基于广义最小二乘法的随机非线性系统自适应跟踪

摘要

The estimation-based modular design was applied for the adaptive tracking problems for a class of stochastic nonlinear systems in the form of parametric-strict-feedback driven by Wiener noises of unknown covariance. The CLF (control Lyapunov function) was constructed. The ISS (input-to-state stability) controller with strong parametric robustness was designed and a complete controller-identifier separation was achieved. According to the Swapping technique, filters were designed to convert dynamic parametric models into static models. In view of unknown covariance, the generalized least-square algorithm was employed for discussing methods of estimating the covariance.%运用基于估计的模块化设计思想,研究了受方差不确定Wiener噪声干扰的参数严格反馈形式非线性系统对已知信号的自适应跟踪问题.构造控制Lyapunov函数(control Lyapunov function,CLF),设计了具有鲁棒稳定特性的输入状态稳定(input-to-state stability,ISS)控制器,确保系统满足控制器-辨识器分离；运用Swapping技术设计辨识器模块,将动态参数模型转换成静态模型,考虑到方差不确定,采用广义最小二乘算法进行参数估计,讨论了系统方差的估计方法.