Robust Nonlinear Regulation: Continuous-Time Internal Models and Hybrid Identifiers

Abstract

The paper deals with the problem of robust output regulation for minimum-phase nonlinear systems in a semiglobal setting. We present a different perspective to the problem of adaptive regulation in which prediction error identification methods, which are routinely used in other control contexts, can be adopted to design robust nonlinear regulators. The proposed control structure combines continuous-time dynamics and “hybrid identifiers”, the latter specifically designed to estimate the actual steady state control law. Besides presenting the main idea and a general framework, the paper addresses the specific case in which a linear regression law is used as model structure for the steady state control law and a least square optimization criterion is adopted as estimation method. The proposed framework encompasses existing frameworks proposed so far in the nonlinear continuous-time literature.