Adaptive Asymptotic Exponential Regulation for Nonparametric Uncertain Strict-Feedback Systems With Asymmetric Time-Varying Output Constraints
提出一种自适应渐近指数调节方法，用于非参数不确定严格反馈系统，在非对称时变输出约束下无需持续激励条件，通过新参数估计误差减速变换和非线性状态依赖函数保证约束不被违反。
This article provides an adaptive asymptotic exponential regulation solution for nonparametric uncertain strict-feedback systems under asymmetric time-varying output constraints. Unlike most existing methods for achieving exponential regulation, where the persistent excitation (PE) condition is in need, here in this work the restriction on the PE condition is removed by introducing a novel parameter estimation error deceleration transformation technique. In addition, more general nonparametric uncertainties but not parametric uncertainties are considered in this article. Furthermore, a new nonlinear state-dependent function (NSDF) is employed to ensure that the asymmetric time-varying output constraint cannot be violated all the time, which also allows the same convergence rate between the system state and transformed function. Additionally, all signals in the closed-loop system are ensured to be bounded. Both theoretical analysis and simulation examples validate the effectiveness of the proposed control algorithm.