具有多重随机干扰的NSV自适应鲁棒轨迹跟踪控制

doi:10.16182/j.issn1004731x.joss.22-0736

摘要/Abstract

摘要：

针对具有随机噪声输入干扰、泊松随机波动干扰和控制输入饱和的近空间飞行器 (near space vehicle， NSV)纵向轨迹控制模型，提出一种自适应鲁棒轨迹跟踪随机控制方案，实现对高度和速度参考信号的有效跟踪。对于外环轨迹控制，分别针对高度子系统和速度子系统，设计鲁棒随机控制器，通过数值计算方法对等效控制输入转化，得到内环姿态控制所需的姿态角参考信号。针对内环姿态控制问题，基于反步控制 (backstepping control， BC)方法、动态面控制 (dynamic surface control， DSC)技术和随机鲁棒控制方法，设计一种基于辅助系统的自适应鲁棒随机控制方案，实现随机干扰下的NSV姿态精确跟踪，衰减多重随机噪声干扰和参数不确定的影响，获得满意的鲁棒 $H ∞$ 控制性能。数值仿真进一步验证了所提方案的有效性。

关键词: NSV, 多重随机干扰, 输入饱和, $H ∞$ 控制, 轨迹跟踪

Abstract:

A stochastic control scheme of adaptive robust trajectory tracking is proposed for near space vehicle (NSV) with stochastic noise input disturbances, Poisson random fluctuation disturbances, and control input saturation. The effective tracking of the height and speed reference signals is realized. For the outer loop trajectory control, the robust stochastic controller is designed for the height subsystem and the speed subsystem respectively. Additionally, the required attitude angle reference signals for the inner loop attitude control are obtained by converting the equivalent control input via numerical calculation. For the inner loop attitude control problems, an adaptive robust stochastic control scheme based on an auxiliary system is designed according to the backstepping control (BC) method, dynamic surface control (DSC) technology, and stochastic robust control method. As a result, precise attitude tracking of NSV under stochastic disturbances is realized, and the influence of multiple stochastic noise disturbances and parameter uncertainty is weakened. The satisfactory robust H_∞ tracking control performance of NSV is obtained, and numerical simulations further verify the effectiveness of the proposed scheme.

Key words: NSV, multiple stochastic disturbances, input saturation, $H ∞$ control, trajectory tracking

中图分类号:

TP391

闫晓辉,姚玉武,吴昱桦等 . 具有多重随机干扰的NSV自适应鲁棒轨迹跟踪控制[J]. 系统仿真学报, 2023, 35(11): 2359-2372.

Yan Xiaohui,Yao Yuwu,Wu Yuhua,et al . Adaptive Robust Trajectory Tracking Control for NSV with Multiple Stochastic Disturbances[J]. Journal of System Simulation, 2023, 35(11): 2359-2372.

图/表 7

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