Neural network-based distributed adaptive fault-tolerant containment control

In practical applications, multi-agent systems (MASs) often face challenges arising from incomplete knowledge of system dynamics, and agent actuators may suffer from faults such as partial failures or biased inputs. This paper investigates the fault-tolerant containment control problem for nonlinear MASs subject to actuator faults and proposes a neural network-based control approach. The system model is assumed to involve unknown nonlinearities, and the follower agents may experience actuator faults. Neural networks are employed to approximate the unknown nonlinear dynamics, and adaptive parameters are introduced and updated online based on the system evolution. An adaptive distributed fault-tolerant control protocol is developed by integrating neural network approximations, adaptive parameter adjustments, and relative state errors between neighboring agents. By dynamically tuning the control effort through the adaptive parameters, the proposed protocol effectively compensates for system nonlinearities and ensures the achievement of the containment control objective, even in the presence of actuator faults. Simulation results are presented to demonstrate the effectiveness of the proposed control strategy.