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

Shiyi Huang,Caisheng Wei,Zheng Wang,Zhaohui Yuan,Xiaofei Chang,Lulu Rong,Omar-Jacobo Santos

doi:10.1155/2021/2221495

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.

Highlights

As is well known, the adaptive control methodology can adjust control parameters automatically according to the variation of the environment and the system uncertainties and can improve the adaptability with respect to the uncertain factors mentioned above [1,2,3,4,5]
In [6], a novel adaptive switching dynamic surface control (DSC) strategy is first presented for fractional-order nonlinear systems in the nonstrict feedback form with unknown dead zones and arbitrary switching
In [6], a novel adaptive prescribed performance tracking control scheme has been proposed for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance

Summary

Introduction

The adaptive control methodology can adjust control parameters automatically according to the variation of the environment and the system uncertainties and can improve the adaptability with respect to the uncertain factors mentioned above [1,2,3,4,5]. In [6], a novel adaptive switching dynamic surface control (DSC) strategy is first presented for fractional-order nonlinear systems in the nonstrict feedback form with unknown dead zones and arbitrary switching. In [6], a novel adaptive prescribed performance tracking control scheme has been proposed for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. In [7], a novel event-triggered-based fuzzy adaptive finite-time control method has been proposed for stochastic nonlinear nonstrict feedback systems with unmodeled dynamics. In [9], a novel adaptive fault-tolerant attitude control approach has been designed based on the long short-term memory (LSTM) network for the fixedwing UAV subject to the high dynamic disturbances and actuator faults. In [10], a novel evasion-faced fast adaptive

Objectives

Results

Conclusion