Abstract
The performance of model-based controller design is strongly influenced by the quality of the underlying model. However, the development of an accurate first-principle model is not a trivial task. In this research, a data-based LQI controller design method directly from plant data is developed. By incorporating the just-intime learning technique, LQI design can be carried out without the need of a first-principle model, which is normally assumed to be available in the traditional LQI design. Simulation results are presented to illustrate the proposed method and a comparison with its conventional counterparts is made.
