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对于小行星绕飞任务的探测器姿态控制问题,已有方法大都考虑了干扰力矩和参数不确定等因素,而忽视了执行器故障情况。针对执行器故障条件下的小行星探测器姿态控制问题,提出了一种基于自适应迭代学习的容错控制方法。所设计的控制器包括两部分:其一针对执行器故障,设计了自适应迭代学习控制器,采用类滑模的思想和自适应迭代学习算法对控制器参数进行调整,进而补偿执行器故障带来的影响,保证系统在控制输出不足情况下的高精度姿态稳定性;其二针对探测器参量变化、外部环境干扰等不确定情况,设计了基于自适应神经网络的迭代学习控制器,采用径向基函数(Radial Basis Function,RBF)神经网络对系统非线性部分进行逼近,同时对控制器参数进行自适应迭代学习调整,进而保证系统在不确定情况下的动态性能。数值仿真结果表明该控制器能够有效抑制外部环境干扰和内部参数变化带来的不利影响,在执行器部分失效甚至完全失效故障情况下,仍能保证系统的鲁棒性并实现误差在10-2数量级内的较高姿态控制精度。
For attitude control of astronauts around the fly mission, most of the existing methods consider such factors as disturbance torque and parameter uncertainty, while neglecting the actuator fault condition. Aiming at the attitude control of asteroid detectors under actuator fault conditions, a fault-tolerant control method based on adaptive iterative learning is proposed. The design of the controller includes two parts: one for the actuator failure, designed adaptive iterative learning controller, the use of sliding mode of thinking and adaptive iterative learning algorithm to adjust the controller parameters, and then compensate for the actuator fault zone To guarantee the high-precision attitude stability of the system under the condition of insufficient control output. Secondly, an iterative learning controller based on adaptive neural network is designed to deal with the uncertainties such as the change of detector parameters and the disturbance of external environment. The Radial Basis Function (RBF) neural network is used to approximate the non-linear part of the system and adjust the parameters of the controller adaptively and iteratively, so as to ensure the dynamic performance of the system under uncertain conditions. The numerical simulation results show that the controller can effectively restrain the adverse effects caused by the disturbance of the external environment and the change of internal parameters. When the actuator is partially failed or even completely failed, the robustness and error of the system can be guaranteed. Higher order control accuracy within the order of magnitude.