Neural-based fixed-time composite learning control for multiagent systems with intermittent faults.

In this paper, a distributed fixed-time composite learning control problem is addressed for nonlinear multiagent systems (MASs) subject to intermittent actuator faults. First, a distributed estimator is constructed for followers that are unable to communicate directly with the leader. Then, instead of using the traditional adaptive neural network (NN) algorithm, a predictor-based composite learning technique is proposed, which incorporates the prediction error into the NN update law to enhance the estimation accuracy of the unknown nonlinearity. Furthermore, an adaptive fault-tolerant control compensation mechanism is developed for intermittent faults that may occur indefinitely and frequently. To guarantee that all signals of the closed-loop system are bounded in fixed time, a nonsingular fixed-time fault-tolerant controller in the form of quadratic function is established. Finally, simulation results confirm the effectiveness of the presented algorithm. • This paper presents a singularity-free fixed-time NN algorithm for nonlinear MASs, and a composite learning algorithm is established to improve the approximation accuracy of nonlinear functions by introducing a prediction error into NN update law. • For followers without access to the leader, a local estimator is utilized to estimate the leader information. Therefore, the present control method avoids the emergence of coupling terms between agents during the controller design. • This paper considers intermittent actuator faults that may occur indefinitely and frequently, posing significant challenges. [ABSTRACT FROM AUTHOR]