A robust optimal design for strictly positive realness in recursive parameter adaptation

摘要

This paper provides an optimization-based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow-band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.