论文标题
设置具有保证模拟精度的线性系统的成员资格识别
Set Membership identification of linear systems with guaranteed simulation accuracy
论文作者
论文摘要
使用有限的测量噪声影响的有限的采样数据，考虑了线性系统的模型识别问题，并具有未知的界限。目的是在最坏情况模拟误差范围内识别单步预测模型及其准确性。为此，设置成员身份识别框架是利用的。得出理论结果，使人们可以估计噪声结合和系统衰减速率。然后，使用这些数量和数据来定义可行的参数集（FPS），该参数集包含与可用信息兼容的所有可能模型。在这里，估计的衰减速率用于通过添加约束来强制实施模型脉冲响应的所需收敛行为来完善标准FPS公式。此外，得出了无限的未来视野的保证模拟误差界限，从而改善了与有限模拟范围有关的最新结果。这些界限是确保已确定模型渐近稳定性的结果和方法的基础。最后，通过数值优化识别所需的一步模型，并评估相关的仿真误差界。输入输出和状态空间模型结构均已解决。该方法在一个数值示例和自主滑翔机滚动速率动力学的现实实验数据上展示。
The problem of model identification for linear systems is considered, using a finite set of sampled data affected by a bounded measurement noise, with unknown bound. The objective is to identify one-step-ahead models and their accuracy in terms of worst-case simulation error bounds. To do so, the Set Membership identification framework is exploited. Theoretical results are derived, allowing one to estimate the noise bound and system decay rate. Then, these quantities and the data are employed to define the Feasible Parameter Set (FPS), which contains all possible models compatible with the available information. Here, the estimated decay rate is used to refine the standard FPS formulation, by adding constraints that enforce the desired converging behavior of the models' impulse response. Moreover, guaranteed simulation error bounds for an infinite future horizon are derived, improving over recent results pertaining to finite simulation horizon only. These bounds are the basis for a result and method to guarantee asymptotic stability of the identified model. Finally, the desired one-step-ahead model is identified by means of numerical optimization, and the related simulation error bounds are evaluated. Both input-output and state-space model structures are addressed. The approach is showcased on a numerical example and on real-world experimental data of the roll rate dynamics of an autonomous glider.
