For multi-agent systems (MASs) subject to input delays and time-varying state constraints, this work proposes a predefined-time consensus control scheme. The state constraints, input delays, and additional delays caused by event triggering are obstacles that affect the control performance and practicality of MASs. To achieve predefined-time consensus while strictly satisfying the state constraints, the scheme first tackles two key obstacles: input delays and unknown system nonlinearities. Specifically, the Pade approximation technique is adopted to reconstruct the input delay into a new error variable, and then adaptive neural networks (NNs) are used to compensate for both the delay-induced error and the unknown nonlinearities. Meanwhile, by leveraging an asymmetric barrier Lyapunov function (ABLF) and a predefined-time stability framework, the scheme fundamentally ensures that the MASs achieve state consensus within a predefined time and strictly satisfy the time-varying state constraints. Further, to solve the inherent communication burden problem in networked MASs and improve the applicability of the scheme, a dynamic event-triggered mechanism (DETM) is designed as a complementary optimization: based on a dynamically evolving internal variable, the DETM only triggers communication when necessary, thereby significantly reducing the number of control updates and inter-agent data transmissions. At the same time, the scheme eliminates the requirement for agents to continuously monitor their states to determine triggering conditions, further enhancing the practicality of the scheme. Simulation results verify the effectiveness of the proposed method.

The fixed-time formation control problem for uncertain nonlinear multiagent systems (MASs) with time-varying actuator failures is investigated. Actuator failures would have a huge impact on the system performance, especially when the actuator failures are time-varying, which may even cause system insecurity. In order to cope with time-varying actuator failures, a fixed-time convergence approach is proposed based on adaptive fuzzy control technology. Simultaneously, solving the above problems would aggravate the occupation of communication resources of the system. Therefore, a periodic adaptive event-triggered control scheme is developed, in which the triggering period can be adjusted adaptively. Furthermore, the convergence time of formation errors can be preset by applying the fixed-time control method. The convergence velocity can be accelerated immensely. Eventually, theoretical analysis and simulation illustrate that the proposed method can effectively compensate the time-varying actuator failures, and the MASs can achieve formation within a fixed time by using less communication resources.

A fixed‐time event‐triggered consensus control method is proposed for uncertain nonlinear multiagent systems with actuator failures. Since actuator failures, external disturbances and control gains are time‐varying and completely unknown, the effects of these system constraints on the system are completely unknown, which makes the implementation of fixed‐time tracking control challenging. To deal with these system constraints, radial basis function neural networks (RBFNNs) are applied to approximate the uncertain dynamics, and a boundary estimation method is presented to achieve adaptive compensation for them. Furthermore, considering that the implementation of this boundary estimation method requires a large number of communication resources, an event triggering mechanism is designed to reduce the update frequency of the controller. It is theoretically confirmed that using the proposed control scheme, all the followers can track the leader with sufficient accuracy in a predetermined time, and all the closed‐loop signals are bounded. Finally, the simulation experiments verify the theoretical results.

In our recent work, we propose an indirect adaptive fuzzy control scheme for uncertain unparametrizable nonlinear systems, which ensures that the number of adaptive laws does not increase with the number of fuzzy rules, and the time derivative of the chosen Lyapunov function is negative semidefinite. However, the scheme involves a class of high-order smooth functions and their time derivatives, which can make the controller structure become sophisticated especially when the relative degree of system is quite large. To overcome this problem, in the article, we propose a new direct adaptive fuzzy control scheme based on a class of reduced-order smooth functions. With the scheme, no partial-derivative term is involved in controller and virtual controllers, only one adaptive law is used regardless of the increase of fuzzy rules, and also the time derivative of Lyapunov function can be ensured negative semidefinite. It is proved that all closed-loop signals are bounded, and the output tracking error converges to a prescribed interval asymptotically. The transient tracking performance and robustness of the proposed control scheme are also considered. The effectiveness of the obtained results is illustrated by two practical control systems.

As the control tasks become complex, fulfilling such tasks cooperatively is the first choice in practice. In this article, the output consensus problem of heterogeneous multiagent systems is studied by deploying distributed observers in follower agents. Each observer in the follower only measures part of the leader’s output, which relieves the burden of a simple agent when the leader’s output is of large-scale dimensions. Then, all followers in the system work cooperatively to estimate the full state of the leader. By using parameterized Riccati equation and output regulation theory, sufficient conditions are given to design the distributed observers and the output consensus protocol. Finally, a numerical example is conducted to verify the obtained result.

This paper studies the leader-following consensus problem for a class of strict-feedback multiagent systems with unknown nonlinearities and state time-delays under directed topology. By using the backstepping technique, an adaptive consensus control protocol is proposed, where neural networks are employed to neutralize uncertain nonlinearities. To eliminate the effects of time-delays, Lyapunov–Krasovskii functionals, and Young’s inequalities are used in the design process. It is notable that the computation burden is dramatically alleviated by proposing a novel adaptive mechanism. For communication topology containing a spanning tree, the proposed controller guarantees that the consensus tracking error will converge to an adjustable neighborhood of the origin. Finally, a numerical example is provided to validate the effectiveness of our result.

Pore-scale facial features, similar to fingerprints and irises, are effective to distinguish human identities. Nonetheless, there is a few of pore-scale facial feature database, which constrains deep learning methods to be employed of generating pore-scale facial features. In this paper, we propose a novel method by merging motion smoothness constraint and 3D model constraint, to generate a large and complex pore-scale facial feature database. The proposed method uses a powerful motion smoothness constraint in feature matching, rather than the standard ratio-test, and utilizes 3D model constraint to eliminate the incorrect matching. In the experiment, by using the proposed method, the matched numbers into high matched quality are two times higher than state-of-the-art with the same local features.

Hybrid learning usually incorporate online learning activities since face-to-face class time has been reduces significantly. One of the major challenges in hybrid course is to maintain the same level of student-to-student and student to-instructor interaction as in traditional classes. Various strategies, which include online discussion, online journals, online tutorial, etc., have been developed to engage students and improve the interaction. Online discussion is one of the common strategies. Online discussion relies on asynchronous communication, where participants communicate by posting messages to the bulletin board system such as Blackboard Discussions. Asynchronous communication has the potential to make collaboration efforts more rewarding and productive for students by enabling them to communicate at any time and from any networked location. The focus of this ongoing study is to explore changes in students' level of high-order thinking and knowledge construction in asynchronous threaded discussions, the emergence of communication patterns and structures in the asynchronous communication network, and relationships between role centrality and concept centrality in student discourse. It will employ content analysis and social network analysis to pursue its research foci.