This article investigates the prescribed-time containment control problem for multiple wheeled mobile robots (MWMRs) with unknown control gains. A novel and generalized prescribed-time stability theorem is established, significantly advancing existing frameworks and facilitating controller design. For each follower, a distributed containment observer is developed to ensure that the observer state converges precisely to the convex hull formed by multiple leaders within a prescribed time. This approach effectively transforms the containment control problem into a more tractable tracking control problem, meanwhile enabling local reconstruction of unmeasured neighbor states using available observer outputs. Then, the type-B Nussbaum function is employed to eliminate the effects of the time-varying unknown control coefficient. Based on the developed prescribed-time theorem and the time-varying parametric Lyapunov equation (PLE), the fully actuated controllers are designed for the MWMRs. It can guarantee that the containment errors converge to zero within the prescribed time. Finally, simulation results validate the effectiveness of the proposed method.

This article aims to solve the distributed online convex optimization (DOCO) problems subjected to multiple coupled constraints over time-varying (TV) unbalanced digraphs. The existing global constraint models and coupled constraint models, where the number of constraints is related to the number of nodes, are not sufficient to reflect the characteristics of multiple coupled constrained optimization problems. On account of this drawback, a multiple coupled constraint model is constructed, which contains several coupled constraints, only including a part of all nodes. In addition, practical TV scenarios commonly come with complex network connectivity, which requires diverse matrices for fusing various information. In view of connectivity requirements, a novel TV distributed primal–dual push–pull (TDPP) algorithm, which can convert two types of weight matrices to all onefold row stochastic (RS) matrices, is proposed to tackle multiple coupled constrained problems. Under some general and necessary assumptions and conditions, both desired sublinear dynamic regret and constraint violation can be acquired by a strict theoretical analysis. Finally, two numerical examples are utilized to verify the superiority and validity of the TDPP algorithm compared with similar algorithms.

Correlation filter (CF)-based approaches have gained widespread attention in the field of unmanned aerial vehicle (UAV) visual tracking due to their light-weight characteristics. However, CFs are prone to generating low-quality response in challenging UAV scenarios, e.g., fast motion and background clutter. In this paper, in order to model the tracker more robustly, we first conduct an effective regularization analysis from the perspectives of response- and background-learning. Specifically, to address response degradation, we propose a module for learning temporal consistency and reversibility of response, supplemented by a novel background-aware module to enhance the ability to learn from negative samples. In addition, we propose a fast coarse-to-fine scale search strategy, which alleviates the challenges in estimating bounding boxes under non-uniform aspect ratios. We have developed two tracker versions, namely RBLT and DeepRBLT, based on the depth of the features. Comprehensive experiments on four UAV benchmarks and one generic benchmark have indicated the superiority of our trackers compared to other state-of-the-art trackers, with enough speed for real-time applications.

Correlation filter (CF)-based approaches have been widely applied in online object tracking tasks for unmanned aerial vehicles (UAVs) due to their high computational efficiency and low memory consumption. One of the key steps is to perform correlation operations between the appearance model (AM) and the filter. However, as the difficulty in controlling the learning rate of the AM, most existing trackers are prone to causing degradation. In this paper, we propose a novel complementary AM (CAM) consisting of a primary model (PM) and a secondary model (SM). Specifically, the learning rates of the PM and SM are approximately complementary, allowing the CAM to consider both past and current information. Moreover, in order to take full advantage of historical information, a CAM-based reversibility reasoning approach is proposed for CF training. It can robustly handle the variations in object appearance. Then we further create a deep tracker by fusing convolutional features which demonstrates more outstanding performance. We also embed the CAM into two advanced trackers to validate the scalability of the CAM. Comprehensive experiments on six challenging UAV tracking benchmarks have indicated the superiority of our method compared to other 36 state-of-the-art CPU- and GPU-based trackers, with a speed of 45 FPS running on a cheap CPU.

For large-scale machine learning tasks, distributing data in multiple clients, and using distributed optimization algorithms with a parameter server can accelerate the training process. The federated average algorithm has been widely used for distributed optimization via training local models in parallel and aggregating local models in a server to obtain the global model. To further improve the performance of the federated average algorithm, a novel federated learning algorithm have been proposed in this article by embedding a lazy strategy in the distributed Adam-type algorithm. In the proposed algorithm, the learning rate is adjusted adaptively in local update and lazy update strategy is applied on the second-order momentum of clients to make the learning rate identical. The convergence of the proposed algorithm is provided for both convex and nonconvex loss functions. Experiments have been conducted on MNIST digit recognition data set and CIFAR-10 data set. Experimental results show that the proposed algorithm can significantly reduce the communication overhead, thereby reduce the training time by 60% for CIFAR-10 data set, and the proposed algorithm achieve better performance than the federated average algorithm and its momentum version.

We provide new criterion and a class of strict Lyapunov functions (SLFs) for time-varying systems (TVSs) with zero homogeneity. The definition of homogeneous auxiliary system is given, where it is assumed that certain homogeneous functions are admitted with their derivatives, in terms of the error systems, bounded by periodic functions. Based on the homogeneous auxiliary system, sufficient conditions of uniform asymptotical stability for TVSs are formulated using the homogeneity framework. Unlike existing results, where non-SLFs or persistence of excitation condition are required, our criterion is greatly relaxed for broad classes of systems. The utility of our result is illustrated by case-study of pendulum stability with quasi-periodical frictions.

This paper addresses the adaptive synchronization problem of networked mechanical systems in task space with time-varying communication delays, where both kinematic and dynamic uncertainties are considered and the information flow in the networks is represented by a directed graph. Based on a novel coordination auxiliary system, we first extend existing feedback architecture to achieve synchronization of networked mechanical systems in task space with slow-varying delays. Given that abrupt turns arise for the delays sometimes, we then propose a delay-independent adaptive synchronization control scheme which removes the requirement of the slow-varying condition. Both of the two control schemes are established with time-domain approaches by using Lyapunov-Krasovskii functions. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.