Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterization

This research work demonstrates a method to design adaptive controller for a class of nonlinearly parameterized systems with unknown parameters. The main motivation behind this work is to deal with the widely known issues with adaptive control systems like oscillatory transient response, sluggish performance and poor parameter convergence. Multiple identification models with second level adaptation are used to describe the region of uncertainty for the given plant and to provide the best possible controller parameters. The two levels involved in the adaptive process improves the performance. A special attention is paid to the persistence of excitation condition and parameter convergence. Plants with nonlinear parameterizations are considered to ascertain the applicability of the proposed methodology. A Simulation work demonstrates the efficacy of the proposed methodology.