一种基于深度学习故障诊断的飞行器分层容错控制方法

• Format: Patent
• Language: Chinese
• Published: 07.06.2022
• Subjects: CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PHYSICS; REGULATING

本发明公开了一种基于深度学习故障诊断的飞行器分层容错控制方法，属于飞行器控制领域；首先建立飞行器数学模型并分环写为姿态环和角速率环的仿射非线性形式；进一步考虑执行机构可能的故障，将其视作集总干扰，并对角速率环进行改写；接着结合固定时间扩张状态观测器和二次规划控制分配方法，构成传统容错控制器；然后，利用传统容错控制器进行大量飞行仿真，对深度学习故障诊断单元进行训练，用于诊断故障参数；最后，结合修正的固定时间扩张状态观测器、修正的容错控制律和鲁棒最小二乘控制分配，构成飞行器分层容错控制方法的控制框架，将最终的舵面偏角分配至考虑了故障后的各执行机构；本发明提升了控制性能以及容错性能。 The invention discloses an aircraft hierarchical fault-tolerant control method based on deep learning fault diagnosis, and belongs to the field of aircraft control. The method comprises the following steps: firstly, establishing an aircraft mathematical model and writing the aircraft mathematical model into affine nonlinear forms of an attitude ring and an angular rate ring; further considering possible faults of an execution mechanism, taking the possible faults as lumped interference, and rewriting an angular rate ring; combining a fixed time expansion state observer and a quadratic programming control distribution method to form a traditional fault-tolerant controller; then, using a traditional fault-tolerant controlle