Quantum mechanics

Control (management)

Engineering

Biology

Bandwidth (computing)

Control engineering

Actuator

Computer science

Distributed computing

Controller (irrigation)

Computer network

Physics

Fault tolerance

Mathematics

Agronomy

Artificial intelligence

Geometry

Observer (physics)

Convex hull

Control theory (sociology)

Lyapunov stability

Regular polygon

Differentiator

IEEE Transactions on Neural Networks and Learning Systems

This paper investigates the distributed finite-time fault-tolerant containment control problem for multiple unmanned aerial vehicles (multi-UAVs) in the presence of actuator faults and input saturation. The distributed finite-time sliding-mode observer (SMO) is first developed to estimate the reference for each follower UAV. Then, based on the estimated knowledge, the distributed finite-time fault-tolerant controller is recursively designed to guide all follower UAVs into the convex hull spanned by the trajectories of leader UAVs with the help of a new set of error variables. Moreover, the unknown nonlinearities inherent in the multi-UAVs system, computational burden, and input saturation are simultaneously handled by utilizing neural network (NN), minimum parameter learning of NN (MPLNN), first-order sliding-mode differentiator (FOSMD) techniques, and a group of auxiliary systems. Furthermore, the graph theory and Lyapunov stability analysis methods are adopted to guarantee that all follower UAVs can converge to the convex hull spanned by the leader UAVs even in the event of actuator faults. Finally, extensive comparative simulations have been conducted to demonstrate the effectiveness of the proposed control scheme.

Distributed Finite-Time Fault-Tolerant Containment Control for Multiple Unmanned Aerial Vehicles