Novel algorithms for the synchronization control of nonlinear systems

We show how the design of repetitive learning controls, which typically require perfect knowledge of the period T characterizing the periodic reference signals to be tracked, can be generalized, through the design of novel recursive add-on period identifiers, to face sufficiently small period uncertainties in synchronization problems. Exponential period identification is achieved so that the asymptotic convergence properties guaranteed by the classical learning controls are preserved. Simulation results concerning multi-link robot synchronization tasks are included. Copyright (c) 2015 John Wiley & Sons, Ltd.