高空台空气起动系统的无模型自适应迭代学习控制

岑游彬; 齐义文; 李鑫; 彭晋; 李贺

doi:10.3969/j.issn.2095-1248.2023.05.010

沈阳航空航天大学学报 ›› 2023, Vol. 40 ›› Issue (5) : 75 -81. DOI: 10.3969/j.issn.2095-1248.2023.05.010

信息科学与工程

高空台空气起动系统的无模型自适应迭代学习控制

岑游彬 ¹ ,
齐义文 ¹ ,
李鑫 ² ,
彭晋 ² ,
李贺 ¹

作者信息 +

Model-free adaptive iterative learning control for high-altitude air-starting system

Youbin CEN ¹ ,
Yiwen QI ¹ ,
Xin LI ² ,
Jin PENG ² ,
He LI ¹

Author information +

文章历史 +

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摘要

针对高空台空气起动系统存在非线性、强耦合性、大延迟、不确定性等问题，提出了一种基于Smith估计的高空台空气起动系统无模型自适应迭代学习控制算法。首先，利用Smith估计器，估计和补偿系统延迟误差；其次，基于系统输入和输出数据，获得高空台空气起动系统的动态线性化数据模型；最后，提出高空台空气起动系统的无模型自适应迭代学习控制方法，通过不断更新每轮的控制器，实现系统的快速精准调节。仿真结果表明，随着迭代次数的增加，所提出方法能减小系统跟踪误差，修正系统输出跟踪轨迹。同时，相较于单纯的无模型自适应迭代学习控制算法，加入Smith估计器补偿延迟后，系统动态性能可得到显著改善，验证了所提算法的可行性和有效性。

Abstract

Aiming at the problems of nonlinearity，strong coupling，large delay and uncertainty of the air-starting system at high-altitude，a model-free adaptive iterative learning control algorithm for air -starting system based on Smith estimation was proposed. Firstly，the Smith estimator was used to estimate and compensate for the system delay error. Secondly，based on the input and output data of the system，the dynamic linearized data model of the high-altitude platform air starting system was obtained. Finally，a model-free adaptive iterative learning control method for the high-altitude platform air starting system was proposed，which continuously updated the control law for each round to achieve rapid and accurate adjustment of the system. Simulation results show that with the increasing of iteration times，the proposed method can reduce the system tracking error and correct the system output tracking trajectory. Meanwhile，compared to the simple model-free adaptive iterative learning control algorithm，the dynamic performance of the system is improved significantly after joining Smith estimator，which verifies the feasibility and effectiveness of the proposed algorithm.