Recursive lazy learning for modeling and control

The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory that can obtain better performance than neural networks in identification and control, even using smaller training data sets.

Abstract

This paper presents a local method for modeling and control of non-linear dynamical systems from input-output data. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory. The local modeling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems in configurations which are far from equilibrium. Simulations of the identification of a non-linear benchmark model and of the control of a complex non-linear system (the bioreactor) are presented. The experimental results show that the approach can obtain better performance than neural networks in identification and control, even using smaller training data sets.