Authors:
Simon G. Fabri
and
Marvin K. Bugeja
Affiliation:
University of Malta, Malta
Keyword(s):
Dual Adaptive Control, Kalman Filter, Neural Networks, Intelligent Control.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
;
Nonlinear Signals and Systems
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
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.