Fully asynchronous distributed optimization with linear convergence over directed networks

We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λ^(k)) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem.

Data-Driven Control of Linear Systems via Quantized Feedback

Quantized feedback control is fundamental to system synthesis with limited communication capacity.In sharp contrast to the existing literature on quantized control which requires an explicit dynamical model,the authors study the quadratic stabilization and performance control problems with logarithmically quantized feedback in a direct data-driven framework,where the system state matrix is not exactly known and instead,belongs to an ambiguity set that is directly constructed from a finite number of noisy system data.To this end,the authors firstly establish sufficient and necessary conditions via linear matrix inequalities for the existence of a common quantized controller that achieves our control objectives over the ambiguity set.Then,the authors provide necessary conditions on the data for the solvability of the LMIs,and determine the coarsest quantization density via semi-definite programming.The theoretical results are validated through numerical examples.

CONSENSUS DESIGN FOR CONTINUOUS-TIME MULTI-AGENT SYSTEMS WITH COMMUNICATION DELAY

This paper investigates a consensus design problem for continuous-time first-order multiagent systems with uniform constant communication delay.Provided that the agent dynamic is unstable and the diagraph is undirected,sufficient conditions are derived to guarantee consensus.The key technique is the adoption of historical input information in the protocol.Especially,when agent's own historical input information is used in the protocol design,the consensus condition is constructed in terms of agent dynamic,communication delay,and the eigenratio of the network topology.Simulation result is presented to validate the effectiveness of the theoretical result.

AUV Based Source Seeking with Estimated Gradients

这份报纸跟寻求问题为的消息提供者讲话一自治在水下有估计的坡度的车辆(AUV ) 。AUV 与多重传感器被嵌入，它仅仅能与未知分发检测来源的信号力量。解决这挑战，一种传感器配置明确地作为一个半圆被设计估计信号地的坡度。然后，一个控制器经由估计的坡度被获得驱使 AUV 走近消息提供者。而且，为本地化错误的上面的界限以半圆和信号分发的半径被提供。最后，作者由把策略用于包括一个模拟例子一为在华南海寻求海床的一个区域的最深的点的遥远的环境监视单位(REMUS ) 。