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Uncertainty in Control Problems: A Survey

Author:Herzallah Randa, Al-Balqa' Applied University, Jordan
Topic:3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.)
Session:Neural Control
Keywords: uncertainty, neural networks, Bayesian methods, mixture density network, conditional distributions, adaptive control, multiple model approaches

Abstract

Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterized by multi-valued function or even if it exhibits a number of modes of behavior during its operation. Recently, control engineers and theorist have developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. Basically, those techniques are based on incorporating models uncertainty which are proven to give more accurate control results under uncertain conditions. In this paper we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher level of complexity and uncertainty.