论文标题
具有放松激发条件和遗忘因子的非线性回归方程的新的最小二乘参数估计器
A New Least Squares Parameter Estimator for Nonlinear Regression Equations with Relaxed Excitation Conditions and Forgetting Factor
论文作者
论文摘要
在此注释中，提出了新的高性能最小二乘参数估计器。估算器的主要特征是：（i）对于所有可识别的线性回归方程，保证了全局指数收敛； （ii）它包含了一个遗忘因素，允许其保留随着时变的参数的警觉性； （iii）由于添加了一个混合步骤，因此它依赖于一组标量回归方程，以确保出色的瞬态性能； （iv）它适用于验证单调性条件的非线性参数化回归和具有切换时间变化参数的系统； （v）证明它相对于加性干扰是有限的输入结合状态稳定的； （vi）给出了估计器的连续和离散时间版本。文献中报道的一系列示例来说明了所提出的估计器的出色性能。
In this note a new high performance least squares parameter estimator is proposed. The main features of the estimator are: (i) global exponential convergence is guaranteed for all identifiable linear regression equations; (ii) it incorporates a forgetting factor allowing it to preserve alertness to time-varying parameters; (iii) thanks to the addition of a mixing step it relies on a set of scalar regression equations ensuring a superior transient performance; (iv) it is applicable to nonlinearly parameterized regressions verifying a monotonicity condition and to a class of systems with switched time-varying parameters; (v) it is shown that it is bounded-input-bounded-state stable with respect to additive disturbances; (vi) continuous and discrete-time versions of the estimator are given. The superior performance of the proposed estimator is illustrated with a series of examples reported in the literature.
