摘要
提出了一种基于观测器的采样数据无模型自适应控制方法,将无模型自适应控制从离散时间系统扩展到连续时间系统。提出了一种动态线性化方法,将一类高阶非线性非仿射连续时间系统转化为基于采样数据的非线性仿射输入输出数据模型;为了将系统的不确定性和未知非线性转化为未知的时变参数向量和非线性余项,分别设计了参数自适应更新律和扩展状态观测器对其进行估计和补偿。由此,提出了一种新的采样数据自适应控制律,包含了采样周期和更多过去时刻的输入输出信息,从而可提高系统的控制性能。仿真结果表明所提出方法是有效的。
This paper proposes an observer based sampled-data model-free adaptive control method,which extends the model-free adaptive control from discrete-time systems to continuous-time systems.A dynamic linearization method is proposed to transform a class of highorder nonlinear affine continuous-time systems into a nonlinear affine input-output data model based on sampled data.The uncertainty and unknown nonlinearity of the system are transformed into unknown time-varying parameter vectors and nonlinear residual terms,which are estimated and compensated by the designed parameter adaptive update law and extended state observer,respectively.Therefore,a new adaptive control law for sampled data is proposed,which includes the sampling period and more input and output information of past moments,and thus can improve the control performance of the system.Simulation results verify the effectiveness of the proposed method.
作者
崔汶志
池荣虎
CUI Wenzhi;CHI Ronghu(College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《青岛科技大学学报（自然科学版）》
CAS
2024年第6期107-114,共8页
Journal of Qingdao University of Science and Technology:Natural Science Edition
基金
国家自然科学基金项目(62273192)
山东省科技型中小企业创新能力提升工程项目(2022TSGC1003)。
