Iterative Identification and Control Design: Methodology and Applications

Abstract

When using model-based controller design methodologies in order to control a plant whose model is not a priori known, there are several practical issues to be taken into account. Theoretically, the procedure is to identify an accurate enough model of the plant and, then, apply any model-based controller design technique. But the real world is much more complex and issues as the presence of nonlinearities in the plant or/and in the actuators, the noise that always corrupts the data, the existing actuator constraints or computational limitations, etc. arise, imposing natural limitations over the achievable performance of the plant to be controlled as well as in the possible model to be experimentally obtained. In this contribution, an iterative framework has been used to overcome some of these problems. The proposed algorithm starts by using a very rough plant model and low-demanding specifications. These and the plant model are progressively improved only as needed, i.e. until the final desired performance is achieved or no improvements are attained in consecutive iterations. This procedure somehow avoids useless effort: on the one hand, the identification stage is only a tool for controller design, not aiming at achieving a very accurate model at any working condition but just at those frequencies that are interesting for control purposes; on the other hand, the bandwidth is increased as long as the plant allows it, avoiding undesirable experimental results trying to achieve what is not possible at all.