Intuitionistic Neo-Fuzzy predictive control

This paper presents an implicit predictive control strategy based on Intuitionistic Neo-Fuzzy predictor, as a first attempt to investigate the potentials of the intuitionistic fuzzy logic for the purpose of control applications. The proposed predictor represents a simple fuzzy-neural network as fusion from the concepts of the intuitionistic fuzzy logic, the neo-fuzzy neuron theory and the classical Takagi-Sugeno inference mechanism. The predictions are then coupled into generalized predictive control scheme where a standard quadratic control cost function is minimized over a set of predefined horizons. For simplicity, the considered process variables and the calculated output control sequence are iteratively bounded instead of explicitly constrained, in order to investigate the computational procedures related to implementation of an intuitionistic fuzzy logic. To investigate the potentials of the proposed predictive control approach, numerical experiments to control a Continuous Stirred Tank Reactor (CSTR) under uncertain conditions are studied.