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BLSTM-Based Adaptive Finite-Time Output-Constrained Control for a Class of AUSs with Dynamic Disturbances and Actuator Faults
Mathematical Problems in Engineering ( IF 1.430 ) Pub Date : 2021-09-23 , DOI: 10.1155/2021/2221495
Shiyi Huang 1 , Lulu Rong 2 , Xiaofei Chang 3 , Zheng Wang 3, 4, 5 , Zhaohui Yuan 1 , Caisheng Wei 6
Affiliation  

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.

中文翻译:

基于 BLSTM 的自适应有限时间输出约束控制一类具有动态扰动和执行器故障的 AUS

在本文中,已经为一类航空航天无人系统(AUS)构建了基于BLSTM的自适应有限时间控制结构。首先,构建了一种兼具时间记忆特性和高学习速度的新型神经网络结构,即广泛的长短期记忆(BLSTM)网络。其次,利用几个非线性函数将跟踪误差转换为新的状态向量,以保证 AUS 的输出约束。第三,设计了分数阶控制律和相应的自适应律,从而形成了自适应有限时间控制方案。此外,为了处理不确定性和故障电梯输出,使用了多变量系统的不等式。因此,通过融合 BLSTM 的输出,结合跟踪误差的变换和自适应有限时间控制律,建立了一种新型的基于BLSTM的智能自适应有限时间控制结构,用于输出约束下的AUS。仿真结果表明,所提出的基于BLSTM的自适应控制算法能够对具有多种不确定性的AUS取得良好的控制效果。
更新日期:2021-09-23
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