摘要：AbstractThis work deals with the dual-control problem of simultaneous regulation and model parameter estimation in model predictive control. We propose an adaptive model predictive control which guarantees a persistently exciting closed loop sequence by only looking forward in time into the prediction horizon. Earlier works needed to look backwards and preserve prior regressor data. With the new approach, under the assumption of a known periodic persistently exciting reference trajectory around the equilibrium, we demonstrate exponential convergence of nonlinear systems under the influence of the adaptive model predictive control combined with a recursive least squares identifier with forgetting factor despite bounded noise. The results are, at this stage, local in state and parameter estimate space.
关键词：KeywordsAdaptive controlrecursive least squaresclosed-loop identificationmodel predictive controlpersistence of excitation
