基于输入输出数据的预设性能自适应参数估计

Adaptive prescribed performance parameter estimation with input/output data

中文关键词  参数估计  预设性能  非线性系统  系统输入输出

英文关键词  parameter estimation  prescribed performance  nonlinear system  system input-output

中文摘要

      针对现有自适应参数估计方法多依赖系统状态信息,且无法定量分析参数误差收敛性能(如收敛速度、最 大误差)等问题,本文提出一种基于系统输入输出数据的非线性系统自适应参数估计新方法.首先,为避免使用系统 状态信息,引入K滤波操作,构建系统未知参数与输入输出数据之间的映射关系.其次,设计辅助变量,提取参数估 计误差信息来设计自适应律.为提高参数估计误差收敛性能,设计预设性能函数及误差等价转化机制,实现对参数 估计误差瞬态及稳态收敛性能的预先设计.最后,构建基于参数估计误差信息的可预设收敛性能的自适应律,并利 用Lyapunov稳定性分析方法证明参数估计误差收敛性.通过对比仿真及实验证明了所提方法的正确性和有效性.

英文摘要

      Although numerous adaptive parameter estimation methods have been developed, most of them rely on the system state information and cannot quantitatively analyze the parameter error convergence performance (e.g., convergence rate, maximum overshoot, etc). In this sense, this paper will propose a novel adaptive prescribed performance parameter estimation method with nonlinear system input/output. To avoid using system state information, the K-filter operation is first introduced to obtain a mapping from the system input/output to the system unknown parameters. Then, a set of auxiliary variables are designed to extract the parameter estimation error information with simple algebraic calculation for parameter estimation. To improve the parameter estimation error convergence, the prescribed performance function (PPF) and associated error transformation mechanism are suggested to predefine the transient performance and steady-state performance of the parameter estimation error. Then, a novel adaptive parameter estimation algorithm is presented and the parameter estimation error convergence is rigorously proved by using the Lyapunov theory. Finally, numerical simulation and experimental results are also provided to show the superiority of the proposed method over some available results.