Closed-Loop Identification and Robust Control Design of Vehicle via Modeling

This paper presents a modeling approach to control a vehicle by visual feedback and H∞ control; this approach is based on two models designed using a closed-loop identification. The models are constructed from data for a curved path and straight path. Closed-loop identifications such as Closed-loop subspace model identification method (CL-MOESP) have been proposed recently and validated through simulations. However, consideration based experimental data have rarely been studied. We investigate the most effective identification approach of a vehicle model by experimental data. The most useful vehicle identification using visual feedback control is CL-MOESP. To evaluate its effectiveness, we use the fitting rate (FIT). Next, we design a robust controller based on the constructed models. Specifically, we formulate a model error for each models based on the curved and straight paths and use an H∞ control algorithm for the vehicle controller within a given error. A nominal plant is regarded as the model given by the curve data. The model error is estimated as the difference between the models given by the straight and curved paths.