Radio FD has emerged as an attractive technique capable of doubling the spectral efficiency over half duplex. However, for signal reception, an FD node needs to suppress its transmitter's signals quite significantly. In point to point communication systems, these transmitter signals are termed self-interference. When working with an FD mobile network, the self-interference problem becomes much more complicated because the receiver of an FD base station (BS) receives interference not only from its BS transmitter in its cell, but also from those in the surrounding cells. For the UL channel, self-interference extends to the problem of multiple interference. And, a similar interference problem can be found among the MSs over a DL channel. In both cases, the interference owing to the FD implementation spreads beyond the scope of the self-interference. This article describes the development of FD BSs that use antenna arrays to deal with the BSs' interference, and thus enable FD communication over the UL channel, where the theoretical focus is placed on how to use the antenna array to nullify the multiple interference and receive the signals of the desired MSs simultaneously. To complete the system construction, FD MSs have also been developed to enable DL transmission. A prototype system is described for the scenario of two cells and one FD MS for tests of FD communication over UL channels and DL channels in terms of video performance. Good video quality is demonstrated at both the BS and MS.

In this article, the event-triggered saturation-tolerant control problem of nonlinear multi-agent systems (MASs) is investigated based on the finite-time fuzzy composite learning approach. Specifically, a novel concept, named as deferred saturation-tolerant prescribed performance control, is proposed, which guarantees the flexible prescribed performance in the face of input saturation, while there are no needs of initial restrictions on distributed errors. Moreover, by extracting weight errors from filtering operations and auxiliary variables, a finite-time fuzzy composite learning rule driven by weight and distributed errors is developed for improving the learning performance and ensuring that unknown nonlinearities are precisely estimated. Then resorting to event-triggered communication mechanism, signal transmissions among connected agents only occur when triggering conditions are satisfied, contributing to a reduced communication burden. Finally, simulations with comparative studies are provided to confirm the effectiveness and superiority of the proposed method.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

This paper presents an adaptive fuzzy bipartite consensus tracking control scheme for nonlinear multi-agent systems (MASs) with dead-zone inputs and actuator faults. The consensus problem for unbalanced communication topology of the MASs is difficult to deal with. First, the hierarchical algorithm is introduced to transform the consensus tracking problem for multi-agents into the tracking problem of the single agent. Then, the novel error transformation and coordinate transformation are proposed based on the hierarchical design theory, leading to the condition of global Laplacian matrix information unnecessary so that the computational difficulty and communication burden are significantly reduced. Besides, the control performance would be seriously affected by the input constraints such as dead-zone and faults. There is a balance between the input constraints and the output constraints for MASs, but these two constraints are handled independently in the most existing works. Thus, a flexible performance function is developed to balance the system performance and the stability requirements by flexible signal switching. The proposed adaptive fuzzy control scheme ensures that not only the influence of the dead-zone and various types of actuator faults is eliminated, but also the preassigned finite-time tracking performance is achieved. Finally, the simulation experiment is conducted to verify the effectiveness of the established control scheme.

This paper proposes an all agents connectivity-preserving method and a data-filter memory-based event-triggered (ET) strategy to design cooperative learning control algorithm. Firstly, a type of error functions are proposed to achieve that all agents are within the communication boundary, which do not limit the initial values of agents. The designed method can dynamically adjust the boundary function based on the initial position of agents and gradually converge to the preset communication boundary, without abandoning any agents. Secondly, a data-filter memory-based ET strategy is proposed, which includes the designed error-based data filtering rules. The filtering rules avoid the problem that the ET mechanism stores abnormal historical data when the system has faults. Moreover, the presented error-based cooperative learning adaptive protocol does not need to presuppose that neighbor weights are bounded, reducing the conservatism. Finally, based on the above works, the constructed ET control method can achieve control objectives, and the effectiveness is demonstrated through theoretical analysis and simulation results. Note to Practitioners —In practice, multiagent systems (MASs) communicate through wireless communication mostly, which inevitably leads to an upper limit on the communication distance between agents. Exceeding the limitation of communication module will cause the problem that MASs cannot achieve signal transmission. Therefore, it is crucial to design appropriate constraint methods for different initial positions of agents to ensure that all agents can enter the predetermined communication boundary. In addition, the memory ET strategy can calculate the threshold of conditions based on the historical data. But in practice, it cannot guarantee the continuous normal operation of the system. If there are abnormal values in the stored signal data, this will lead to unreasonable calculation of the threshold. In response to this issue, this paper considers additional data filtering rules to avoid storing data when systems exist faults. Meanwhile, the cooperative learning algorithm proposed in this paper can adjust the learning information weights based on the control performance of neighbor agents, and remove the assumption of bounded neighbor learning laws.

This research considers the predefined-time adaptive fuzzy formation control issue for high-order nonlinear multi-agent systems. By applying fuzzy logic systems, the systems unknown nonlinear functions can be approximated. To refrain from “explosion of complexity problem”, a novel dynamics surface for high-order nonlinear multi-agent systems is presented. Further, to minimize the communication burden, an event-triggered mechanism suitable for high-order nonlinear multi-agent systems is applied in the control methods. With the help of the backstepping design scheme and the adding power integral method, an adaptive fuzzy predefined-time formation control approach is proposed so that all signals in the considered systems are bounded and realize the desired formation control within predefined time. The illustrative examples are presented to verify the validity of the suggested method.