论文标题
可提供模型参数的指数收敛的强大方法解决LTI植物识别问题
Robust method to provide exponential convergence of model parameters solving LTI plant identification problem
论文作者
论文摘要
这项研究的范围是线性时间不变（LTI）植物的参数识别的问题，其中1）输入信号不是频率富的，2）受到初始条件和外部干扰。使用特殊派生的微分方程用作滤波器的内存回归器扩展（MRE）方案，用于解决上述问题。这样的过滤器使我们能够获得有限的回归剂值，以满足初始激发（IE）的条件。使用MRE方案，使用遗忘因子的递归最小二乘（RLS）方法用于得出适应定律。已经证明了以下属性用于拟议的方法。如果满足IE条件，则：1）标识的参数误差为有限的值，并将指数收敛到零（如果没有外部干扰）或具有可调率的有限集（如果是它们的情况下），2）参数适应率是有限的值。通过模拟实验证明并证明了上述特性。
The scope of this research is a problem of parameters identification of a linear time-invariant (LTI) plant, which 1) input signal is not frequency-rich, 2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a limited regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares (RLS) method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: 1) the parameter error of identification is a limited value and converges to zero exponentially (if there are no external disturbances) or to a bounded set (in the case of them) with an adjustable rate, 2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.
