输出非线性方程误差类系统递推最小二乘辨识方法

Recursive least squares identification methods for output nonlinear equation-error type systems

随着控制技术的发展,控制对象的规模越来越大,使得辨识算法的计算量也越来越大.对于结构复杂的非线性系统,特别是包含未知参数乘积的非线性系统,使得过参数化辨识方法的参数数目大幅度增加,辨识算法的计算量也急剧增加,因此探索计算量小的参数估计方法势在必行.针对输出非线性方程误差类系统,讨论了基于过参数化模型的递推最小二乘类辨识方法;为减小过参数化辨识算法的计算量和提高辨识精度,分别利用分解技术和数据滤波技术,研究和提出了基于模型分解的递推最小二乘辨识方法和基于数据滤波的递推最小二乘辨识方法.最后给出了几个典型辨识算法的计算量、计算步骤、流程图.

With the development of control technology, the scales of the control systems become larger and larger, so does the computational load of the identification algorithms.For nonlinear systems with complex structures, especially for the nonlinear systems that contain the products of the unknown parameters of the nonlinear part and linear part, the sizes of the involved matrices in the over-parameterization model based least squares methods greatly in- crease, this makes the computational amount of the identification algorithms increase dramatically. Therefore, it is necessary to explore new parameter estimation methods with less computation. For output nonlinear equation-error type systems, this paper discusses the over-parameterization model based recursive least squares type identification algorithms; in order to reduce computational loads and improve the identification accuracy, this paper uses the de- composition technique and the filtering technique and presents the model decomposition based reeursive least squares identification methods and the filtering based reeursive least squares identification methods. Finally, the computational efficiency,the computational steps and the flowcharts of several typical identification algorithms are discussed.