Fast fixed-time prescribed performance adaptive control for multi-aircraft formation reconfiguration based on BELNN disturbance observer

In order to enhance the control performance and formation security, this paper studies the transient (overshoot and convergent speed) and steady performance constraints on formation reconfiguration of multi-aircraft system (MAS) under leader-follower structure. A fast fixed-time prescribed performance adaptive control (FFTPAC) strategy is proposed, which composes of a brain emotional learning neural network (BELNN) disturbance observer, a preset-time polynomial prescribed performance function and a fast variable power fixed-time reaching law. Firstly, BELNN is creatively designed to estimate external disturbances with simple structure and fast learning ability, which can reduce chattering and initial estimated error remarkably compared with high-order sliding mode disturbance observers. Secondly, a novel preset-time polynomial prescribed performance function is presented to ensure that formation tracking error changes within the prescribed region and converges to a specified small range at a time set in advance directly. Moreover, initial change form of the tracking error is flexible to avoid excessive initial control input value in traditional prescribed performance control. Thirdly, in order to promote efficiency of the formation reconfiguration, a novel fast variable power fixed-time reaching law is proposed to increase convergent rate of the system, where a novel nonlinear function involved system states and a variable power term are introduced based on constant power reaching law. The FFTPAC strategy can achieve excellent control performance and robustness under unknown disturbances during formation reconfiguration of MAS. Finally, the superiority of FFTPAC is verified by comparing with existing advanced methods.