Resumo

DAVILA GOMEZ, Amalia; PENA PALACIO, Alejandro; ORTIZ VALENCIA, Paula Andrea  e  DELGADO TREJOS, Edilson. Stochastic or Complex Dynamics with Missing Data: A Review on Control. Iteckne [online]. 2013, vol.10, n.1, pp.113-127. ISSN 1692-1798.

Control theory for processes with stochastic or complex dynamics has a successful performance as long as a model can be adjusted to the system behavior, although for real applications in the industry, where data can be reduced or incomplete, this statement may not be true. Different control schemes have been proposed and the literature reports promising results with neural fuzzy models. These models combine the adaptability of neural networks with the robust inference of fuzzy logic, in order to model expert knowledge by learning rules, identify complex dynamics and enhance the control adaptability when stochastic disturbances are present, which sometimes cause incomplete system data. This paper presents a review on the difficulties and solutions related to the control of stochastic or complex systems with incomplete data. This study, initially, discusses the control structures when the system data present notable uncertainty levels. Next, different adaptive control schemes are presented, and finally, nonlinear and stochastic control approaches based on neural fuzzy systems are reviewed. Thereby, in a preliminary way, this review establishes that a system under the conditions mentioned above should be controlled by hybrid models supported on probabilistic routines and optimization procedures in order to appropriately consider the stochastic disturbances and the uncertainty levels without reducing the control performance and yielding consistent accuracy.

Palavras-chave : Adaptive control; stochastic systems; neural networks; fuzzy Logic; neuro-fuzzy models; incomplete system data.

        · resumo em Espanhol     · texto em Espanhol     · Espanhol ( pdf )