Charles Q. Zhan

System Identification for Robust Control

This paper presents a robust-control-oriented system identification method aiming to minimize the normalized coprime factor uncertainty of the performance-weighted system. The nominal model and the normalized coprime factor uncertainty bound are estimated employing a filter bank approach, which approximately calculates the chordal distance between the identified model and the true system in the frequency range of interest. An iterative LMI optimization is formulated to identify the model coefficients. The optimal stability margin is also calculated and compared to the identified nominal uncertainty bound to decide if re-identification is necessary.

A new robust-control-oriented system identification method

Abstract This paper introduces a new robust-control-oriented system identification method, which consists of the following three steps: 1. High-order ARX model identification; 2. Loop shaping weighting functions design based on the high-order ARX model; 3. Control-oriented model reduction by minimizing the weighted L 2 -gap between the high-order ARX model and the low-order model. This method truly integrates the control objective into the identification step. A robust controller can be readily designed as a result of the identification. Simulation examples are given to show that smaller weighted ν -gap can be achieved by using the proposed method.

Non-invasive valve stiction detection using wavelet technology

In this paper, we present a novel approach of valve stiction detection using wavelet technology. A new non-invasive method is developed with the closed-loop normal operating data. The redundant dyadic discrete wavelet transform is used to decompose the data at different resolution scales. Based on the Lipschitz regularity theory, wavelet coefficients analysis across scales is performed to detect the jumps in the controlled variables. Adaptive wavelet denoising is then applied to the data. Features of the valve stiction patterns are extracted from the denoised data and the valve stiction probability is calculated.