论文标题
一类非线性系统的模型识别和自适应状态观察
Model Identification and Adaptive State Observation for a Class of Nonlinear Systems
论文作者
论文摘要
在本文中，我们考虑了输出回馈规范形式中一类连续时间非线性系统的状态估计和模型识别的联合问题。提出了一个自适应观察者，该观察者结合了扩展的高增益观察者和离散时间标识符。扩展的观察者为标识符提供了一个数据集，该数据集允许识别系统模型，并且标识符根据新的估计模型对扩展观察者进行调整。标识符的设计作为系统识别问题，并提出了足够的条件，如果满足，则允许将不同的识别算法用于适应阶段。通过基于小波的标识符进行了递归最小二乘和多解决黑盒标识的情况。提供了稳定性结果，将渐近估计误差与标识符的预测能力有关。相对于无噪声估计，还建立了影响系统方程和测量的加性干扰的鲁棒性。
In this paper we consider the joint problems of state estimation and model identification for a class of continuous-time nonlinear systems in output-feedback canonical form. An adaptive observer is proposed that combines an extended high-gain observer and a discrete-time identifier. The extended observer provides the identifier with a data set permitting the identification of the system model and the identifier adapts the extended observer according to the new estimated model. The design of the identifier is approached as a system identification problem and sufficient conditions are presented that, if satisfied, allow different identification algorithms to be used for the adaptation phase. The cases of recursive least-squares and multiresolution black-box identification via wavelet-based identifiers are specifically addressed. Stability results are provided relating the asymptotic estimation error to the prediction capabilities of the identifier. Robustness with respect to additive disturbances affecting the system equations and measurements is also established in terms of an input-to-state stability property relative to the noiseless estimates.
