The adaptive fixed-time consensus problem for a class of nonlinear multi-agent systems (MASs) with actuator faults is considered in this paper. To approximate the unknown nonlinear functions in MASs, radial basis function neural networks are used. In addition, the first order sliding mode differentiator is utilized to solve the “explosion of complexity” problem, and a filter error compensation method is proposed to ensure the convergence of filter error in fixed time. With the help of the Nussbaum function, the actuator failure compensation mechanism is constructed. By designing the adaptive fixed-time controller, all signals in MASs are bounded, and the consensus errors between the leader and all followers converge to a small area of origin. Finally, the effectiveness of the proposed control method is verified by simulation examples.

This article concentrates on the adaptive neural control approach of $n$ -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

This paper addresses the problem of distributed fuzzy adaptive pinning control for multiagent systems with output saturation via adopting the dynamic event-triggered mechanism. With the framework of the backstepping technique, a new adaptive pinning control protocol is developed, where an effective fuzzy strategy and a class of tuning functions are integrated into the control protocol to reduce the required design adaptive parameters. Then, the output saturation of nonlinear multiagent systems is first addressed via the signal compensation method. Moreover, a dynamic event-triggered mechanism about control information is proposed, where two dynamic laws are designed to further increase the adjustment margin of the controller. Finally, simulation results are utilized to demonstrate the suitability and feasibility of theoretical algorithm.

The state estimation problem of heterogeneous multi-agent systems with random transport protocol is investigated in this paper. Due to the dependency of the agent dynamics and the random sparse structure induced by the random transport protocol, the optimal state estimation design becomes complex and challenging. An optimal state estimator is successfully designed by the Hadamard product and gradient method. Based on the analysis of the matrix functions, a sufficient condition is established to guarantee that the average estimate error covariance is limited. Finally, a numerical example and a smart grid model are utilized to demonstrate the effectiveness of the deigned estimator.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step $n$ and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

In this article, we focus on the distributed group asynchronous consensus problem for cyber-physical systems (CPSs) with unknown dynamics and switching topologies. This article considers a class of networked distributed CPSs composed of cloud computing systems, and it is subjected to delay detection models and topology switching. First, an asynchronous switching observer is tailored for each group of agents to guarantee the precise acquisition of the leader's information. Further, we introduce a model-free adaptive control method to devise controllers for each group of agents, which can continue to learn adaptively only from the agent's input and output data without knowing the agent dynamics. Finally, the stability of both the observer and controller are proved, respectively. The observers' and controllers' effectiveness is further confirmed by the simulation results.