Transient performance, estimator resetting and filtering in nonlinear multiple model adaptive backstepping control

Multiple model adaptive control (MMAC) is a well-established approach for implementing adaptive systems with fast transient response. The authors consider a recently developed MMAC method based on adaptive nonlinear backstepping control where the parameter estimate may be instantaneously reset based on a criterion that requires a negative jump in the associated control Lyapunov function. Particular attention is paid to the transient effects of data filtering, which must be introduced in any practical implementation of the MMAC algorithm in order to reduce the sensitivity to noise, disturbances and model uncertainty. Insight into the robust behaviour of the adaptive system resulting from the filtering and an investigation into the trade-offs between high transient performance and robustness to uncertainty is the main aim of the authors who also suggest data filter tuning guidelines and illustrate the results using a simulation example.