Nonlinear adaptive control using multiple identification models

A multiple model adaptive controller is proposed for nonlinear systems in parametric-strict-feedback form. By running in parallel multiple identification models and designing a suitable switching scheme, some models close to the real plant can be selected quickly, so that transient performance can be improved significantly. Global asymptotic stability of the closed-loop switching system is proved. A simulation example is given to demonstrate the effectiveness of the proposed multiple model adaptive controller.