problem for general nonlinear and continuous state-observation systems attracts lots of attention in the control theory. The essence of optimal filtering requires solving the Duncan-Mortensen-Zakai (DMZ) equation in a computationally feasible way. Under the pioneering work of Yau-Yau filtering, the DMZ equation is reduced to a pathwise computation of a forward Kolmogorov equation with time-varying initial conditions, which is very challenging. To overcome the computationa...
-
作者：von Esch, Maximilian Pierer; Volz, Andreas; Graichen, Knut
作者单位：University of Erlangen Nuremberg
摘要：This article presents a sensitivity-based algorithm for distributed optimal control problems (OCP) of multi-agent systems with nonlinear dynamics and state/input couplings, as they arise, for instance, in distributed model predictive control. The algorithm relies on first-order sensitivities to cooperatively solve the distributed OCP in parallel. The solutions to the resulting local OCPs are computed with a fixed-point scheme and communicated within one communication step per algorithm iterati...
-
作者：Wang, Lei; Ren, Zihao; Yuan, Deming; Shi, Guodong
作者单位：Zhejiang University; Nanjing University of Science & Technology; University of Sydney
摘要：Distributed computing is fundamental to multiagent systems, with solving distributed linear equations as a typical example. In this article, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a...
-
作者：Fisher, Michael W.; Hug, Gabriela; Dorfler, Florian
作者单位：University of Waterloo; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In Part I, a novel Galerkin-type method for finite dimensional approximations of transfer functions in Hardy space was developed based on approximation by simple poles. In Part II, this approximation is applied to system level synthesis, a recent approach based on a clever reparameterization, to develop a new technique for optimal control design. To solve system level synthesis problems, prior work relies on finite impulse response approximations that lead to deadbeat control, and that can exp...
-
作者：Ikeda, Takuya
作者单位：University of Kitakyushu
摘要：Maximum hands-off control is the optimal solution to the L(0 )optimal control problem. While convex approximation is typically used to relax this problem, it does not necessarily result in maximum hands-off control. Therefore, this study introduces a nonconvex approximation method and a class of nonconvex optimal control problems that are always equivalent to the maximum hands-off control problem. A computation method based on difference of convex functions optimization is then derived and num...
