Increased transient performance for the adaptive control of feedback linearizable systems using multiple models

We consider a class of minimum-phase non-linear systems with large parametric uncertainties. The non-linear dynamics is assumed to be linearly parameterized in terms of the unknown parameters. A novel scheme which utilizes multiple models in a model reference adaptive control (MRAC) framework is proposed to improve the transient performance of the adaptive scheme. The proposed approach makes use of fixed models from a compact and partitioned parameter space and resets the parameter update dynamics to the model which gives a negative jump to the control Lyapunov function. The overall stability of closed loop system under the switching is preserved based on the Lyapunov approach. A simulation study is given in order to demonstrate the efficient use of the algorithm.