Lazy learning for control design

This paper presents two local methods for the control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. The modeling procedure adopts lazy learning, a query-based approach for local modeling inspired to memory-based approximators. In the first method the lazy technique returns the forward and inverse models of the system which are used to compute the control action to take. The second is an indirect method inspired to adaptive control where the self-tuning identification module is replaced by a lazy approximator. Simulation examples of control of nonlinear systems starting from observed data are given.

• Type: Conference
• Year: 1998
• Where: ESANN
• Authors: Gianluca Bontempi, Mauro Birattari, Hugues Bersini
• Keywords: ESANN 1998, ESANN 2007, Lazy Learning, Modeling Procedure Adopts, Nonlinear Dynamical Systems