Kalman Filter-based estimators for dual adaptive neural control : a comparative analysis of execution time and performance issues

Abstract

The real time implementation of neural network-based dual adaptive control for nonlinear systems can become significantly demanding because of the amount of network parameters requiring estimation. This paper explores the effect of three different estimation algorithms for dual adaptive control of a class of multiple-input, multiple-output nonlinear systems in terms of tracking performance and execution time. It is shown that the Unscented and Square-root Unscented Kalman filter estimators lead to a significant improvement in tracking performance when compared with the Extended Kalman filter, but with an appreciable increase in execution time. Such issues need to be given due consideration when implementing controllers for on-line operation.