A joint structured complex uncertainty identification and mu-synthesis algorithm

A joint uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations in the general linear fractional transformation (LFT) form and additive disturbances - customary representation in modern robust control and 2) to select from this set according to closed-loop control objectives. The motivation is to avoid conservatism of physics-based uncertainty modelling yet giving confidence in the model. The algorithm works on sampled, bounded-energy experimental data on the frequencydomain and integrates model invalidation/construction and control synthesis in order to achieve robust performance. Standard D-K iteration steps are combined with an optimization step on a group of selected data. The efficiency and applicability of the method is demonstrated on a vehicle control problem with real experimental data