Distributed Prescribed Performance Control for MUAVS Under Cyber-Attacks and Disturbances via Adaptive NN and Disturbance Observer

This paper investigates the cooperative control strategy of multiple unmanned aerial vehicle system (MUAVS) under cyber-attacks and external disturbances to achieve the high performance and high-precise tracking control. In view of system description and optimization methodology, a distributed finite time prescribed performance (DFTPP) control strategy is proposed with the adaptive neural network (ANN) and disturbance observer (DO) to handle the adverse influences of cyber-attacks and disturbance, which will destroy the stability of MUAVS. To address the strong coupling between the altitude states, the stepwise virtual control signals were designed through the distributed backstepping law. The finite-time prescribed performance function simultaneously predefines the error boundary and the convergence time, which constrains the tracking errors in a residual set of 0 and the settling time. Through Lyapunov asymptotic function, tracking errors of MUAVS were proved to be uniformly ultimately bounded (UUB) and converge to nearly 0. The experimental results demonstrate the effectiveness and superior performance of the proposed control algorithm.