首页> 外文期刊>International Journal of Intelligent Computing and Cybernetics >DSC-backstepping based robust adaptive LS-SVM control for near space vehicle's reentry attitude
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DSC-backstepping based robust adaptive LS-SVM control for near space vehicle's reentry attitude

机译:基于DSC反推的鲁棒自适应LS-SVM控制用于近太空飞行器的再入姿态

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摘要

Purpose - The purpose of this paper is to propose a robust control scheme for near space vehicle's (NSV's) reentry attitude tracking problem under aerodynamic parameter variations and external disturbances. Design/methodology/approach - The robust control scheme is composed of dynamic surface control (DSC) and least squares support vector machines (LS-SVM). DSC is used to design a nonlinear controller for HSV; then, to increase the robustness and improve the control performance of the controller. LS-SVM is presented to estimate the lumped uncertainties, including aerodynamic parameter variations and external disturbances. The stability analysis shows that all closed-loop signals are bounded, with output tracking error and estimate error of LS-SVM weights exponentially converging to small compacts. Findings - Simulation results demonstrate that the proposed method is effective, leading to promising performance. Originality/value - First, a robust control scheme composed of DSC and adaptive LS-SVM is proposed for NSV's reentry attitude tracking problem under aerodynamic parameter variations and external disturbances; second, the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS-SVM to estimate aerodynamic parameter variations and external disturbances.
机译:目的-本文的目的是提出一种鲁棒的控制方案,用于在空气动力学参数变化和外部干扰条件下的近太空飞行器(NSV)的再入姿态跟踪问题。设计/方法/方法-鲁棒的控制方案由动态表面控制(DSC)和最小二乘支持向量机(LS-SVM)组成。 DSC用于设计HSV的非线性控制器。然后,增加鲁棒性并改善控制器的控制性能。提出了LS-SVM来估计总的不确定性,包括空气动力学参数的变化和外部干扰。稳定性分析表明,所有闭环信号都是有界的,LS-SVM权重的输出跟踪误差和估计误差以指数形式收敛到小型模型。研究结果-仿真结果表明,该方法是有效的,可带来令人鼓舞的性能。原创性/价值-首先,针对气动参数变化和外部干扰下的NSV再入姿态跟踪问题,提出了一种由DSC和自适应LS-SVM组成的鲁棒控制方案。其次,由于采用LS-SVM估计空气动力学参数变化和外部干扰,因此该方法比传统的动态表面控制具有更好的跟踪性能。

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