BLSTM-Based Adaptive Finite-Time Output-Constrained Control for a Class of AUSs with Dynamic Disturbances and Actuator Faults

Abstract

In this paper, a BLSTM-based adaptive finite-time control structure has been constructed for a class of aerospace unmanned systems (AUSs). Firstly, a novel neural network structure possessing both the time memory characteristics and high learning speed, broad long short-term memory (BLSTM) network, has been constructed. Secondly, several nonlinear functions are utilized to transform the tracking errors into a novel state vector to guarantee the output constraints of the AUSs. Thirdly, the fractional-order control law and the corresponding adaptive laws are designed, and as a result, the adaptive finite-time control scheme has been formed. Moreover, to handle the uncertainties and the faulty elevator outputs, an inequality of the multivariable systems is utilized. Consequently, by fusing the output of the BLSTM, the transformation of the tracking errors, and the adaptive finite-time control law, a novel BLSTM-based intelligent adaptive finite-time control structure has been established for the AUSs under output constraints. The simulation results show that the proposed BLSTM-based adaptive control algorithm can achieve favorable control results for the AUSs with multiple uncertainties.

Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

“…Although the FBLS demonstrates good effectiveness and low computational cost, a major drawback is that its inherent feed-forward network structure limits its application to static problems. To enable dynamic mapping capabilities, Du et al (2021), Huang et al (2021) and Wang et al (2022) proposed a broad long short-term memory network and Hsu et al (2022) proposed a broad-learning recurrent Hermite neural network to enhance the learning process for modeling complex systems while maintaining the same width design as the BLS and FBLS.…”