一类非参数不确定运动系统的自适应空间重复学习控制  

Adaptive spatial repetitive learning control for a class of nonparametric uncertain motion systems

作　　者：陈强[1] 胡如海 胡轶[1] CHEN Qiang;HU Ruhai;HU Yi(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023)

机构地区：[1]浙江工业大学信息工程学院,杭州310023

出　　处：《高技术通讯》2022年第6期565-575,共11页Chinese High Technology Letters

基　　金：国家自然科学基金(61973274);浙江省自然科学基金(LY20E070007);高端装备先进感知与智能控制教育部重点实验室开放课题(GDSC202010)资助项目。

摘　　要：针对一类在空间区间内重复运行的非参数不确定运动系统,本文提出一种基于李雅普诺夫方法的自适应空间重复学习控制(SRLC)策略。首先,引入空间微分算子将系统从时间域转换到空间域形式,并将系统非参数不确定性划分为空间周期不确定和非周期不确定两部分。其次,设计全饱和空间重复学习律估计和补偿空间周期非参数不确定部分,同时保证被估计值的连续性和有界性。此外,将非周期不确定部分转换为参数化不确定形式,并设计其上界参数的空间自适应更新律用以补偿系统非周期不确定。最后,设计控制器确保系统输出能够精确跟踪空间周期性期望信号。仿真结果验证了所提方法的有效性。In this paper,an adaptive spatial repetitive learning control(SRLC)scheme is proposed based on Lyapunov method for a class of nonparametric uncertain motion systems running repeatedly in the space domain.Firstly,spatial differential operators are introduced to transform the system from the time domain to the space domain,and nonparametric system uncertainty can be divided into two parts:spatial periodic uncertainty and nonperiodic uncertainty.Secondly,a fully saturated spatial repetitive learning law is presented to estimate and compensate for the spatial periodic nonparametric uncertainty.Meanwhile,the continuity and boundedness of the estimated value can be guaranteed by the presented repetitive learning law.In addition,the nonperiodic uncertainty is transformed into the parametric uncertainty and compensated by designing a spatial adaptive update law of the upper bound parameter.Lastly,a repetitive learning controller is designed to ensure that the system output can accurately track the spatial periodic expected signal.Simulation results are given to verify the effectiveness of the proposed method.

关 键 词：空间重复学习控制(SRLC) 自适应控制 非参数不确定 空间运动系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]