Aspects of the Theory and Application of Intelligent Modelling, Control and Estimation

Neurofuzzy algorithms have been extensively developed in recent years for the real time/online identification of nonlinear a priori unknown dynamical processes. As with all rule base paradigms they suffer from the curse of dimensionality, restricting their practical use to low dimensional control type problems. This paper shows how adaptive construction algorithms based on additive and extended additive decomposition techniques can overcome this problem, to produce parsimonious neurofuzzy models which retain their transparency or interpretability. Not only does this approach extend the applicability of neurofuzzy algorithms, it also enables low complexity controllers, or estimators to be derived. In this context neurofuzzy state estimators are derived which automatically parameterise a Kalman filter for a process state estimate reconstruction from any input/output data source. This approach avoids the usual pitfalls of the extended Kalman filter, and is optimal for local models. The local modelling approach is shown to be directly applicable to adaptive control of a priori unknown nonlinear systems