Online Identification of Complex Multi-Input-Multi-Output System Based on Generic Evolving Neuro-Fuzzy Inference System

Unmanned Aerial Vehicles (UAV) has been deployed for miscellaneous defence operations and commercial civilian applications. Nowadays, identification of the UAV dynamic elicits overwhelming interests within the community. This is mainly inflicted by the pivotal characteristic of the UAV suffering from the Multi Input Multi Output (MIMO), nonlinear, non-stationary and erratic natures which are attractive to be explored. In essence, the identification of the UAV is time-critical in which a computationally prohibitive algorithm is unappealing to be taken place. Unfortunately, the omnipresent approach in identifying the dynamic of the UAV still relies on offline or batched learning procedure necessitating a complete training set to be available a priori. This rationale unwraps a new unchartered territory of Evolving Neuro-Fuzzy System (ENFS) which is well-known efficient learning machine capable of coping with any variations of the system dynamic. Nonetheless, the ENFS is capable of commencing its learning process from an empty rule base or initial trained fuzzy model which is alike with the autonomous mental development in human’s brain. For brevity, the online identification strategy of the UAV based on a novel ENFS namely GENEFIS which is a short form of Generic Evolving Neuro-Fuzzy Inference System is addressed by this paper. In summary, our proposed algorithm is not only usable to online identification of the UAV but also can outperform the state of the art algorithms in terms of predictive quality and compactness of the rule base.