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An adaptive neural network fault‐tolerant control method is proposed for the non‐affine multi‐agent systems with actuator failure and input saturation. Through the transformation of the original multi‐agent systems, an equivalent model is proposed. Considering that the signal input of multi‐agent systems is limited, this article will solve the input saturation problem. But in a real system, the actuator would probably fail. Therefore, a fault tolerant control algorithm is proposed to solve the fault problem in the systems. In this article, an adaptive distributed controller is designed to eliminate the uncertainty of the systems. At the same time, the integral barrier Lyapunov function is selected to verify the stability. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel controller design method for stochastic nonlinear systems with unmodeled dynamics, uncertain parameters, and unknown covariance noise. In order to deal with these uncertainties, a new event‐based small gain controller is designed. The event‐triggered scheme can reduce the computational burden caused by disturbance and noise. By combining the technique of changing supply rate with small gain condition, a stochastic input‐to‐state practically stability Lyapunov function is obtained for the subsystem. Simultaneously, the changing supply function is used to treat with unmodeled dynamics. Based on backstepping process and adaptive control approach, the influence of unknown covariance noise is overcome. It is proved that the designed state‐feedback controller can ensure the overall system is stochastic input‐to‐state practically stability. [ABSTRACT FROM AUTHOR]

An adaptive neural network (NN) distributed tracking control strategy is proposed for nonlinear strict‐feedback multi‐agent systems, which is affected by time‐varying full state constraints. The influence of asymmetric state constraints is also considered. The constraint bounds are related to both state vectors and time. Different from the usual constant boundary or function boundary, the constraint boundary adopted in this article not only considers the influence of state variables, but also takes time into consideration, which makes the constraint boundary more flexible and more difficult to solve. In addition, in order to solve the disturbance of the unknown function in the system, this article adopts the NN technology to realize the approximation effect of the unknown function, and designs a adaptive distributed controller based on the asymmetric obstacle Lyapunov function and backward step method, the closed‐loop signals are proved to be CSUUB. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

This article puts forward an adaptive neural network fault‐tolerant control scheme under the state constraints for the flexible manipulator system with uncertain terms. The dynamic model of the system is described by partial differential equations. The tangent barrier Lyapunov functions are chosen in the design process for the sake of ensuring that all states in the system satisfy the constrained conditions. The uncertainties resulted from load mass, hub inertia, and bending stiffness in the system are approximated by using the universal approximation property of neural networks. The adaptive method is used to counteract the influence of joint motor actuator failure. At the same time, combining with the backstepping design framework to design effective controllers to assure that all signals in the closed‐loop system are bounded. Lyapunov stability analysis method is used to prove the stability of the system. Finally, the simulation results prove the availability of the raised control method. [ABSTRACT FROM AUTHOR]