一类不确定非线性系统基于特征模型的复合自适应控制
Characteristic model-based composite adaptive control for a class of uncertain nonlinear systems
摘要点击 60  全文点击 73  投稿时间:2018-03-01  修订日期:2018-08-26
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DOI编号  10.7641/CTA.2018.80134
  2019,36(7):1137-1146
中文关键词  不确定  非线性系统  特征模型  复合自适应控制
英文关键词  uncertainty  nonlinear system  characteristic model  composite adaptive control
基金项目  国家自然科学基金重点项目,国家自然科学基金
学科分类代码  
作者单位E-mail
常亚菲 北京控制工程研究所 cyfei1106@163.com 
中文摘要
      针对一类不确定非线性系统的跟踪控制问题, 提出一种基于特征模型的复合自适应控制方法. 该方法的创新性在于基于系统的误差特征模型, 构建一种综合跟踪控制误差和模型估计误差的特征参量复合自适应律, 该自适应律用于控制器设计和分析, 可同时实现跟踪控制误差和模型估计误差的收敛. 此外, 为便于特征参量自适应律的设计和分析, 根据特征参量的慢时变特性, 将其视为未知标称常数项和时变误差项之和, 并且选用其中常数项的估计量作为自适应控制参数. 进一步, 为抑制特征参量中时变误差项对系统稳定性和模型估计误差收敛性的影响, 在控制器及复合自适应律设计中引入带饱和函数的非线性环节. 理论分析证明闭环控制系统稳定, 且跟踪控制误差和模型估计误差收敛到原点的一个邻域内. 仿真结果表明, 与现有仅根据模型估计误差调节的基于特征模型的自适应控制方法相比, 所提出的复合自适应控制方法具有更好的控制性能.
英文摘要
      A characteristic model-based composite adaptive control method is proposed for a class of uncertain nonlinear systems. The novelty here is that a composite adaptation law which consists of tracking-control-error and model-estimation-error for the characteristic parameters is designed, and it is further incorporated into the adaptive control system, which can retain convergence of the tracking-control-error and the model-estimation-error simultaneously. Considering the slow time-varying characteristics of the characteristic parameter, it is divided into a unknown nominal constant and a time-varying error, and estimation of the constant part is used for control. Further, to reject the undesired influence of the time-varying error on the system stability and convergence, a nonlinear term consisting a saturation function is added to the controller and the characteristic parameter adaption law. The system analysis guarantees that the closed system is stable, and the tracking-control-error and the model-estimation-error converges into the neighborhood of origin. Simulation results show that the newly suggested characteristic model-based composite adaptive method can achieve better control performance than other characteristic model-based conventional adaptive schemes.