Improved transient performance of nonlinear adaptive backstepping using estimator resetting based on multiple models

It is proved that the transient performance of nonlinear adaptive backstepping can be improved by resetting the parameter estimator, without loss of stability. The estimator resetting algorithm is based on multiple model adaptive control, where a number of models with xed parameter vectors are monitored online in order to detect parameter vectors that gives a negative jump in the control Lyapunov function when replacing the estimate provided by the standard adaptation law. Application to wheel slip control is studied.