This article proposes a flexible formation tracking control protocol (FFTC) for multiple unmanned surface vessels (USVs) to pass through narrow water channels with unknown curvatures. An observer is developed to estimate the curvatures of channels. A flexible formation tracking control protocol is, thereby designed to steer USV fleets to pass through narrow channels in a flexible serial formation. Furthermore, the asymptotically stable conditions of the closed-loop multi-USV systems are theoretically derived. Finally, both numerical simulation and experimental results with a fleet of three HUSTER-0.3 USVs are reported to substantiate the effectiveness of the proposed FFTC protocol for navigating through narrow channel in a laboratory context.

This work studies the scaled consensus problems of exponentially unstable networked systems with time-varying input delays. First, a distributed consensus algorithm is designed under the truncated predictor feedback technique. Then, a delay bound is evaluated by utilizing the Lyapunov function-based method. Moreover, the consensus can be guaranteed when the upper bound of the time-varying input delay is no more than the delay bound. Furthermore, the relation between the delay bound and the network synchronizability is established. In other words, better network synchronizability implies a bigger delay bound. Finally, in the simulation examples, the feasibility of the theoretical results is verified, and the actual delay bounds are evaluated.

随着机器人技术的不断发展,制造业迎来新一轮的革命。当前,机器人正从以传统汽车制造业为代表,以零部件搬运、喷涂、焊接等为主要任务的任务单一且复杂度较低的场景,过渡到以3C制造业为代表,具备小批量、多品类、短周期生产特点的多任务、高复杂度的场景中,这对机器人提出了新的挑战。基于以上背景,本文研究了以服务器为装配对象的多机器人装配技术。针对多机器人服务器装配中的协同运动问题,本文首先从传统的单机器人运动学入手对机器人进行了D-H建模,对基于主从关系多机器人协同运动推导了其前提实现条件与运动约束关系。针对多机器人服务器装配中的定位问题,本文分别设计了由动捕系统驱动的全局定位控制与由图像驱动的视觉伺服控制方法,以此组成了跨尺度定位方案来满足不同子任务对定位域和定位精度的需求。针对多机器人服务器装配中的力交互问题,结合六维力传感器,本文设计了自适应的阻抗力插装控制方法,使得机器人能够根据反馈力跟踪目标期望力,以此满足柔顺装配需求。根据经典控制存在的缺陷,本文引入了自回归小波神经网络,结合自适应动态面控制设计了力矩控制器,并根据李雅普诺夫函数推导出在满足系统稳定性前提下的神经网络内的参数更新条件。最后,基于课题组研发的多机器人协同装配实验平台,以服务器装配工序中的内存条装配任务进行了多机器人协同装配实验,实验结果表明了所设计的方案与提出的方法的可行性与有效性,为未来多机器人协同用于3C行业装配制造提供了参考解决思路,加快推动机器人在相关领域的布局,实现真正意义上的智能制造。

A directed acyclic graph (DAG) is a common topology in biological, engineering, and social networks. A network topology is critical in determining a collective behavior of a network dynamic system. For example, the convergence rate of a consensus behavior in a multiagent system relies on the eigenvalues of the Laplacian associated with the network topology. This article aims to analyze the influence of adding a reverse edge into a DAG on convergence rate. It reveals the existence of the so-called interfering reverse edges; adding one single edge in this category can reduce the so-called dominant convergence rate even for a large network. More specifically, a necessary and sufficient condition of an interfering reverse edge is explicitly constructed. According to the condition, a computationally efficient method is proposed to assess an interfering reverse edge.

In this article, we investigate the event-triggered robust output regulation problem for a class of nonlinear systems. Using the idea of time regularization, an event-triggered mechanism with dwell time is proposed together with a model-based control law, which avoids a practical convergence bound and excludes the Zeno behavior as well. The problem is further converted into an event-triggered robust stabilization problem for an augmented system, which is further solved by a model-based event-triggered control law combined with the proposed event-triggered mechanism. Finally, the proposed method achieves an exact solution to the original event-triggered output regulation problem.

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.

This article develops an identification algorithm for nonlinear systems. Specifically, the nonlinear system identification problem is formulated as a sparse recovery problem of a homogeneous variant searching for the sparsest vector in the null subspace. An augmented Lagrangian function is utilized to relax the nonconvex optimization. Thereafter, an algorithm based on the alternating direction method and a regularization technique is proposed to solve the sparse recovery problem. The convergence of the proposed algorithm can be guaranteed through theoretical analysis. Moreover, by the proposed sparse identification method, redundant terms in nonlinear functional forms are removed and the computational efficiency is thus substantially enhanced. Numerical simulations are presented to verify the effectiveness and superiority of the present algorithm.

Formation control finds broad applications in numerous fields, such as cooperative detection, surveillance, transportation, and disaster rescue. In this article, aiming at hunting a moving target, a two-stage surrounding control algorithm is proposed for linear multiagent systems subject to input saturations. An adaptive distributed observer is developed for each agent to reconstruct the target's position. With the assistance of the algebraic graph theory and low gain feedback technique, distributed controllers are designed to drive the agents to encircle the moving target with a fixed radius and evenly distributed phase angles. Finally, both numerical simulations and experiments are conducted to verify the effectiveness of the proposed control algorithm.

For scenarios to pass through narrow and irregular channels, we develop a pragmatic distributed flexible formation protocol for multiple unmanned surface vessel systems (Multi-USVs). Therein, for path tracking with two leaders USVs, we propose a model predictive trajectory tracking scheme. Specifically, we design a scanning-formation controller with a trajectory state estimator for the followers to follow the leaders in order to efficiently fulfill the passing-through mission. Asymptotic stability conditions are derived to guarantee the feasibility of the developed multi-USV controller. Finally, both numerical simulations and experiments are conducted to show the effectiveness of the proposed multi-USV control strategies.