This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

This article addresses the cooperative fault-tolerant control problem for networks of stochastic nonlinear systems with actuator faults and input saturation. The fuzzy neural networks (FNNs) are employed to estimate the unknown functions and stochastic disturbance terms. To analyze the nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is constructed to estimate the saturation function. A novel adaptive fault-tolerant control protocol is proposed by using backstepping design technique. By using the stochastic Lyapunov functional strategy, it is proved that all the followers’ outputs eventually converge to a small neighborhood of the leader’s output, and all the signals in the closed-loop systems are bounded in probability. Finally, the performance of the proposed control strategy is illustrated through simulation.

This article investigates the bipartite consensus tracking control problem for nonlinear networked systems with antagonistic interactions and unknown backlash-like hysteresis. The generalized networked multiagent systems model is considered, in which every agent is an independent individual, and this model allows competitive and cooperative interactions to coexist. A Gaussian function is applied to simulate competition and cooperation among agents. Radial basis function (RBF) neural network (NN) is applied to estimate the unknown nonlinear function. By using backstepping technology, we propose an adaptive neural control protocol, which not only ensures that in the closed-loop system all the signals are bounded but also realizes bipartite consensus control. Finally, we present a simulation example to illustrate the effectiveness of the obtained result.

In this article, we research the output feedback consensus tracking control problem of nonlinear multiagent systems with unknown disturbance under event-triggered communication. An event-triggered mechanism with dynamic threshold utilizing only output information is proposed to reduce the controller updates and communication load between neighboring agent. This mechanism is combined with the state and disturbance observer to derive an output feedback-based adaptive backstepping event-triggered control protocol via dynamic filtering technique, which avoids the differentiation of virtual control protocol and eliminates Zeno behavior. Based on Lyapunov stability theory, it is proven that all signals of the closed-loop systems are bounded and output consensus tracking can be achieved. The effectiveness of the proposed control scheme is verified via a physical system example.

The primary objective of this article is to construct a distributed adaptive cooperative fault-tolerant control (FTC) protocol for dynamic output-constrained nonlinear multi-agent systems (MASs) subject to actuator fault and unknown output dead zone (UODZ) in the presence of stochastic false data injection (SFDI) attacks. By establishing a unified universal barrier function (UUBF) upon output, the original constrained system is transformed into a fully equivalent non-constrained system, which not only eliminates the restrictive condition of the constraint boundaries/functions but also solves the output constraints problem without changing the control structure. To circumvent the effects of unknown time-varying output coefficient derived from UODZ, we introduce the Nussbaum function in backstepping design. An adaptive FTC strategy based on the parameter adaptive law compensation approach that does not require the lower and upper bounds of the unknown fault coefficient is developed, and it can tolerate SFDI attacks. The result shows that the derived adaptive backstepping FTC protocol can not only achieve the cooperative control for MASs subject to UODZ under SFDI attacks, but also guarantee that all signals are bounded in the closed-loop system and the output constraints are not violated. Finally, simulation examples validate the validity of the developed schemes.

This article studies the bipartite tracking control problem of distributed nonlinear multiagent systems with input quantization, external disturbances, and actuator faults. We use the radial basis function (RBF) neural networks (NNs) to model unknown nonlinearities. Due to the fact that the upper bounds of disturbances and the number of actuator faults are unknown, an intermediate control law is designed based on a backstepping strategy, where a compensation term is introduced to eliminate external disturbances and actuator faults. Meanwhile, a novel smooth function is incorporated into the real distributed controller to reduce the effect of quantization on the virtual controller. The proposed distributed controller not only realizes the bipartite tracking control but also ensures that all signals are bounded in the closed-loop systems and the outputs of all followers converge to a neighborhood of the leader output. Finally, simulation results demonstrate the effectiveness of the proposed control algorithm.