Research Initiation Award: Robust Identification in H-infinity

h∞理论已被广泛用于鲁棒控制系统设计，以解决数学模型描述不准确的问题。然而，与当前鲁棒控制设计方法兼容的识别技术很少。基于实验信息（噪声数据），给出了h -∞范数下系统辨识问题，得到了h -∞范数下辨识误差有明确上界的系统辨识模型。本提案旨在开发线性定常系统在h -∞框架下的面向控制的辨识技术。拟进行的研究主要集中在以下几个方面：建立符合h -∞框架的实用噪声模型；2. 研究线性算法在系统辨识中的作用；3. 针对h -∞范数中识别误差的先验上界，提出了不同噪声模型的鲁棒收敛识别算法；4. 优化鲁棒收敛识别算法的性能；5. 研究基于实验噪声时间响应数据的h∞系统辨识。本建议的最终目标是更新识别技术，使其与现代鲁棒控制方法兼容。

H-infinity theory has been widely accepted for robust control system design to cope with the inaccurate description of the mathematical models. However, there exist very few identification techniques which are compatible with current robust control design methodologies. The problem of system identification in H-infinity is thus formulated to obtain an identified system model with explicit upper bound on identification error in H-infinity norm based on experimental information (noisy data). This proposal aims to develop control oriented identification techniques in H-infinity framework for linear time-invariant systems. The proposed research will focus on following issues: 1. Establish practical noise models which fit the H-infinity framework; 2. Investigate the role of linear algorithms in system identification; 3. Develop robustly convergent identification algorithms for different noise models with priori upper bounds for identification error in H-infinity norm; 4. Optimize the performance of robustly convergent identification algorithms; 5. Study system identification in H-infinity based on experimental noisy time response data. The ultimate goal of this proposal is to update identification techniques so that they are compatible with modern robust control methodologies.