Local Learning for Nonlinear Control

Abstract

This paper presents a local method for modeling and control of nonlinear dynamical systems, when only a limited amount of input-output data is available. 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. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control nonlinear systems in far from equilibrium configurations. Simulation results of the control of a complex nonlinear system (the bioreactor) are presented