OPTIMAL POPOV CONTROLLER ANALYSIS AND SYNTHESIS FOR SYSTEMS WITH REALPARAMETER UNCERTAINTIES

Robust performance analysis plays an important role in the design of c ontrollers for uncertain multivariable systems, Recent research has in vestigated the use of absolute stability criteria to develop less cons ervative analysis tests for systems with linear and nonlinear real par ameter uncertainties, This note extends previous work on optimal H-2 p erformance analysis using the Popov criterion by presenting an numeric al homotopy algorithm that can be used to analyze systems with less re strictive assumptions on the structure of the uncertainty block, The t echnique is used to compare relative robustness capabilities of the va rious control algorithms that have been designed for the Middeck activ e control experiment (MACE), The analysis is combined with the previou sly presented Popov controller synthesis to yield compensators that gu arantee robust performance for systems with real parameter uncertainty .