The unknown disturbances and the changing uncertainties bring difficulties for designing a stable attitude controller for UAV. In this paper, a novel adaptive fault-tolerant attitude control approach is designed based on the long short-term memory (LSTM) network for the fixed-wing UAV subject to the high dynamic disturbances and actuator faults. Firstly, the high dynamic disturbances can be compensated by the adaptive laws. Meanwhile, the actuator faults can be handled by the proposed adaptive fault-tolerant control (AFTC) scheme. Moreover, the LSTM network is introduced to approximate the unknown and time-accumulating nonlinearities. With the introduction of the one-to-one nonlinear mapping (NM), the output constraints in the control system can be guaranteed. Additionally, it can be demonstrated that the boundness of all the signals can be assured. At last, numerical simulation results are provided to illustrate the effectiveness of the proposed method.

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.